Forem: Nikoloz Turazashvili (@axrisi)

BrewOps: I built a production-grade HTCPCP server because nobody else would

Nikoloz Turazashvili (@axrisi) — Wed, 01 Apr 2026 19:47:59 +0000

This is a submission for the DEV April Fools Challenge

What I built

We identified a critical gap in modern cloud infrastructure.

RFC 2324, the Hyper Text Coffee Pot Control Protocol, has been a ratified internet standard since April 1, 1998. Twenty-eight years ago. It defines BREW and WHEN as HTTP methods. It specifies 418 I'm a Teapot as a status code. It mandates the Accept-Additions header for cream and whisky. It requires servers to return Safe: if-user-awake on every response.

And in 28 years, nobody built a production-ready implementation with SLA monitoring. Nobody added an incident timeline. Nobody tracked Caffeine Dispensed as a metric. Nobody implemented the Security Considerations section, which literally warns about "denial of coffee service attacks."

Until now.

BrewOps is a fully RFC 2324-compliant HTCPCP/1.0 server. Written in Go. Zero external dependencies. It includes RFC 7168 TEA extension support, a live web dashboard, a CLI called brew-ctl (because kubectl was taken), and actual Denial of Coffee Service detection. The Dockerfile is called Brewfile. The Docker Compose service is named barista. The build stage works in /coffeeshop. I regret nothing.

Here's what happens when you try to brew coffee in a teapot:

$ curl -X BREW https://brewops.10mins.email/pot-2 -d 'start'

  418 I'm a Teapot
  ════════════════

        ╭───╮
        │   │
        │   │
    ╭───╯   ╰─╮
    │  TEAPOT  │
    ╰──────╯───╯

  "I'm short and stout.
   Here is my handle, here is my spout.
   Tip me over, pour me out."

  The requested pot (pot-2) is a teapot and CANNOT brew coffee.

  RFC 2324 §2.3.2:
    "Any attempt to brew coffee with a teapot should result in
     the error code '418 I'm a teapot'. The resulting entity
     body MAY be short and stout."

That's a real response from a real server. Right now. Running in production. With a 99.97% brew uptime SLA. Go ahead, try it. I'll wait. I have coffee.

Demo

Live server: https://brewops.10mins.email

Dashboard: https://brewops.10mins.email/dashboard -- open this first, then run curl commands. Watch your requests appear in the incident timeline alongside everyone else's. It's multiplayer. Yes, for a coffee pot.

Try these from your terminal

I know you want to. Nobody can resist poking the teapot.

# Brew coffee (auto-creates a new pot for you, no reservation needed)
curl -X BREW https://brewops.10mins.email/pot -d 'start'

# Poke the teapot (you will get 418'd and you will deserve it)
curl -X BREW https://brewops.10mins.email/pot-2 -d 'start'

# Brew with Whisky at 2pm on a Tuesday (the server will judge you)
curl -X BREW https://brewops.10mins.email/pot \
  -H 'Accept-Additions: Cream, Whisky' -d 'start'

# Brew tea, because the teapot has feelings and wants to be useful
curl -X BREW https://brewops.10mins.email/tea/earl-grey \
  -H 'Content-Type: message/teapot' -d 'start'

# Say "when" for milk (the RFC has a whole section on this, I'm not kidding)
curl -X WHEN https://brewops.10mins.email/pot-0

# Check how many pots you people have created
curl https://brewops.10mins.email/status

# Launch a Denial of Coffee Service attack against my server
# (the server will call you out but still serve your coffee)
for i in $(seq 1 15); do
  curl -s -X BREW https://brewops.10mins.email/pot -d 'start' > /dev/null
done

Every BREW /pot creates a new pot in the fleet. Every 5th pot is secretly a teapot. You won't know until you try to brew coffee in it and get 418'd. The teapot lottery has a 20% hit rate and a 100% disappointment rate.

What the dashboard tracks

The dashboard is styled like a 90s web forum that got promoted to an SRE tool. Teal background. Black borders. Orange uppercase headings. It tracks, in real time:

Total brews worldwide (this number will haunt me)
418s served (always embarrassingly high because everyone tries the teapot first)
Caffeine dispensed in milligrams (we meter it like cloud compute)
DoCS attacks detected (you WILL trigger this reading the article, I guarantee it)
Brew uptime: 99.97% (hardcoded, because we're that confident)
Spills this quarter: 3 (always 3, it's load-bearing)
Data retention policy: until next deploy

Code

turazashvili / brewops

The world's first production-grade HTCPCP/1.0 server. RFC 2324 compliant. 418 I'm a Teapot. DoCS attack detection. SLA: 99.97%. Zero dependencies. Zero real-world value.

BrewOps

Enterprise-Grade Beverage Infrastructure Observability

"There is coffee all over the world. Increasingly, in a world in which computing is ubiquitous, the computists want to make coffee." — RFC 2324, Section 1

BrewOps is the world's first production-grade implementation of the Hyper Text Coffee Pot Control Protocol (HTCPCP/1.0) with full RFC 7168 TEA extension support, real-time observability, and Denial of Coffee Service (DoCS) attack detection.

We identified a critical gap in modern cloud infrastructure: despite HTCPCP being standardized since 1998, there are zero production-ready implementations with SLA monitoring, incident timelines, and real-time telemetry. BrewOps closes this gap.

What Is This

A fully functional HTCPCP server that:

Responds to BREW, GET, WHEN, and PROPFIND HTTP methods per the RFC
Returns proper 418 I'm a Teapot when you try to brew coffee in a teapot
Auto-creates new pots on each request (every 5th pot is a surprise teapot)
Streams…

View on GitHub

16 files. 4,500 lines of Go. Zero external dependencies. The whole thing compiles into a single binary. The LICENSE includes a standard MIT clause plus: "COFFEE IS PROVIDED 'AS IS', WITHOUT WARRANTY OF TEMPERATURE, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTY THAT THE BEVERAGE WILL NOT BE A TEAPOT."

How I built it

Go, pure standard library. No frameworks, no routers, no npm install, no node_modules, no existential crisis. Go's net/http accepts any HTTP method, which is what makes BREW and WHEN work. Most languages would fight you on this. Go just shrugs and passes it through.

The server

One binary, brewopsd (the d is for daemon, like httpd, except this one makes coffee). It handles:

BREW and POST methods -- RFC 2324 says servers MUST accept both, but POST is deprecated. Using POST for coffee is like using var in 2026. Technically works. Spiritually wrong.
GET -- returns pot status. The response includes temperature labels that rotate between "Tepid (Why Bother)", "Scalding (Lawsuit Pending)", "Beyond Hot (Insurance Voided)", and "Thermonuclear (Handle At Own Risk)".
WHEN -- stops milk. "When coffee is poured, and milk is offered, it is necessary for the holder of the recipient of milk to say 'when'." I didn't write that. Larry Masinter did. In a real RFC. In 1998.
PROPFIND -- returns brew metadata. Lists Larry Masinter as the protocol author. Returns all 16 supported additions including Aquavit, because the RFC lists it and I don't argue with internet standards.

The pot state machine

Every pot goes through: idle -> grinding -> brewing -> pouring -> ready -> cooling -> idle. The whole lifecycle takes about 60 seconds. Dynamic pots get cleaned up by a janitor goroutine that runs every 15 seconds, because even coffee infrastructure needs someone mopping the floor.

The teapot lottery

BREW /pot dynamically creates a new pot. Every 5th one is a teapot. The teapots get names like "Surprise Teapot", "The 418 Generator", "Definitely Not A Coffee Pot", and "Agent Teapot". When you BREW coffee in one, you get 418'd, and the dashboard logs a CRITICAL incident. The incident messages rotate between 25 options including:

"Postmortem scheduled. Root cause: it's a teapot."
"Filing JIRA ticket TEA-47: 'Pot refuses to brew coffee.' Status: Won't Fix."
"The teapot doesn't dream of being a coffee pot. It's at peace."
"Stack trace: main() -> brew() -> validatePot() -> TEAPOT. That's it."
"Error budget: infinite. You can 418 this teapot all day. It doesn't care."

There are 100+ rotating messages across all response types. You get different snarky commentary every time.

DoCS detection

Section 7 of RFC 2324 says: "Unmoderated access to unprotected coffee pots from Internet users might lead to several kinds of denial of coffee service attacks."

So I implemented it. If you send more than 10 BREW requests in 30 seconds, the server classifies it as a DoCS attack. Your response gets a big warning banner. The dashboard CRITICAL counter goes up. The incident log says things like "Hostile brewing activity detected. The coffee must flow." or "DoCS incident #5. The On-Brew Engineer has left the building. And the country."

The brew still succeeds, though. This is a coffee pot, not a firewall. As the RFC itself notes: "Modern coffee pots do not use fire. Thus, no firewalls are necessary."

The dashboard

Vanilla HTML, CSS, and JavaScript. Three files. No React. No build step. The backend has a state machine, a janitor goroutine, SSE streaming, and rate-limited DoCS detection. The frontend is index.html, style.css, and dashboard.js. The engineering effort distribution between backend and frontend is roughly 95/5, which I think says something about my priorities.

Responses are dual-mode: curl gets pretty plain text with ASCII art. The dashboard JS sends Accept: application/json and gets structured data. So the same endpoint serves a nursery rhyme to your terminal and JSON to the browser. RFC-compliant content negotiation, technically.

Deployment

Docker container built from the Brewfile, behind nginx. Nginx passes any HTTP method through to the backend, so BREW and WHEN work. I originally deployed to a PaaS that uses Cloudflare, and Cloudflare silently drops non-standard HTTP methods. Which means Cloudflare is not HTCPCP-compliant. I have filed zero bug reports about this but considered filing several.

I actually read both RFCs cover to cover. I spent longer on protocol compliance than I'd like to admit for something that makes coffee. The Accept-Additions header parses quality parameters. The 300 Multiple Options response for tea includes a proper Alternates header. The Safe: if-user-awake header is on every single response. Someone will probably never check any of this. But it's there. Because if you're going to implement a joke protocol, you might as well do it right.

Prize category

Best Ode to Larry Masinter and Community Favorite.
I'm also competing for the overall prompt, in case that wasn't obvious from the 4,500 lines of Go I(A) wrote for a coffee pot.

Larry Masinter wrote RFC 2324 on April 1, 1998. It was supposed to be a joke. It defined a coffee pot protocol with custom HTTP methods, a nursery rhyme in the spec, and a security section that warns about "trojan grounds." Twenty-eight years later, I built a fully compliant server with SLA monitoring, DoCS attack detection, and a live dashboard that tracks caffeine dispensed in milligrams.

The server is live. The dashboard is live. Every curl command you run from reading this post will show up in the same incident timeline as everyone else's. The 418 counter is going to climb. The DoCS attacks are going to pile up. Someone is going to BREW 200 pots and the fleet is going to look ridiculous and the dashboard is going to struggle to render them all and that's fine. That's the point. That's the joke. We built enterprise infrastructure for a coffee pot and then let the internet loose on it.

Larry, if you're reading this: the protocol works. Sorry it took 28 years.

About me

I'm Niko. My title on this project is "Chief Brewing Officer & RFC Compliance Lead," which is the best title I've ever had and the least useful.

My day job involves building actual software that solves actual problems. This is not that. This is what happens when you read an RFC from 1998 and think "but what if someone actually built this" and then can't stop thinking about it until you do.

If you want to see what I do when I'm not implementing 28-year-old joke protocols, or if you want to file a JIRA ticket about the teapot (Status: Won't Fix):

GitHub: @turazashvili -- where the coffee is brewed
DEV: @axrisi -- where the coffee is documented

If you enjoyed this, leave a reaction. If you triggered a 418, leave a reaction. If you launched a DoCS attack against my server while reading this, you owe me at least two reactions and a mass brew has been logged and the On-Brew Engineer has been paged and they're not happy about it.

Coffee budget for this project generously provided by Vexrail. I did not approve this expense. I am also the one who spent it. The coffee was good though.

Drop Your Challenge Submission Here - v1

Nikoloz Turazashvili (@axrisi) — Wed, 01 Apr 2026 17:58:44 +0000

Built something for a DEV Challenge but feel like not enough people saw it?

This post is for exactly that.

Sometimes great submissions get buried in the challenge feed and never get the attention they deserve. So let’s fix that.

Use this thread to show what you built, meet other builders, and get real feedback on your project.

Share your submission in the comments

Include:

your project title
a short description of what it does
the link to your submission
what kind of feedback you want

Why this thread exists

Not every strong project gets noticed the first time.

Some are posted early. Some are posted late. Some just get lost in the volume.

This is a second chance to put your work in front of more people.

What you can get from posting here

more visibility
feedback from other developers
new ideas for improving your project
more discovery for underrated submissions

If you made something cool, drop it below.

You might find your next supporter, collaborator, or your first real traction in this thread.

👀👀👀

Nikoloz Turazashvili (@axrisi) — Thu, 26 Mar 2026 15:16:52 +0000

Stop Paying for Cloud: Deploy a Highly Available Cluster on Your Own Hardware in Minutes | MicroCloud

Nikoloz Turazashvili (@axrisi) — Thu, 26 Mar 2026 13:53:03 +0000

A 4-node highly available cluster with distributed storage, software-defined networking, and automatic failover. About 30 minutes to deploy. No Terraform. No Ansible playbooks. No 47-page architecture doc.

That's Canonical's MicroCloud: lightweight, repeatable cluster deployments built for edge computing and distributed workloads. Here's the full walkthrough.

TL;DR: Install 4 snaps, run microcloud init on one node, microcloud join on the others, answer the storage/networking wizard, and you have a production-ready HA cluster. Full walkthrough below.

1. What Hardware Do You Actually Need?

Before touching the command line, you need the right hardware for your target environment. MicroCloud is flexible enough to support both lightweight test setups and production clusters.

Test/Development Environments

MicroCloud is lightweight enough that a test setup can start with a single-member deployment. You need as little as 8 GB of RAM per machine, and local storage is often sufficient.

Highly Available Production Environments

For production, redundancy is key. A highly available setup requires at least four physical nodes. Each machine should ideally feature 32 GB of RAM and 10 GB network cards to handle throughput.

Storage Allocation

Within each production node, you should allocate three separate NVMe disks: one for the operating system, one for local storage, and one for distributed storage (via Ceph).

Why four nodes? Separating disk types and maintaining a four-node count provides critical redundancy. If one node goes offline, the extra member ensures the cluster maintains a quorum, preventing a "split-brain" scenario where remaining nodes lose synchronization. Your applications stay reachable and highly available.

2. Install Everything in One Command

The foundation of your MicroCloud cluster is a clean, standardized operating system. Every server you intend to use must be provisioned with an Ubuntu Long-Term Support (LTS) release.

Once your servers are online, install the core services across every node. Canonical packages these components as containerized "snaps":

sudo snap install microcloud lxd microceph microovn

Installing these services as snaps keeps your host operating system clean. All dependencies are bundled directly within the snap container, so you completely avoid package version conflicts.

3. The Cluster Handshake (Zero Manual SSL)

With the software installed, it's time to establish the cluster using an automated handshake process.

Step 1: Initialize the Primary Node

Choose one machine to act as your initiator. SSH into this node and run:

sudo microcloud init

When the wizard asks if you want to set up more than one cluster member, select "yes".

Step 2: Select Internal IPs

A table of available network interfaces will appear. Choose the specific IP address you want to use for MicroCloud's internal cluster communication.

Step 3: Generate the Passphrase

The initiator terminal will generate a secure passphrase and enter a waiting state. Leave this session running.

Step 4: Join the Secondary Nodes

Open new SSH sessions into your remaining nodes and run:

sudo microcloud join

Select the corresponding internal IP address on each node, enter the passphrase generated by the primary node, and verify that the unique fingerprint matches.

This automates trust distribution between your nodes. No manual SSL certificate management. No private key distribution. Return to your initiator terminal, select all the secondary systems that reached out, and accept them into the cluster.

4. Storage: Local Speed + Distributed Safety

MicroCloud lets you pair the high-speed performance of local hardware with the resilience of a distributed cluster. The setup wizard guides you through configuring both.

Local Storage

When prompted to set up local storage pools, select exactly one designated disk from each machine.

Warning: Any disk you select for storage pools must be completely unpartitioned and free of any existing file systems. If you select a pre-formatted disk, the initialization will fail, and you'll need to manually wipe the drives and restart the process.

Distributed Storage

Next, the wizard moves to distributed storage, handled by MicroCeph. You must select at least three unpartitioned disks across the cluster, as Ceph requires a minimum of three to properly replicate data for high availability. You will also be given options to wipe existing data, set up a CephFS distributed file system, or enable disk-level encryption.

This dual-storage strategy lets you run intensive edge workloads on fast local caching while ensuring your primary databases remain safely replicated across the distributed cluster.

5. Networking Without the Networking Pain

With compute and storage locked in, the wizard moves to network configuration, handled by MicroOVN. This software-defined network allows your virtual machines to communicate across the cluster.

External Uplink

The menu will display your physical network interfaces. Select one interface per machine to act as the external uplink. This uplink network must support both broadcast and multicast traffic for distributed networking to function correctly.

Gateways and IP Ranges

You will need to provide the IPv4 (and optionally IPv6) gateway for the uplink network using CIDR notation (e.g., 192.0.2.1/24). Then define a specific block of IPv4 addresses on your network reserved exclusively for your LXD instances.

Tip: MicroCloud uses mDNS (multicast DNS) to automatically discover the initiator node during setup. Some restrictive cloud environments block this traffic. If your secondary nodes can't find the initiator, manually input the initiator's static IP address to bypass the discovery phase and force the handshake.

6. Hit Enter, Get a Cloud

Once you answer the final prompts, MicroCloud takes over. It begins the automated bootstrap process, configuring and launching all requested services across every member of the cluster.

This turns what used to be a complex infrastructure deployment into a simple, repeatable process. Monitor the terminal output; when you see the final message stating "MicroCloud is ready," your multi-node cluster is live, synchronized, and operational.

You can manage everything via the command line, or enable the Canonical LXD graphical user interface for centralized visual control over your instances, networks, and storage pools.

Your cluster is live. From here you can launch containers, spin up VMs, or start deploying workloads to the edge. All on your own hardware, under your own control, at a fraction of what you'd pay a cloud provider.

Anyone running MicroCloud in production? What's your node count and what are you running on it?

You still have time! Submit your project!

Nikoloz Turazashvili (@axrisi) — Wed, 18 Mar 2026 21:14:44 +0000

Jess Lee for The DEV Team

Mar 18

Notion MCP Challenge: Badges Revealed + A New Prize! 🏆 😻

#devchallenge #notionchallenge

Comments 9

1 min read

I Stress-Tested Google's Colab MCP Server with a Real Quantum Workflow

Nikoloz Turazashvili (@axrisi) — Wed, 18 Mar 2026 20:16:13 +0000

> I used Google's new Colab MCP server to let an AI agent build, run, debug, and submit a quantum chemistry notebook to real IBM Quantum hardware.

Google just announced the Colab MCP server, an open-source bridge that lets any MCP-compatible AI agent control a live Google Colab notebook.

Jeffrey Mew for Google AI

Mar 17

Announcing the Colab MCP Server: Connect Any AI Agent to Google Colab

#cloud #agents #mcp #ai

Comments 18

3 min read

That sounds useful on paper. But I wanted to see whether it could handle something more demanding than a typical dataset demo.

So I gave an agent a real task: build a quantum chemistry notebook from scratch, install the right dependencies, debug compatibility issues, run a full parameter sweep, and submit a job to real IBM Quantum hardware.

It worked.

A blank Colab notebook became a fully working quantum chemistry workflow, complete with dependency fixes, a 15-point scan, and a real IBM Quantum job submission.

From a blank notebook to a real hardware result, the agent:

created and structured the notebook
installed the required packages
fixed multiple version and runtime errors
ran a 15-point hydrogen molecule scan
submitted a real job to IBM's 156-qubit quantum processor

That is what made this experiment interesting to me. Not just the quantum part, but the workflow itself.

Colab MCP turned Colab from a notebook I manually edit into a cloud lab an agent could actively operate.

See the notebook

Here is the actual Colab notebook the agent built and ran during this experiment:

Open the Colab notebook

Why this was a good test for Colab MCP

Most product demos stay in safe territory. Load a CSV. Draw a chart. Maybe install one package.

I wanted something messier.

Quantum chemistry is a good stress test because it combines several failure points at once:

dependency management
scientific Python tooling
cloud execution
notebook organization
runtime debugging
optional handoff to real hardware

If Colab MCP could survive that stack, it would tell me much more about its real-world usefulness than a toy notebook ever could.

The setup: Colab MCP

Google's new Colab MCP server gives an MCP-compatible agent programmatic control over a Google Colab notebook. Not read-only access. Real notebook control.

That means an agent can:

create and edit cells
write markdown and code
execute cells
inspect outputs and tracebacks
iterate until the notebook actually works

The setup is simple. Add the MCP server to your agent config:

"mcpServers": {
  "colab-proxy-mcp": {
    "command": "uvx",
    "args": ["git+https://github.com/googlecolab/colab-mcp"],
    "timeout": 30000
  }
}

Prerequisites are minimal:

Python
git
uv

Once the server is configured, it connects to the Colab notebook you already have open in your browser.

The task: compute the H2 potential energy surface

I chose the hydrogen molecule, H₂.

This is the smallest meaningful benchmark in quantum chemistry. It is simple enough to run quickly, but still rich enough to show whether the quantum workflow is doing something real.

The goal was to compute how the molecule's energy changes as the bond between the two hydrogen atoms is stretched. This is called the potential energy surface.

Why does that matter?

Because it lets you compare three things directly:

a standard classical approximation
a quantum algorithm
the exact reference solution

That makes H₂ the perfect test case for both the scientific result and the tooling around it.

What the agent actually did

I opened a fresh Colab notebook, connected the MCP agent, and gave it the task.

From there, the agent generated, executed, and debugged the notebook inside Colab, while I only provided the task and access setup.

Open Code Session for article: "I Stress-Tested Google's Colab MCP Server with a Real Quantum Workflow"

Agent

Let me start building the notebook in Colab via MCP. First, let me connect and clear the existing test cells.

Agent

▸ mcp__colab-proxy-mcp__colab-proxy-mcp_open_colab_browser_connection {}

Input

{
}

Output

true

Agent

▸ mcp__colab-proxy-mcp__colab-proxy-mcp_get_cells {"cellIndexStart":0,"includeOutputs":false}

Input

{
  "cellIndexStart": 0,
  "includeOutputs": false
}

Output

{
  "cells": [
    {
      "cell_type": "code",
      "id": "lIYdn1woOS1n",
      "source": [

      ]
    }
  ]
}

Agent

Good, fresh notebook with one empty cell. Let me start building. I'll delete the empty cell and create the notebook from scratch.

Agent

▸ mcp__colab-proxy-mcp__colab-proxy-mcp_delete_cell {"cellId":"lIYdn1woOS1n"}

Input

{
  "cellId": "lIYdn1woOS1n"
}

Output

{
}

Agent

Now let me build the notebook cell by cell. Starting with the title and the dependency install.

Agent

▸ mcp__colab-proxy-mcp__colab-proxy-mcp_add_text_cell {"cellIndex":0,"content":"# H2 Potential Energy Surface via VQE on Quantum Ha...

Input

{"cellIndex":0,"content":"# H2 Potential Energy Surface via VQE on Quantum Hardware\n\n**Goal**: Compute the ground-state energy of the hydrogen molecule (H₂) at multiple bond distances using the Vari...

Output

{
  "newCellId": "WY52xc2KioKm"
}

Agent

▸ mcp__colab-proxy-mcp__colab-proxy-mcp_add_code_cell {"cellIndex":1,"language":"python","code":"%%capture install_output\n# Instal...

Input

{"cellIndex":1,"language":"python","code":"%%capture install_output\n# Install quantum computing stack\n# Pin qiskit to 1.4.x because qiskit-nature 0.7.2 is incompatible with qiskit 2.x\n!pip install ...

Output

{
  "newCellId": "BM5vtucfipT_"
}

Agent

▸ mcp__colab-proxy-mcp__colab-proxy-mcp_run_code_cell {"cellId":"BM5vtucfipT_"}

Input

{
  "cellId": "BM5vtucfipT_"
}

Output

{
  "outputs": [

  ]
}

Agent

Install ran silently (captured output). Let me verify the imports work.

Agent

▸ mcp__colab-proxy-mcp__colab-proxy-mcp_add_code_cell {"cellIndex":2,"language":"python","code":"import qiskit\nimport qiskit_aer\n...

Input

{"cellIndex":2,"language":"python","code":"import qiskit\nimport qiskit_aer\nimport qiskit_nature\nimport qiskit_algorithms\nimport pyscf\n\nprint(\"Qiskit core:      \", qiskit.__version__)\nprint(\"...

Output

{
  "newCellId": "QYC4kY5Giyz3"
}

Agent

▸ mcp__colab-proxy-mcp__colab-proxy-mcp_run_code_cell {"cellId":"QYC4kY5Giyz3"}

Input

{
  "cellId": "QYC4kY5Giyz3"
}

Output

{
  "outputs": [
    {
      "output_type": "stream",
      "name": "stdout",
      "text": [
        "Qiskit core:       1.4.5\n",
        "Qiskit Aer:        0.17.2\n",
        "Qiskit Nature:     0.7.2\n",
        "Qiskit Algorithms: 0.4.0\n",
        "PySCF:             2.12.1\n",
        "\n",
        "All imports OK — ready to do quantum chemistry\n"
      ]
    }
  ]
}

Agent

All packages installed and working. Now let me build the H2 single-point demo.

Agent

▸ mcp__colab-proxy-mcp__colab-proxy-mcp_add_text_cell {"cellIndex":3,"content":"## Step 1: Build the H₂ Hamiltonian\n\nWe use PySCF...

Input

{"cellIndex":3,"content":"## Step 1: Build the H₂ Hamiltonian\n\nWe use PySCF (a classical quantum chemistry engine) to compute the electronic integrals for H₂, then map the fermionic Hamiltonian to q...

Output

{
  "newCellId": "FvfU0W7ai2J3"
}

Agent

▸ mcp__colab-proxy-mcp__colab-proxy-mcp_add_code_cell {"cellIndex":4,"language":"python","code":"import numpy as np\nfrom qiskit_na...

Input

{"cellIndex":4,"language":"python","code":"import numpy as np\nfrom qiskit_nature.second_q.drivers import PySCFDriver\nfrom qiskit_nature.units import DistanceUnit\nfrom qiskit_nature.second_q.mappers...

Output

{
  "newCellId": "802FQ4-Hi4iT"
}

Agent

▸ mcp__colab-proxy-mcp__colab-proxy-mcp_run_code_cell {"cellId":"802FQ4-Hi4iT"}

Input

{
  "cellId": "802FQ4-Hi4iT"
}

Output

{
  "outputs": [
    {
      "output_type": "stream",
      "name": "stdout",
      "text": [
        "Molecule: H2\n",
        "Bond distance: 0.735 Angstrom (equilibrium)\n",
        "Basis set: STO-3G\n",
        "Spatial orbitals: 2\n",
        "Particles (alpha, beta): (1, 1)\n",
        "Nuclear repulsion energy: 0.719969 Hartree\n",
        "\n",
        "Qubit Hamiltonian:\n",
        "  Qubits required: 4\n",
        "  Pauli terms: 15\n",
        "\n",
        "First 5 terms:\n",
        "  -0.810548 * IIII\n",
        "  +0.172184 * IIIZ\n",
        "  -0.225753 * IIZI\n",
        "  +0.120913 * IIZZ\n",
        "  +0.172184 * IZII\n"
      ]
    }
  ]
}

Agent

Working perfectly. H2 mapped to 4 qubits with 15 Pauli terms. Now let me add the VQE single-point calculation.

Agent

▸ mcp__colab-proxy-mcp__colab-proxy-mcp_add_text_cell {"cellIndex":5,"content":"## Step 2: Run VQE at Equilibrium (Single Point)\n\...

Input

{"cellIndex":5,"content":"## Step 2: Run VQE at Equilibrium (Single Point)\n\nWe compare three methods:\n1. **Hartree-Fock (HF)**: Classical mean-field approximation — fast but misses electron correla...

Output

{
  "newCellId": "YCEiEkBMi7ld"
}

Agent

▸ mcp__colab-proxy-mcp__colab-proxy-mcp_add_code_cell {"cellIndex":6,"language":"python","code":"from qiskit_nature.second_q.circui...

Input

{"cellIndex":6,"language":"python","code":"from qiskit_nature.second_q.circuit.library import UCCSD, HartreeFock\nfrom qiskit_nature.second_q.algorithms import GroundStateEigensolver\nfrom qiskit_algo...

Output

{
  "newCellId": "INJR_4Y-i-1Q"
}

Agent

▸ mcp__colab-proxy-mcp__colab-proxy-mcp_run_code_cell {"cellId":"INJR_4Y-i-1Q"}

Input

{
  "cellId": "INJR_4Y-i-1Q"
}

Output

{
  "outputs": [
    {
      "output_type": "stream",
      "name": "stdout",
      "text": [
        "=== EXACT (Classical Diagonalization) ===\n",
        "  Total energy: -1.13730604 Hartree\n",
        "\n",
        "=== HARTREE-FOCK ===\n",
        "  Total energy: -1.11699900 Hartree\n"
      ]
    },
    {
      "output_type": "stream",
      "name": "stderr",
      "text": [
        "/tmp/ipykernel_18018/2065787370.py:33: DeprecationWarning: The class ``qiskit.primitives.estimator.Estimator`` is deprecated as of qiskit 1.2. It will be removed no earlier than 3 months after the release date. All implementations of the `BaseEstimatorV1` interface have been deprecated in favor of their V2 counterparts. The V2 alternative for the `Estimator` class is `StatevectorEstimator`.\n",
        "  vqe = VQE(Estimator(), ansatz, SLSQP())\n"
      ]
    },
    {
      "output_type": "error",
      "ename": "AlgorithmError",
      "evalue": "'The primitive job to evaluate the energy failed!'",
      "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
        "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
        "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/qiskit_algorithms/minimum_eigensolvers/vqe.py\u001b[0m in \u001b[0;36mevaluate_energy\u001b[0;34m(parameters)\u001b[0m\n\u001b[1;32m    324\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 325\u001b[0;31m                 \u001b[0mjob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mansatz\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moperator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    326\u001b[0m                 \u001b[0mestimator_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mTypeError\u001b[0m: BaseEstimatorV1.run() missing 1 required positional argument: 'observables'",
        "\nThe above exception was the direct cause of the following exception:\n",
        "\u001b[0;31mAlgorithmError\u001b[0m                            Traceback (most recent call last)",
        "\u001b[0;32m/tmp/ipykernel_18018/2065787370.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     34\u001b[0m \u001b[0mvqe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minitial_point\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mansatz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_parameters\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m \u001b[0mvqe_solver\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGroundStateEigensolver\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmapper\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvqe\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 36\u001b[0;31m \u001b[0mvqe_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvqe_solver\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msolve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mproblem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     37\u001b[0m \u001b[0mvqe_energy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvqe_result\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtotal_energies\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreal\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/qiskit_nature/second_q/algorithms/ground_state_solvers/ground_state_eigensolver.py\u001b[0m in \u001b[0;36msolve\u001b[0;34m(self, problem, aux_operators)\u001b[0m\n\u001b[1;32m     71\u001b[0m         \"\"\"\n\u001b[1;32m     72\u001b[0m         \u001b[0mmain_operator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maux_ops\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_qubit_operators\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mproblem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maux_operators\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 73\u001b[0;31m         \u001b[0mraw_mes_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msolver\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute_minimum_eigenvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmain_operator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maux_ops\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     74\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     75\u001b[0m         \u001b[0meigenstate_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mEigenstateResult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mraw_mes_result\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/qiskit_algorithms/minimum_eigensolvers/vqe.py\u001b[0m in \u001b[0;36mcompute_minimum_eigenvalue\u001b[0;34m(self, operator, aux_operators)\u001b[0m\n\u001b[1;32m    230\u001b[0m             \u001b[0mwas_updated\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_set_default_batchsize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimizer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    231\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 232\u001b[0;31m             optimizer_result = self.optimizer.minimize(\n\u001b[0m\u001b[1;32m    233\u001b[0m                 \u001b[0mfun\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mevaluate_energy\u001b[0m\u001b[0;34m,\u001b[0m  \u001b[0;31m# type: ignore[arg-type]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    234\u001b[0m                 \u001b[0mx0\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitial_point\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/qiskit_algorithms/optimizers/scipy_optimizer.py\u001b[0m in \u001b[0;36mminimize\u001b[0;34m(self, fun, x0, jac, bounds)\u001b[0m\n\u001b[1;32m    160\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_options\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"maxfun\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_options\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"maxiter\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    161\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 162\u001b[0;31m         raw_result = minimize(\n\u001b[0m\u001b[1;32m    163\u001b[0m             \u001b[0mfun\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfun\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    164\u001b[0m             \u001b[0mx0\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mx0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/scipy/optimize/_minimize.py\u001b[0m in \u001b[0;36mminimize\u001b[0;34m(fun, x0, args, method, jac, hess, hessp, bounds, constraints, tol, callback, options)\u001b[0m\n\u001b[1;32m    794\u001b[0m                                **options)\n\u001b[1;32m    795\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mmeth\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'slsqp'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 796\u001b[0;31m         res = _minimize_slsqp(fun, x0, args, jac, bounds,\n\u001b[0m\u001b[1;32m    797\u001b[0m                               constraints, callback=callback, **options)\n\u001b[1;32m    798\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mmeth\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'trust-constr'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/scipy/optimize/_slsqp_py.py\u001b[0m in \u001b[0;36m_minimize_slsqp\u001b[0;34m(func, x0, args, jac, bounds, constraints, maxiter, ftol, iprint, disp, eps, callback, finite_diff_rel_step, workers, **unknown_options)\u001b[0m\n\u001b[1;32m    417\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    418\u001b[0m     \u001b[0;31m# ScalarFunction provides function and gradient evaluation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 419\u001b[0;31m     sf = _prepare_scalar_function(func, x, jac=jac, args=args, epsilon=eps,\n\u001b[0m\u001b[1;32m    420\u001b[0m                                   \u001b[0mfinite_diff_rel_step\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfinite_diff_rel_step\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    421\u001b[0m                                   bounds=new_bounds, workers=workers)\n",
        "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/scipy/optimize/_optimize.py\u001b[0m in \u001b[0;36m_prepare_scalar_function\u001b[0;34m(fun, x0, jac, args, bounds, epsilon, finite_diff_rel_step, hess, workers)\u001b[0m\n\u001b[1;32m    308\u001b[0m     \u001b[0;31m# ScalarFunction caches. Reuse of fun(x) during grad\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    309\u001b[0m     \u001b[0;31m# calculation reduces overall function evaluations.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 310\u001b[0;31m     sf = ScalarFunction(fun, x0, args, grad, hess,\n\u001b[0m\u001b[1;32m    311\u001b[0m                         \u001b[0mfinite_diff_rel_step\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbounds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepsilon\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mepsilon\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    312\u001b[0m                         workers=workers)\n",
        "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/scipy/optimize/_differentiable_functions.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, fun, x0, args, grad, hess, finite_diff_rel_step, finite_diff_bounds, epsilon, workers)\u001b[0m\n\u001b[1;32m    272\u001b[0m         \u001b[0;31m# Initial function evaluation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    273\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_nfev\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 274\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_fun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    275\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    276\u001b[0m         \u001b[0;31m# Initial gradient evaluation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/scipy/optimize/_differentiable_functions.py\u001b[0m in \u001b[0;36m_update_fun\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    351\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_update_fun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    352\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf_updated\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 353\u001b[0;31m             \u001b[0mfx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_wrapped_fun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    354\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_nfev\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    355\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mfx\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lowest_f\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/scipy/_lib/_util.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m    588\u001b[0m         \u001b[0;31m# Send a copy because the user may overwrite it.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    589\u001b[0m         \u001b[0;31m# The user of this class might want `x` to remain unchanged.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 590\u001b[0;31m         \u001b[0mfx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    591\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnfev\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    592\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/qiskit_algorithms/minimum_eigensolvers/vqe.py\u001b[0m in \u001b[0;36mevaluate_energy\u001b[0;34m(parameters)\u001b[0m\n\u001b[1;32m    326\u001b[0m                 \u001b[0mestimator_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    327\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 328\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mAlgorithmError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"The primitive job to evaluate the energy failed!\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    329\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    330\u001b[0m             \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mestimator_result\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mAlgorithmError\u001b[0m: 'The primitive job to evaluate the energy failed!'"
      ]
    }
  ]
}

Agent

The issue is a V1 vs V2 primitives mismatch. qiskit-algorithms 0.4.0 tries to call the Estimator with V2 tuple syntax but qiskit.primitives.Estimator is V1. I need to use qiskit_aer.primitives.Estimator or use StatevectorEstimator. Let me fix this.

Agent

▸ mcp__colab-proxy-mcp__colab-proxy-mcp_update_cell {"cellId":"INJR_4Y-i-1Q","content":"from qiskit_nature.second_q.circuit.libra...

Input

{"cellId":"INJR_4Y-i-1Q","content":"from qiskit_nature.second_q.circuit.library import UCCSD, HartreeFock\nfrom qiskit_nature.second_q.algorithms import GroundStateEigensolver\nfrom qiskit_algorithms ...

Output

{
}

Agent

▸ mcp__colab-proxy-mcp__colab-proxy-mcp_run_code_cell {"cellId":"INJR_4Y-i-1Q"}

Input

{
  "cellId": "INJR_4Y-i-1Q"
}

Output

{
  "outputs": [
    {
      "output_type": "stream",
      "name": "stdout",
      "text": [
        "=== EXACT (Classical Diagonalization) ===\n",
        "  Total energy: -1.13730604 Hartree\n",
        "\n",
        "=== HARTREE-FOCK ===\n",
        "  Total energy: -1.11699900 Hartree\n",
        "\n",
        "=== VQE (UCCSD Ansatz, 3 parameters) ===\n",
        "  Total energy: -1.13730604 Hartree\n",
        "  Circuit depth: 4\n",
        "\n",
        "=== COMPARISON ===\n",
        "  HF error vs exact:  20.3070 mHartree\n",
        "  VQE error vs exact: 0.0000 mHartree\n",
        "  VQE captures 100.00% of correlation energy\n"
      ]
    }
  ]
}

Agent

VQE nails it -- captures 100% of the correlation energy, matching the exact solution to machine precision. Now let me build the PES scan.

Agent

▸ mcp__colab-proxy-mcp__colab-proxy-mcp_add_text_cell {"cellIndex":7,"content":"## Step 3: Potential Energy Surface Scan\n\nNow we ...

Input

{"cellIndex":7,"content":"## Step 3: Potential Energy Surface Scan\n\nNow we sweep the H-H bond distance from 0.3 Å (compressed) to 3.0 Å (stretched/dissociated) and compute the energy at each point u...

Output

{
  "newCellId": "GiYX4KWwjJ6e"
}

Agent

▸ mcp__colab-proxy-mcp__colab-proxy-mcp_add_code_cell {"cellIndex":8,"language":"python","code":"import time\n\n# Bond distances to...

Input

{"cellIndex":8,"language":"python","code":"import time\n\n# Bond distances to scan (Angstroms)\ndistances = np.linspace(0.3, 3.0, 15)\n\n# Storage\nhf_energies = []\nexact_energies = []\nvqe_energies ...

Output

{
  "newCellId": "ecleQCY9jM7f"
}

Agent

▸ mcp__colab-proxy-mcp__colab-proxy-mcp_run_code_cell {"cellId":"ecleQCY9jM7f"}

Input

{
  "cellId": "ecleQCY9jM7f"
}

Output

{
  "outputs": [
    {
      "output_type": "stream",
      "name": "stdout",
      "text": [
        "Scanning 15 bond distances from 0.30 to 3.00 Angstrom\n",
        "======================================================================\n",
        "\n",
        "[1/15] d = 0.3000 A ... HF=-0.593828  Exact=-0.601804  VQE=-0.601804\n",
        "\n",
        "[2/15] d = 0.4929 A ... HF=-1.036415  Exact=-1.048389  VQE=-1.048389\n",
        "\n",
        "[3/15] d = 0.6857 A ... HF=-1.116747  Exact=-1.135016  VQE=-1.135016\n",
        "\n",
        "[4/15] d = 0.8786 A ... HF=-1.096691  Exact=-1.124108  VQE=-1.124108\n",
        "\n",
        "[5/15] d = 1.0714 A ... HF=-1.045246  Exact=-1.085591  VQE=-1.085591\n",
        "\n",
        "[6/15] d = 1.2643 A ... HF=-0.984538  Exact=-1.042714  VQE=-1.042714\n",
        "\n",
        "[7/15] d = 1.4571 A ... HF=-0.923833  Exact=-1.005256  VQE=-1.005256\n",
        "\n",
        "[8/15] d = 1.6500 A ... HF=-0.867804  Exact=-0.977130  VQE=-0.977130\n",
        "\n",
        "[9/15] d = 1.8429 A ... HF=-0.818527  Exact=-0.958371  VQE=-0.958371\n",
        "\n",
        "[10/15] d = 2.0357 A ... HF=-0.776570  Exact=-0.946970  VQE=-0.946970\n",
        "\n",
        "[11/15] d = 2.2286 A ... HF=-0.741645  Exact=-0.940490  VQE=-0.940490\n",
        "\n",
        "[12/15] d = 2.4214 A ... HF=-0.713012  Exact=-0.936963  VQE=-0.936963\n",
        "\n",
        "[13/15] d = 2.6143 A ... HF=-0.689771  Exact=-0.935095  VQE=-0.935095\n",
        "\n",
        "[14/15] d = 2.8071 A ... HF=-0.671046  Exact=-0.934126  VQE=-0.934125\n",
        "\n",
        "[15/15] d = 3.0000 A ... HF=-0.656048  Exact=-0.933632  VQE=-0.933632\n",
        "\n",
        "======================================================================\n",
        "PES scan complete in 8.2 seconds\n"
      ]
    }
  ]
}

Agent

All 15 points computed in 8.2 seconds. VQE matches exact to 6+ decimal places at every point. You can clearly see HF diverging from the exact solution at stretched distances. Now the visualization.

Agent

▸ mcp__colab-proxy-mcp__colab-proxy-mcp_add_text_cell {"cellIndex":9,"content":"## Step 4: Visualize the Potential Energy Surface\n...

Input

{"cellIndex":9,"content":"## Step 4: Visualize the Potential Energy Surface\n\nThe key plot: energy vs bond distance for all three methods. Watch how Hartree-Fock fails at large distances (the \"stati...

Output

{
  "newCellId": "mlNuFW9DjRdX"
}

Agent

▸ mcp__colab-proxy-mcp__colab-proxy-mcp_add_code_cell {"cellIndex":10,"language":"python","code":"import matplotlib.pyplot as plt\n...

Input

{"cellIndex":10,"language":"python","code":"import matplotlib.pyplot as plt\n\nfig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5))\n\n# --- Left: Full PES ---\nax1.plot(distances, hf_energies, 'b--o...

Output

{
  "newCellId": "w4fDxBnbjUom"
}

Agent

▸ mcp__colab-proxy-mcp__colab-proxy-mcp_run_code_cell {"cellId":"w4fDxBnbjUom"}

Input

{
  "cellId": "w4fDxBnbjUom"
}

Output



...109080 bytes truncated...

The tool call succeeded but the output was truncated. Full output saved to: [local tool-output path omitted]tool_d018d6d27001jSNy4APPBrsC5F
Use the Task tool to have explore agent process this file with Grep and Read (with offset/limit). Do NOT read the full file yourself - delegate to save context.

Agent

Plot generated. Now let me move on to the IBM Quantum hardware submission. I need you to provide your IBM key -- let me add the cell that will ask for it.

Agent

▸ mcp__colab-proxy-mcp__colab-proxy-mcp_add_text_cell {"cellIndex":11,"content":"## Step 5: Submit to Real IBM Quantum Hardware\n\n...

Input

{"cellIndex":11,"content":"## Step 5: Submit to Real IBM Quantum Hardware\n\nNow the real test. We take the H₂ Hamiltonian at equilibrium and submit it to an actual quantum processor. This means:\n\n-...

Output

{
  "newCellId": "nK_wYFZ4jaGY"
}

Agent

▸ mcp__colab-proxy-mcp__colab-proxy-mcp_add_code_cell {"cellIndex":12,"language":"python","code":"from qiskit_ibm_runtime import Qi...

Input

{"cellIndex":12,"language":"python","code":"from qiskit_ibm_runtime import QiskitRuntimeService\n\n# Connect to IBM Quantum\n# Replace with your IBM Quantum Platform token\nIBM_TOKEN = \"YOUR_IBM_TOKE...

Output

{
  "newCellId": "6Wn_nptWjbtt"
}

Agent

▸ question {"questions":[{"question":"I need your IBM Quantum token to run the hardware ...

Input

{"questions":[{"question":"I need your IBM Quantum token to run the hardware cell. Please paste it here (I'll put it directly in the notebook cell). Which channel should I use?","header":"IBM Quantum ...

Output

User has answered your questions: "I need your IBM Quantum token to run the hardware cell. Please paste it here (I'll put it directly in the notebook cell). Which channel should I use?"="ibm_quantum_platform". You can now continue with the user's answers in mind.

Agent

▸ question {"questions":[{"question":"Please paste your IBM Quantum Platform token so I ...

Input

{
  "questions": [
    {
      "question": "Please paste your IBM Quantum Platform token so I can put it in the notebook cell and connect to hardware.",
      "header": "IBM Token",
      "options": [

      ]
    }
  ]
}

Output

User has answered your questions: "Please paste your IBM Quantum Platform token so I can put it in the notebook cell and connect to hardware."="can we somehow use envs here? i created token as IBM_TOKEN is says we can access via from google.colab import userdata
userdata.get('secretName')". You can now continue with the user's answers in mind.

Agent

Smart approach using Colab secrets. Let me update the cell to use that.

Agent

▸ mcp__colab-proxy-mcp__colab-proxy-mcp_update_cell {"cellId":"6Wn_nptWjbtt","content":"from qiskit_ibm_runtime import QiskitRunt...

Input

{"cellId":"6Wn_nptWjbtt","content":"from qiskit_ibm_runtime import QiskitRuntimeService\nfrom google.colab import userdata\n\n# Load token from Colab Secrets (set IBM_TOKEN in your Colab secrets panel...

Output

{
}

Agent

▸ mcp__colab-proxy-mcp__colab-proxy-mcp_run_code_cell {"cellId":"6Wn_nptWjbtt"}

Input

{
  "cellId": "6Wn_nptWjbtt"
}

Output

{
  "outputs": [
    {
      "output_type": "stream",
      "name": "stderr",
      "text": [
        "qiskit_runtime_service._discover_account:WARNING:2026-03-18 15:28:29,402: Loading account with the given token. A saved account will not be used.\n",
        "qiskit_runtime_service.__init__:WARNING:2026-03-18 15:28:31,846: Instance was not set at service instantiation. Free and trial plan instances will be prioritized. Based on the following filters: (tags: None, region: us-east, eu-de), and available plans: (open), the available account instances are: open-instance. If you need a specific instance set it explicitly either by using a saved account with a saved default instance or passing it in directly to QiskitRuntimeService().\n",
        "qiskit_runtime_service.backends:WARNING:2026-03-18 15:28:31,848: Loading instance: open-instance, plan: open\n"
      ]
    },
    {
      "output_type": "stream",
      "name": "stdout",
      "text": [
        "Available backends:\n",
        "  ibm_fez: 156 qubits, pending jobs: 0\n",
        "  ibm_torino: 133 qubits, pending jobs: 0\n",
        "  ibm_kingston: 156 qubits, pending jobs: 119\n",
        "  ibm_marrakesh: 156 qubits, pending jobs: 0\n"
      ]
    },
    {
      "output_type": "stream",
      "name": "stderr",
      "text": [
        "qiskit_runtime_service.backends:WARNING:2026-03-18 15:28:36,687: Loading instance: open-instance, plan: open\n",
        "qiskit_runtime_service.backends:WARNING:2026-03-18 15:28:37,636: Using instance: open-instance, plan: open\n"
      ]
    },
    {
      "output_type": "stream",
      "name": "stdout",
      "text": [
        "\n",
        "Selected backend: ibm_fez (156 qubits)\n"
      ]
    }
  ]
}

Agent

Connected to IBM Quantum. We have 4 backends available and selected ibm_fez (156 qubits, 0 pending jobs). Now let me build the hardware submission cell.

Agent

▸ mcp__colab-proxy-mcp__colab-proxy-mcp_add_code_cell {"cellIndex":13,"language":"python","code":"from qiskit.transpiler.preset_pas...

Input

{"cellIndex":13,"language":"python","code":"from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\nfrom qiskit_ibm_runtime import EstimatorV2, Session\nfrom qiskit.quantum_inf...

Output

{
  "newCellId": "iYRcYErzkGSB"
}

Agent

▸ mcp__colab-proxy-mcp__colab-proxy-mcp_run_code_cell {"cellId":"iYRcYErzkGSB"}

Input

{
  "cellId": "iYRcYErzkGSB"
}

Output

{
  "outputs": [
    {
      "output_type": "stream",
      "name": "stderr",
      "text": [
        "ERROR:stevedore.extension:Could not load 'ibm_backend': cannot import name 'calc_final_ops' from 'qiskit.transpiler.passes.utils.remove_final_measurements' (/usr/local/lib/python3.12/dist-packages/qiskit/transpiler/passes/utils/remove_final_measurements.py)\n",
        "ERROR:stevedore.extension:Could not load 'ibm_dynamic_and_fractional': cannot import name 'calc_final_ops' from 'qiskit.transpiler.passes.utils.remove_final_measurements' (/usr/local/lib/python3.12/dist-packages/qiskit/transpiler/passes/utils/remove_final_measurements.py)\n",
        "ERROR:stevedore.extension:Could not load 'ibm_dynamic_circuits': cannot import name 'calc_final_ops' from 'qiskit.transpiler.passes.utils.remove_final_measurements' (/usr/local/lib/python3.12/dist-packages/qiskit/transpiler/passes/utils/remove_final_measurements.py)\n",
        "ERROR:stevedore.extension:Could not load 'ibm_fractional': cannot import name 'calc_final_ops' from 'qiskit.transpiler.passes.utils.remove_final_measurements' (/usr/local/lib/python3.12/dist-packages/qiskit/transpiler/passes/utils/remove_final_measurements.py)\n"
      ]
    },
    {
      "output_type": "stream",
      "name": "stdout",
      "text": [
        "Hamiltonian: 4 qubits, 15 Pauli terms\n",
        "Circuit: 4 qubits, depth 4\n"
      ]
    },
    {
      "output_type": "error",
      "ename": "TranspilerError",
      "evalue": "'Invalid plugin name ibm_dynamic_circuits for stage translation'",
      "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
        "\u001b[0;31mTranspilerError\u001b[0m                           Traceback (most recent call last)",
        "\u001b[0;32m/tmp/ipykernel_18018/3926184593.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m \u001b[0;31m# Transpile for the real backend\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 39\u001b[0;31m \u001b[0mpm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgenerate_preset_pass_manager\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbackend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbackend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimization_level\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     40\u001b[0m \u001b[0misa_circuit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbound_circuit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m \u001b[0misa_hamiltonian\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhamiltonian_spo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_layout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0misa_circuit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/qiskit/utils/deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    182\u001b[0m                 \u001b[0mpredicate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpredicate\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    183\u001b[0m             )\n\u001b[0;32m--> 184\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    185\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    186\u001b[0m         \u001b[0;31m# When decorators get called repeatedly, `func` refers to the result of the prior\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/qiskit/utils/deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    182\u001b[0m                 \u001b[0mpredicate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpredicate\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    183\u001b[0m             )\n\u001b[0;32m--> 184\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    185\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    186\u001b[0m         \u001b[0;31m# When decorators get called repeatedly, `func` refers to the result of the prior\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/qiskit/utils/deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    182\u001b[0m                 \u001b[0mpredicate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpredicate\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    183\u001b[0m             )\n\u001b[0;32m--> 184\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    185\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    186\u001b[0m         \u001b[0;31m# When decorators get called repeatedly, `func` refers to the result of the prior\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/qiskit/utils/deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    182\u001b[0m                 \u001b[0mpredicate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpredicate\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    183\u001b[0m             )\n\u001b[0;32m--> 184\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    185\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    186\u001b[0m         \u001b[0;31m# When decorators get called repeatedly, `func` refers to the result of the prior\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/qiskit/transpiler/preset_passmanagers/generate_preset_pass_manager.py\u001b[0m in \u001b[0;36mgenerate_preset_pass_manager\u001b[0;34m(optimization_level, backend, target, basis_gates, inst_map, coupling_map, instruction_durations, backend_properties, timing_constraints, initial_layout, layout_method, routing_method, translation_method, scheduling_method, approximation_degree, seed_transpiler, unitary_synthesis_method, unitary_synthesis_plugin_config, hls_config, init_method, optimization_method, dt, qubits_initially_zero, _skip_target)\u001b[0m\n\u001b[1;32m    469\u001b[0m             \u001b[0mpm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlevel_0_pass_manager\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpm_config\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    470\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0moptimization_level\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 471\u001b[0;31m             \u001b[0mpm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlevel_1_pass_manager\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpm_config\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    472\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0moptimization_level\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    473\u001b[0m             \u001b[0mpm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlevel_2_pass_manager\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpm_config\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/qiskit/transpiler/preset_passmanagers/level1.py\u001b[0m in \u001b[0;36mlevel_1_pass_manager\u001b[0;34m(pass_manager_config)\u001b[0m\n\u001b[1;32m     75\u001b[0m         \u001b[0mrouting\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     76\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 77\u001b[0;31m     translation = plugin_manager.get_passmanager_stage(\n\u001b[0m\u001b[1;32m     78\u001b[0m         \u001b[0;34m\"translation\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtranslation_method\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpass_manager_config\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimization_level\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     79\u001b[0m     )\n",
        "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/qiskit/transpiler/preset_passmanagers/plugin.py\u001b[0m in \u001b[0;36mget_passmanager_stage\u001b[0;34m(self, stage_name, plugin_name, pm_config, optimization_level)\u001b[0m\n\u001b[1;32m    244\u001b[0m             )\n\u001b[1;32m    245\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mstage_name\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"translation\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 246\u001b[0;31m             return self._build_pm(\n\u001b[0m\u001b[1;32m    247\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtranslation_plugins\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstage_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mplugin_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpm_config\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimization_level\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    248\u001b[0m             )\n",
        "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/qiskit/transpiler/preset_passmanagers/plugin.py\u001b[0m in \u001b[0;36m_build_pm\u001b[0;34m(self, stage_obj, stage_name, plugin_name, pm_config, optimization_level)\u001b[0m\n\u001b[1;32m    267\u001b[0m     ):\n\u001b[1;32m    268\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mplugin_name\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mstage_obj\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 269\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mTranspilerError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Invalid plugin name {plugin_name} for stage {stage_name}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    270\u001b[0m         \u001b[0mplugin_obj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstage_obj\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mplugin_name\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    271\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mplugin_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpass_manager\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpm_config\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimization_level\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mTranspilerError\u001b[0m: 'Invalid plugin name ibm_dynamic_circuits for stage translation'"
      ]
    }
  ]
}

Agent

The transpiler plugin from qiskit-ibm-runtime is incompatible with qiskit 1.4.x. I need to use qiskit.transpile() directly instead of the preset pass manager with the IBM backend. Let me fix this by transpiling manually.

Agent

▸ mcp__colab-proxy-mcp__colab-proxy-mcp_update_cell {"cellId":"iYRcYErzkGSB","content":"from qiskit import transpile\nfrom qiskit...

Input

{"cellId":"iYRcYErzkGSB","content":"from qiskit import transpile\nfrom qiskit_ibm_runtime import EstimatorV2, Session\nfrom qiskit.quantum_info import SparsePauliOp\n\n# Rebuild H2 at equilibrium for ...

Output

{
}

Agent

▸ mcp__colab-proxy-mcp__colab-proxy-mcp_run_code_cell {"cellId":"iYRcYErzkGSB"}

Input

{
  "cellId": "iYRcYErzkGSB"
}

Output

{
  "outputs": [
    {
      "output_type": "stream",
      "name": "stderr",
      "text": [
        "ERROR:stevedore.extension:Could not load 'ibm_backend': cannot import name 'calc_final_ops' from 'qiskit.transpiler.passes.utils.remove_final_measurements' (/usr/local/lib/python3.12/dist-packages/qiskit/transpiler/passes/utils/remove_final_measurements.py)\n",
        "ERROR:stevedore.extension:Could not load 'ibm_dynamic_and_fractional': cannot import name 'calc_final_ops' from 'qiskit.transpiler.passes.utils.remove_final_measurements' (/usr/local/lib/python3.12/dist-packages/qiskit/transpiler/passes/utils/remove_final_measurements.py)\n",
        "ERROR:stevedore.extension:Could not load 'ibm_dynamic_circuits': cannot import name 'calc_final_ops' from 'qiskit.transpiler.passes.utils.remove_final_measurements' (/usr/local/lib/python3.12/dist-packages/qiskit/transpiler/passes/utils/remove_final_measurements.py)\n",
        "ERROR:stevedore.extension:Could not load 'ibm_fractional': cannot import name 'calc_final_ops' from 'qiskit.transpiler.passes.utils.remove_final_measurements' (/usr/local/lib/python3.12/dist-packages/qiskit/transpiler/passes/utils/remove_final_measurements.py)\n"
      ]
    },
    {
      "output_type": "stream",
      "name": "stdout",
      "text": [
        "Hamiltonian: 4 qubits, 15 Pauli terms\n",
        "Circuit: 4 qubits, depth 4\n"
      ]
    },
    {
      "output_type": "error",
      "ename": "TranspilerError",
      "evalue": "'Invalid plugin name ibm_dynamic_circuits for stage translation'",
      "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
        "\u001b[0;31mTranspilerError\u001b[0m                           Traceback (most recent call last)",
        "\u001b[0;32m/tmp/ipykernel_18018/3199051225.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m \u001b[0;31m# Transpile for the real backend using qiskit.transpile (avoids plugin issues)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 39\u001b[0;31m \u001b[0misa_circuit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtranspile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbound_circuit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbackend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbackend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimization_level\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     40\u001b[0m \u001b[0misa_hamiltonian\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhamiltonian_spo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_layout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0misa_circuit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/qiskit/utils/deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    182\u001b[0m                 \u001b[0mpredicate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpredicate\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    183\u001b[0m             )\n\u001b[0;32m--> 184\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    185\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    186\u001b[0m         \u001b[0;31m# When decorators get called repeatedly, `func` refers to the result of the prior\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/qiskit/utils/deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    182\u001b[0m                 \u001b[0mpredicate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpredicate\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    183\u001b[0m             )\n\u001b[0;32m--> 184\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    185\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    186\u001b[0m         \u001b[0;31m# When decorators get called repeatedly, `func` refers to the result of the prior\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/qiskit/utils/deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    182\u001b[0m                 \u001b[0mpredicate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpredicate\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    183\u001b[0m             )\n\u001b[0;32m--> 184\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    185\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    186\u001b[0m         \u001b[0;31m# When decorators get called repeatedly, `func` refers to the result of the prior\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/qiskit/utils/deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    182\u001b[0m                 \u001b[0mpredicate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpredicate\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    183\u001b[0m             )\n\u001b[0;32m--> 184\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    185\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    186\u001b[0m         \u001b[0;31m# When decorators get called repeatedly, `func` refers to the result of the prior\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/qiskit/compiler/transpiler.py\u001b[0m in \u001b[0;36mtranspile\u001b[0;34m(circuits, backend, basis_gates, inst_map, coupling_map, backend_properties, initial_layout, layout_method, routing_method, translation_method, scheduling_method, instruction_durations, dt, approximation_degree, timing_constraints, seed_transpiler, optimization_level, callback, output_name, unitary_synthesis_method, unitary_synthesis_plugin_config, target, hls_config, init_method, optimization_method, ignore_backend_supplied_default_methods, num_processes, qubits_initially_zero)\u001b[0m\n\u001b[1;32m    421\u001b[0m             \u001b[0mmodule\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"qiskit\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    422\u001b[0m         )\n\u001b[0;32m--> 423\u001b[0;31m         pm = generate_preset_pass_manager(\n\u001b[0m\u001b[1;32m    424\u001b[0m             \u001b[0moptimization_level\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    425\u001b[0m             \u001b[0mtarget\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/qiskit/utils/deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    182\u001b[0m                 \u001b[0mpredicate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpredicate\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    183\u001b[0m             )\n\u001b[0;32m--> 184\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    185\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    186\u001b[0m         \u001b[0;31m# When decorators get called repeatedly, `func` refers to the result of the prior\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/qiskit/utils/deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    182\u001b[0m                 \u001b[0mpredicate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpredicate\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    183\u001b[0m             )\n\u001b[0;32m--> 184\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    185\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    186\u001b[0m         \u001b[0;31m# When decorators get called repeatedly, `func` refers to the result of the prior\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/qiskit/utils/deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    182\u001b[0m                 \u001b[0mpredicate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpredicate\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    183\u001b[0m             )\n\u001b[0;32m--> 184\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    185\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    186\u001b[0m         \u001b[0;31m# When decorators get called repeatedly, `func` refers to the result of the prior\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/qiskit/utils/deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    182\u001b[0m                 \u001b[0mpredicate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpredicate\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    183\u001b[0m             )\n\u001b[0;32m--> 184\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    185\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    186\u001b[0m         \u001b[0;31m# When decorators get called repeatedly, `func` refers to the result of the prior\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/qiskit/transpiler/preset_passmanagers/generate_preset_pass_manager.py\u001b[0m in \u001b[0;36mgenerate_preset_pass_manager\u001b[0;34m(optimization_level, backend, target, basis_gates, inst_map, coupling_map, instruction_durations, backend_properties, timing_constraints, initial_layout, layout_method, routing_method, translation_method, scheduling_method, approximation_degree, seed_transpiler, unitary_synthesis_method, unitary_synthesis_plugin_config, hls_config, init_method, optimization_method, dt, qubits_initially_zero, _skip_target)\u001b[0m\n\u001b[1;32m    469\u001b[0m             \u001b[0mpm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlevel_0_pass_manager\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpm_config\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    470\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0moptimization_level\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 471\u001b[0;31m             \u001b[0mpm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlevel_1_pass_manager\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpm_config\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    472\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0moptimization_level\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    473\u001b[0m             \u001b[0mpm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlevel_2_pass_manager\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpm_config\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/qiskit/transpiler/preset_passmanagers/level1.py\u001b[0m in \u001b[0;36mlevel_1_pass_manager\u001b[0;34m(pass_manager_config)\u001b[0m\n\u001b[1;32m     75\u001b[0m         \u001b[0mrouting\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     76\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 77\u001b[0;31m     translation = plugin_manager.get_passmanager_stage(\n\u001b[0m\u001b[1;32m     78\u001b[0m         \u001b[0;34m\"translation\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtranslation_method\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpass_manager_config\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimization_level\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     79\u001b[0m     )\n",
        "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/qiskit/transpiler/preset_passmanagers/plugin.py\u001b[0m in \u001b[0;36mget_passmanager_stage\u001b[0;34m(self, stage_name, plugin_name, pm_config, optimization_level)\u001b[0m\n\u001b[1;32m    244\u001b[0m             )\n\u001b[1;32m    245\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mstage_name\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"translation\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 246\u001b[0;31m             return self._build_pm(\n\u001b[0m\u001b[1;32m    247\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtranslation_plugins\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstage_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mplugin_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpm_config\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimization_level\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    248\u001b[0m             )\n",
        "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/qiskit/transpiler/preset_passmanagers/plugin.py\u001b[0m in \u001b[0;36m_build_pm\u001b[0;34m(self, stage_obj, stage_name, plugin_name, pm_config, optimization_level)\u001b[0m\n\u001b[1;32m    267\u001b[0m     ):\n\u001b[1;32m    268\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mplugin_name\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mstage_obj\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 269\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mTranspilerError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Invalid plugin name {plugin_name} for stage {stage_name}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    270\u001b[0m         \u001b[0mplugin_obj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstage_obj\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mplugin_name\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    271\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mplugin_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpass_manager\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpm_config\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimization_level\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mTranspilerError\u001b[0m: 'Invalid plugin name ibm_dynamic_circuits for stage translation'"
      ]
    }
  ]
}

Agent

The IBM runtime transpiler plugins are broken with qiskit 1.4.x. I need to bypass the IBM plugin by explicitly setting translation_method to the default. Let me fix this.

Agent

▸ mcp__colab-proxy-mcp__colab-proxy-mcp_update_cell {"cellId":"iYRcYErzkGSB","content":"from qiskit import transpile\nfrom qiskit...

Input

{"cellId":"iYRcYErzkGSB","content":"from qiskit import transpile\nfrom qiskit_ibm_runtime import EstimatorV2, Session\nfrom qiskit.quantum_info import SparsePauliOp\n\n# Rebuild H2 at equilibrium for ...

Output

{
}

Agent

▸ mcp__colab-proxy-mcp__colab-proxy-mcp_run_code_cell {"cellId":"iYRcYErzkGSB"}

Input

{
  "cellId": "iYRcYErzkGSB"
}

Output

{
  "outputs": [
    {
      "output_type": "stream",
      "name": "stderr",
      "text": [
        "ERROR:stevedore.extension:Could not load 'ibm_backend': cannot import name 'calc_final_ops' from 'qiskit.transpiler.passes.utils.remove_final_measurements' (/usr/local/lib/python3.12/dist-packages/qiskit/transpiler/passes/utils/remove_final_measurements.py)\n",
        "ERROR:stevedore.extension:Could not load 'ibm_dynamic_and_fractional': cannot import name 'calc_final_ops' from 'qiskit.transpiler.passes.utils.remove_final_measurements' (/usr/local/lib/python3.12/dist-packages/qiskit/transpiler/passes/utils/remove_final_measurements.py)\n",
        "ERROR:stevedore.extension:Could not load 'ibm_dynamic_circuits': cannot import name 'calc_final_ops' from 'qiskit.transpiler.passes.utils.remove_final_measurements' (/usr/local/lib/python3.12/dist-packages/qiskit/transpiler/passes/utils/remove_final_measurements.py)\n",
        "ERROR:stevedore.extension:Could not load 'ibm_fractional': cannot import name 'calc_final_ops' from 'qiskit.transpiler.passes.utils.remove_final_measurements' (/usr/local/lib/python3.12/dist-packages/qiskit/transpiler/passes/utils/remove_final_measurements.py)\n",
        "ERROR:stevedore.extension:Could not load 'ibm_backend': cannot import name 'calc_final_ops' from 'qiskit.transpiler.passes.utils.remove_final_measurements' (/usr/local/lib/python3.12/dist-packages/qiskit/transpiler/passes/utils/remove_final_measurements.py)\n",
        "ERROR:stevedore.extension:Could not load 'ibm_dynamic_and_fractional': cannot import name 'calc_final_ops' from 'qiskit.transpiler.passes.utils.remove_final_measurements' (/usr/local/lib/python3.12/dist-packages/qiskit/transpiler/passes/utils/remove_final_measurements.py)\n",
        "ERROR:stevedore.extension:Could not load 'ibm_dynamic_circuits': cannot import name 'calc_final_ops' from 'qiskit.transpiler.passes.utils.remove_final_measurements' (/usr/local/lib/python3.12/dist-packages/qiskit/transpiler/passes/utils/remove_final_measurements.py)\n",
        "ERROR:stevedore.extension:Could not load 'ibm_fractional': cannot import name 'calc_final_ops' from 'qiskit.transpiler.passes.utils.remove_final_measurements' (/usr/local/lib/python3.12/dist-packages/qiskit/transpiler/passes/utils/remove_final_measurements.py)\n"
      ]
    },
    {
      "output_type": "stream",
      "name": "stdout",
      "text": [
        "Hamiltonian: 4 qubits, 15 Pauli terms\n",
        "Circuit: 4 qubits, depth 4\n",
        "Transpiled circuit: depth 1\n",
        "Gate counts: {'x': 2}\n",
        "\n",
        "Ready to submit to ibm_fez...\n"
      ]
    }
  ]
}

Agent

Transpilation worked. Circuit is just depth 1 with 2 X gates (the HF initial state with zero VQE parameters simplifies dramatically). Now let me submit the job to IBM hardware.

Agent

▸ mcp__colab-proxy-mcp__colab-proxy-mcp_add_code_cell {"cellIndex":14,"language":"python","code":"# Submit the job to IBM Quantum h...

Input

{"cellIndex":14,"language":"python","code":"# Submit the job to IBM Quantum hardware\nprint(f\"Submitting to {backend.name} (156 qubits)...\")\nprint(f\"Circuit: {isa_circuit.num_qubits} qubits used, ...

Output

{
  "newCellId": "4E7J4QLKkW73"
}

Agent

▸ mcp__colab-proxy-mcp__colab-proxy-mcp_run_code_cell {"cellId":"4E7J4QLKkW73"}

Input

{
  "cellId": "4E7J4QLKkW73"
}

Output

{
  "outputs": [
    {
      "output_type": "stream",
      "name": "stdout",
      "text": [
        "Submitting to ibm_fez (156 qubits)...\n",
        "Circuit: 156 qubits used, depth 1\n",
        "Observable: 15 Pauli terms\n",
        "\n"
      ]
    },
    {
      "output_type": "error",
      "ename": "RequestsApiError",
      "evalue": "'400 Client Error: Bad Request for url: https://quantum.cloud.ibm.com/api/v1/sessions. {\"errors\":[{\"code\":1352,\"message\":\"You are not authorized to run a session when using the open plan.\",\"solution\":\"Create an instance of a different plan type or use a different [execution mode](https://quantum.cloud.ibm.com/docs/guides/execution-modes).\",\"more_info\":\"https://cloud.ibm.com/apidocs/quantum-computing#error-handling\"}],\"trace\":\"42d1043d-2fea-47ed-aea6-35e833712d12\"}\\n'",
      "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
        "\u001b[0;31mHTTPError\u001b[0m                                 Traceback (most recent call last)",
        "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/qiskit_ibm_runtime/api/session.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, bare, **kwargs)\u001b[0m\n\u001b[1;32m    328\u001b[0m             \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfinal_url\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 329\u001b[0;31m             \u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_for_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    330\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mRequestException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mex\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/requests/models.py\u001b[0m in \u001b[0;36mraise_for_status\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1025\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mhttp_error_msg\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1026\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mHTTPError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhttp_error_msg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1027\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mHTTPError\u001b[0m: 400 Client Error: Bad Request for url: https://quantum.cloud.ibm.com/api/v1/sessions",
        "\nThe above exception was the direct cause of the following exception:\n",
        "\u001b[0;31mRequestsApiError\u001b[0m                          Traceback (most recent call last)",
        "\u001b[0;32m/tmp/ipykernel_18018/1546127838.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Observable: {len(isa_hamiltonian)} Pauli terms\\n\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mSession\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbackend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbackend\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m     \u001b[0mestimator\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mEstimatorV2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/qiskit_ibm_runtime/session.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, backend, max_time, create_new)\u001b[0m\n\u001b[1;32m    129\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_instance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_instance\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfiguration\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msimulator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session_id\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_create_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcreate_new\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcreate_new\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    132\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_create_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_new\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/qiskit_ibm_runtime/session.py\u001b[0m in \u001b[0;36m_create_session\u001b[0;34m(self, create_new)\u001b[0m\n\u001b[1;32m    134\u001b[0m         \u001b[0;34m\"\"\"Create a session.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    135\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_service\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mQiskitRuntimeService\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mcreate_new\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 136\u001b[0;31m             session = self._service._get_api_client(self._instance).create_session(\n\u001b[0m\u001b[1;32m    137\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_instance\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_max_time\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"dedicated\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    138\u001b[0m             )\n",
        "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/qiskit_ibm_runtime/api/clients/runtime.py\u001b[0m in \u001b[0;36mcreate_session\u001b[0;34m(self, backend, instance, max_time, mode)\u001b[0m\n\u001b[1;32m    220\u001b[0m             \u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mExecution\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    221\u001b[0m         \"\"\"\n\u001b[0;32m--> 222\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_api\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mruntime_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession_id\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbackend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minstance\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_time\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    223\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    224\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcancel_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msession_id\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/qiskit_ibm_runtime/api/rest/runtime_session.py\u001b[0m in \u001b[0;36mcreate\u001b[0;34m(self, backend, instance, max_time, mode)\u001b[0m\n\u001b[1;32m     59\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmax_time\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m             \u001b[0mpayload\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"max_ttl\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmax_time\u001b[0m  \u001b[0;31m# type: ignore[assignment]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 61\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpost\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpayload\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_HEADER_JSON_CONTENT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjson\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcancel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/requests/sessions.py\u001b[0m in \u001b[0;36mpost\u001b[0;34m(self, url, data, json, **kwargs)\u001b[0m\n\u001b[1;32m    635\u001b[0m         \"\"\"\n\u001b[1;32m    636\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 637\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"POST\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mjson\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    638\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    639\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/qiskit_ibm_runtime/api/session.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, bare, **kwargs)\u001b[0m\n\u001b[1;32m    355\u001b[0m                     \u001b[0;34mf\"https://docs.quantum.ibm.com/announcements/service-alerts {message}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    356\u001b[0m                 )\n\u001b[0;32m--> 357\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mRequestsApiError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatus_code\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mex\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    358\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    359\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mRequestsApiError\u001b[0m: '400 Client Error: Bad Request for url: https://quantum.cloud.ibm.com/api/v1/sessions. {\"errors\":[{\"code\":1352,\"message\":\"You are not authorized to run a session when using the open plan.\",\"solution\":\"Create an instance of a different plan type or use a different [execution mode](https://quantum.cloud.ibm.com/docs/guides/execution-modes).\",\"more_info\":\"https://cloud.ibm.com/apidocs/quantum-computing#error-handling\"}],\"trace\":\"42d1043d-2fea-47ed-aea6-35e833712d12\"}\\n'"
      ]
    }
  ]
}

Agent

The open plan doesn't support sessions. I need to use "single job" mode instead -- submit directly to the backend without a session context.

Agent

▸ mcp__colab-proxy-mcp__colab-proxy-mcp_update_cell {"cellId":"4E7J4QLKkW73","content":"# Submit the job to IBM Quantum hardware ...

Input

{"cellId":"4E7J4QLKkW73","content":"# Submit the job to IBM Quantum hardware (single-job mode for open plan)\nprint(f\"Submitting to {backend.name} (156 qubits)...\")\nprint(f\"Circuit: {isa_circuit.n...

Output

{
}

Agent

▸ mcp__colab-proxy-mcp__colab-proxy-mcp_run_code_cell {"cellId":"4E7J4QLKkW73"}

Input

{
  "cellId": "4E7J4QLKkW73"
}

Output

{
  "outputs": [
    {
      "output_type": "stream",
      "name": "stdout",
      "text": [
        "Submitting to ibm_fez (156 qubits)...\n",
        "Circuit: 156 logical qubits, depth 1\n",
        "Observable: 15 Pauli terms\n",
        "\n",
        "Job ID: d6tca5msh9gc73digp2g\n",
        "Waiting for results from real quantum hardware...\n",
        "\n",
        "==================================================\n",
        "RESULTS FROM IBM QUANTUM HARDWARE (ibm_fez)\n",
        "==================================================\n",
        "Electronic energy:      -1.843475 Hartree\n",
        "Nuclear repulsion:      0.719969 Hartree\n",
        "Total energy:           -1.123506 Hartree\n",
        "Standard deviation:     0.004275\n",
        "\n",
        "Comparison at equilibrium (0.735 A):\n",
        "  Exact (classical):    -1.137306 Hartree\n",
        "  VQE (simulator):      -1.137306 Hartree\n",
        "  IBM Hardware:         -1.123506 Hartree\n",
        "  Hardware error:       13.80 mHartree\n",
        "  (1.21% relative error)\n"
      ]
    }
  ]
}

Agent

We got results from real quantum hardware! Job d6tca5msh9gc73digp2g completed on ibm_fez. The hardware result has 13.80 mHartree error (1.21% relative) compared to the exact value -- that's real quantum noise in action.

Now let me add the final comparison visualization and conclusions.

Agent

▸ mcp__colab-proxy-mcp__colab-proxy-mcp_add_code_cell {"cellIndex":15,"language":"python","code":"# Final comparison visualization:...

Input

{"cellIndex":15,"language":"python","code":"# Final comparison visualization: Simulator vs Hardware vs Exact\nfig, ax = plt.subplots(figsize=(10, 6))\n\n# Plot PES curves\nax.plot(distances, exact_ene...

Output

{
  "newCellId": "QjDzo1HakvAM"
}

Agent

▸ mcp__colab-proxy-mcp__colab-proxy-mcp_run_code_cell {"cellId":"QjDzo1HakvAM"}

Input

{
  "cellId": "QjDzo1HakvAM"
}

Output



...98362 bytes truncated...

The tool call succeeded but the output was truncated. Full output saved to: [local tool-output path omitted]tool_d01930f4900158a7lDAKW6aYOs
Use the Task tool to have explore agent process this file with Grep and Read (with offset/limit). Do NOT read the full file yourself - delegate to save context.

Agent

Plot generated. Now let me add the summary and conclusions.

Agent

▸ mcp__colab-proxy-mcp__colab-proxy-mcp_add_text_cell {"cellIndex":16,"content":"## Summary & Results\n\n### What we computed\n| Me...

Input

{"cellIndex":16,"content":"## Summary & Results\n\n### What we computed\n| Method | Energy (Hartree) | Error vs Exact |\n|---|---|---|\n| Hartree-Fock (classical) | -1.11700 | 20.31 mHa |\n| VQE on Si...

Output

{
  "newCellId": "tQB8Q-okk0zi"
}

Agent

The notebook is complete and fully executed in Colab. Here's what we built and ran:

Notebook summary (17 cells in Colab)

Cell	Type	What it does
1	Markdown	Title, goal, explanation of PES and VQE
2	Code	Install deps (qiskit 1.4.x, qiskit-nature, pyscf)
3	Code	Import verification + version check
4	Markdown	H2 Hamiltonian explanation
5	Code	Build H2 Hamiltonian at 0.735 A, Jordan-Wigner mapping (4 qubits, 15 Pauli terms)
6	Markdown	VQE single-point explanation
7	Code	Single-point: Exact vs HF vs VQE — VQE captures 100% correlation energy
8	Markdown	PES scan explanation
9	Code	15-point PES scan (0.3-3.0 A) — completed in 8.2 seconds
10	Markdown	Visualization explanation
11	Code	PES plot + error plot
12	Markdown	IBM hardware explanation
13	Code	Connect to IBM Quantum (Colab secrets), selected `ibm_fez` (156 qubits)
14	Code	Transpile circuit for hardware
15	Code	Submit job to ibm_fez — got -1.1235 Ha (13.8 mHa error, 1.21%)
16	Code	Final comparison plot (simulator vs hardware)
17	Markdown	Results table + conclusions

Real hardware results

Job ID: d6tca5msh9gc73digp2g
Backend: ibm_fez (156 superconducting qubits)
Hardware energy: -1.1235 Hartree (1.21% error from exact)
Simulator energy: -1.1373 Hartree (exact match)

The notebook is ready in your Colab.

1. It resolved the dependency mess

The first real issue was package compatibility.

The Qiskit ecosystem is currently split. Some chemistry-related libraries still depend on the 1.x line and do not yet cleanly support Qiskit 2.x.

The agent figured that out quickly and pinned a working combination:

!pip install 'qiskit>=1.4,<2.0' qiskit-nature==0.7.2 qiskit-algorithms qiskit-aer pyscf qiskit-ibm-runtime

Versions that worked:

Qiskit 1.4.5
Qiskit Nature 0.7.2
Qiskit Algorithms 0.4.0
PySCF 2.12.1

This is exactly the kind of friction that normally burns time when working manually. The agent saw the environment, adjusted, and moved on.

2. It built the molecular Hamiltonian

Next, the agent used PySCF to compute the electronic integrals for H₂ at equilibrium bond length and mapped the molecule into a qubit Hamiltonian.

The result:

Molecule: H2
Spatial orbitals: 2
Particles (alpha, beta): (1, 1)
Nuclear repulsion energy: 0.719969 Hartree

Qubit Hamiltonian:
  Qubits required: 4
  Pauli terms: 15

Two hydrogen atoms became a 4-qubit problem with 15 Pauli terms. Small enough to simulate exactly, but still a real quantum chemistry workload.

3. It ran VQE and debugged the first major failure

At equilibrium geometry, the agent ran three methods:

Method	Energy (Hartree)	Error vs Exact
Hartree-Fock	-1.11700	20.31 mHa
VQE (simulator)	-1.13731	0.00 mHa
Exact diagonalization	-1.13731	-

This part mattered scientifically.

The VQE result matched the exact answer on the simulator, while Hartree-Fock left a noticeable gap. That is the core promise of quantum chemistry workflows like this: quantum methods can recover effects that standard approximations miss.

But the first VQE attempt did not work.

The agent hit:

AlgorithmError: The primitive job to evaluate the energy failed!

The cause was a version mismatch between the primitive being used and the API style expected by qiskit-algorithms.

The fix was to switch to StatevectorEstimator, rerun the notebook, and continue.

That debugging loop is the real value of Colab MCP. The agent does not just generate code. It sees the traceback, changes the notebook, and re-executes until the workflow is alive again.

4. It scanned the full potential energy surface

Once the equilibrium point worked, the agent swept 15 bond distances from 0.3 Å to 3.0 Å:

Scanning 15 bond distances from 0.30 to 3.00 Angstrom
[1/15]  d = 0.3000 A ... HF=-0.593828  Exact=-0.601804  VQE=-0.601804
[2/15]  d = 0.4929 A ... HF=-1.036415  Exact=-1.048389  VQE=-1.048389
[3/15]  d = 0.6857 A ... HF=-1.116747  Exact=-1.135016  VQE=-1.135016
...
[14/15] d = 2.8071 A ... HF=-0.671046  Exact=-0.934126  VQE=-0.934125
[15/15] d = 3.0000 A ... HF=-0.656048  Exact=-0.933632  VQE=-0.933632

PES scan complete in 8.2 seconds

This is where the scientific point became obvious.

As the bond stretches, Hartree-Fock gets much worse. At 3.0 Å, it is off by 277 mHartree. VQE stays essentially aligned with the exact result across the full curve.

So this was not just an orchestration demo. The experiment also captured a real chemistry phenomenon that motivates quantum algorithms in the first place.

Then I pushed it to real quantum hardware

After the simulator run, I wanted to see whether the same workflow would survive contact with real hardware.

The agent connected to IBM Quantum using a token stored in Colab Secrets:

from google.colab import userdata
IBM_TOKEN = userdata.get('IBM_TOKEN')

service = QiskitRuntimeService(
    channel="ibm_quantum_platform",
    token=IBM_TOKEN
)

Available backends included:

ibm_fez: 156 qubits, 0 pending jobs
ibm_torino: 133 qubits, 0 pending jobs
ibm_kingston: 156 qubits, 119 pending jobs
ibm_marrakesh: 156 qubits, 0 pending jobs

The agent selected ibm_fez.

Then things got messy again.

More breakage, more fixes

The IBM runtime transpiler plugins were not compatible with the Qiskit 1.4.x environment used by the chemistry stack.

That caused:

TranspilerError: Invalid plugin name ibm_dynamic_circuits

The fix was to bypass the plugin path and transpile directly against the backend's basis gates and coupling map.

Then another issue appeared: IBM's open plan did not support Sessions in this setup.

So the agent switched from a Session-based flow to:

EstimatorV2(mode=backend)

Again, this is where the workflow becomes meaningful.

The agent was not just writing a notebook. It was navigating a real compatibility chain across Colab, Qiskit, and IBM Runtime.

The hardware result

The final hardware run returned:

Job ID: d6tca5msh9gc73digp2g
Backend: ibm_fez (156 qubits)

Electronic energy:      -1.843475 Hartree
Nuclear repulsion:       0.719969 Hartree
Total energy:           -1.123506 Hartree
Standard deviation:      0.004275

Comparison at equilibrium (0.735 A):
  Exact (classical):    -1.137306 Hartree
  VQE (simulator):      -1.137306 Hartree
  IBM Hardware:         -1.123506 Hartree
  Hardware error:       13.80 mHartree (1.21% relative error)

To verify this was not just a simulator handoff, here is the completed IBM Quantum job record from the run:

So the hardware result landed in the right range, but it was clearly degraded compared to the ideal simulator.

That is not a failure. That is the current reality of NISQ-era quantum computing.

The algorithm worked. The notebook worked. The cloud workflow worked. But physical qubits still introduce noise through gate error, decoherence, and measurement error.

And that contrast was valuable to see in one end-to-end run.

Why this experiment was meaningful

There were two reasons this was worth doing.

1. It showed a real scientific difference

H₂ is small, but meaningful.

It is the smallest benchmark that still exposes whether the algorithmic stack is doing real work. The simulator showed that VQE could recover the exact solution for this system, and the full bond scan highlighted where classical approximation methods begin to fail.

2. It showed a new way to work with agents

This was the bigger surprise.

Colab MCP made it practical to hand a messy technical objective to an agent and let it operate inside a cloud notebook until the notebook actually worked.

That included:

package installation
notebook structure
traceback-driven debugging
reruns after fixes
cloud execution
optional handoff to real quantum hardware

That is much more interesting than simple code generation.

It points toward a new working style where Colab becomes an executable cloud workspace for AI agents, not just a place humans paste code into.

What I learned

Colab MCP is genuinely useful

This did not feel like a gimmick.

The agent created 17 cells, handled several compatibility issues, ran a 15-point scientific sweep, submitted a real quantum job, and produced usable outputs quickly.

That is exactly the kind of workflow where local-only agent setups start to feel cramped.

The package ecosystem is still the roughest part

The biggest friction was not the quantum algorithm.

It was Python package compatibility across Qiskit, chemistry libraries, and IBM runtime tooling.

That is where a notebook-controlling agent helps most. It can read the errors and iterate much faster than a human manually bouncing between terminal, browser, and documentation.

Real hardware is still noisy

The simulator matched the exact answer.

The hardware result came back with 1.21% relative error on a very small problem. That is a useful reminder that the software stack may be getting smoother while the hardware frontier is still about error mitigation and long-term fault tolerance.

Colab is an underrated chemistry environment

PySCF installs cleanly in Colab's Linux runtime and avoids the setup pain many people hit on Windows.

For this kind of workflow, the combination of Colab plus MCP is not just convenient. It may actually be the easiest path from idea to result.

How to reproduce it

Set up the Colab MCP server in your agent config
Open a Google Colab notebook in your browser
Add your IBM Quantum token to Colab Secrets as IBM_TOKEN
Give the agent a concrete task and let it build the notebook

In my case, the task was ambitious enough to break a few times, which is exactly why it was useful.

Final take

Google's Colab MCP announcement is not just about making notebook automation easier.

It changes what Colab can be.

Instead of treating Colab as a notebook you manually edit, you can start treating it as a cloud workspace an agent can actively operate.

This experiment pushed that idea through a workflow that included scientific Python, version conflicts, notebook authoring, parameter sweeps, and real hardware submission.

The result was not magic. It was a real notebook, built in the cloud, debugged in place, and executed end to end by an agent.

That is why I think Colab MCP is worth paying attention to.

> If this was my first stress test of Colab MCP, it was a very convincing one.

Drop Your Challenge Submission Here - v0

Nikoloz Turazashvili (@axrisi) — Tue, 17 Mar 2026 03:38:27 +0000

Built something for a DEV Challenge but feel like not enough people saw it?

This post is for exactly that.

Sometimes great submissions get buried in the challenge feed and never get the attention they deserve. So let’s fix that.

Use this thread to show what you built, meet other builders, and get real feedback on your project.

Share your submission in the comments

Include:

your project title
a short description of what it does
the link to your submission
what kind of feedback you want

Why this thread exists

Not every strong project gets noticed the first time.

Some are posted early. Some are posted late. Some just get lost in the volume.

This is a second chance to put your work in front of more people.

What you can get from posting here

more visibility
feedback from other developers
new ideas for improving your project
more discovery for underrated submissions

If you made something cool, drop it below.

You might find your next supporter, collaborator, or your first real traction in this thread.

Notion Skills Registry: A Package Manager for AI Agent Skills with MCP

Nikoloz Turazashvili (@axrisi) — Mon, 16 Mar 2026 09:28:46 +0000

This is a submission for the Notion MCP Challenge

What I Built

I built Notion Skills Registry, a lightweight package-registry-style system for AI skills powered by Notion MCP.

The problem is simple: once you start using AI coding agents seriously across multiple repos, skill management becomes messy fast.

Prompts get duplicated.

Global skill folders become cluttered.

Different projects end up using different versions of the same workflow.

And once a team starts sharing skills, things turn into copy-paste chaos.

So I built a better pattern:

keep the real skill content in Notion
organize skills by workflow or use case
version them cleanly with names like content-research-writer@v1 and content-research-writer@v2
publish approved versions through a central Skill Versions page
install only the exact versions each repo needs into Claude Code or OpenCode

Instead of stuffing everything into one global skills folder, each project gets a clean local wrapper skill, while the source of truth stays centralized in Notion.

That gives me the best of both worlds:

centralized skill authoring
per-project isolation
version control for prompts and workflows
easier team sharing
a much cleaner upgrade path

The result feels like a package manager for AI skills, except the registry is a Notion workspace and distribution happens through Notion MCP.

Why this is useful

This unlocks a much more scalable workflow for agentic development.

A single Notion workspace can now act as:

a skill registry
a version history for operational prompts
a distribution layer for AI workflows
a lightweight governance system for approving what projects are allowed to install

That matters because AI skills are turning into real infrastructure.

Once teams rely on them for research, content ops, analysis, coding workflows, QA, or internal automations, you need more than random markdown files copied across repos.

You need:

a source of truth
clean versioning
controlled rollout
a way to keep projects in sync without breaking everything

That is exactly what this project is designed to do.

How it works

At a high level:

Skills live in Notion as normal pages.
Each version gets its own page, such as content-research-writer@v1.
A Notion page called Skill Versions acts as the registry index.
Management skills handle install, update, and version checks.
A repo installs a local wrapper skill that points to the exact Notion page.
The real instructions remain centralized in Notion.

The current workflow includes four management commands:

/notion-init
/notion-install
/notion-update
/notion-check-updates

These commands make it possible to initialize a local registry manifest inside a repo, install a skill from Notion, detect newer approved versions, and update wrappers without manually copying prompt files around.

Example structure

Inside Notion, the registry looks like this conceptually:

[TEMPLATE] Notion Skills Registry/
  Skills/
    Content Research Writer/
      content-research-writer@v1
      content-research-writer@v2
    Video Downloader/
      video-downloader@v1
  Skill Versions/
    Content Research Writer
      content-research-writer@v2 - approved
      content-research-writer@v1 - approved
    Video Downloader
      video-downloader@v1 - approved

That means teams can keep drafts, multiple releases, and approved production-ready versions all in one place.

Then per repo, the installed output stays clean:

.opencode/
  notion-skills.json
  skills/
    content-research-writer/
      SKILL.md

or:

.claude/
  notion-skills.json
  skills/
    content-research-writer/
      SKILL.md

What makes this interesting

A lot of people use Notion as documentation.

This project uses Notion as live skill infrastructure.

That shift is what makes it exciting.

Notion stops being just a place where instructions are stored and starts becoming the operational registry that AI agents can install from.

So instead of asking:

Where is the latest version of this prompt?

You can ask:

Which approved version should this repo install right now?

That is a much more useful primitive for teams building with agents.

Video Demo

Here is a walkthrough of the workflow in action:

Show us the code

GitHub repo:

turazashvili / notion-skills-registry

A dynamic skill registry for your AI agent workflow. Stop copying prompts across repos, avoid cluttering global skills, and manage versioned skills in one centralized Notion workspace for Claude Code, OpenCode, and more integrations coming soon.

Notion Skills Registry

Use Notion as a versioned skill registry for OpenCode, Claude Code, and other AI agent workflows.

Instead of copying prompts across repos or stuffing everything into a global skills folder, this project lets you keep skills in one centralized Notion workspace and install them into projects as local wrappers.

Think of it like a lightweight package registry for AI skills, powered by Notion MCP.

What problem this solves

Managing AI skills across projects gets messy fast:

prompts get duplicated across repos
global skills folders become cluttered
teams lose track of the latest approved version
sharing reliable workflows turns into copy-paste chaos
updating a skill in one place does not propagate cleanly

Notion Skills Registry solves that by giving you:

one source of truth for your skills
clear versioning such as content-research-writer@v1 and content-research-writer@v2
approved releases that projects can safely install
project-level isolation, so…

View on GitHub

Notion template:

Notion

A tool that connects everyday work into one space. It gives you and your teams AI tools—search, writing, note-taking—inside an all-in-one, flexible workspace.

skills-mcp.notion.site

How I Used Notion MCP

Notion MCP is the core enabler here.

I used it to turn a Notion workspace into something installable and operational for AI coding workflows.

More specifically, Notion MCP makes it possible for the management skills to:

access the registry page directly from Notion
resolve linked skill version pages
load the exact skill content associated with a selected version
keep local project wrappers connected to a centralized source of truth

That unlocks a workflow where Notion is no longer just static reference material.

It becomes:

a registry
a release surface
a collaboration layer
a versioned distribution system for agent skills

In practice, that means I can author and revise skills in Notion, approve the versions I want projects to consume, and let repositories install either:

the latest approved release
or a pinned version for stability

That model is powerful because different repos often need different levels of change tolerance.

Some should auto-follow the latest approved skill.

Others should stay pinned until someone intentionally upgrades them.

Notion MCP gives me the bridge between those two worlds: centralized authoring and local execution.

What this unlocks beyond this challenge

I think this pattern has a lot of room to expand.

This could evolve into:

shared team skill catalogs
internal skill release workflows
approval pipelines for prompt changes
skill packs by department or project
multi-tool support beyond Claude Code and OpenCode
analytics around which skills are installed where

In other words, this is not just a Notion page with prompts.

It is the beginning of a real skill distribution system for agentic work.

Why I think this is challenge-worthy

This project is useful immediately, but it also points toward a bigger idea.

As AI agents become part of everyday development workflows, teams will need real systems for managing the instructions those agents rely on.

Notion MCP makes that possible in a surprisingly natural way.

It lets a familiar workspace become programmable infrastructure.

That is why I think this project stands out:

it solves a real workflow problem
it uses Notion MCP in a way that is practical, not decorative
it creates a reusable system others can adopt
it turns Notion into something much more operational than a notes app

Closing

If you are building with Claude Code, OpenCode, or similar agent workflows, and you are tired of duplicating prompts across repos, this pattern is worth trying.

Notion Skills Registry gives you one place to create, organize, version, approve, and distribute AI skills cleanly.

That is the workflow I wanted for myself, so I built it.

If this resonates, check out the template and adapt it to your own agent stack.

Template: Notion Skills Registry Template

UPDATE:
This Template now available on Notion Marketplace:
https://www.notion.com/templates/skills-registry-mcp

The Local AI Hardware Guide (2026)

Nikoloz Turazashvili (@axrisi) — Sun, 08 Mar 2026 18:17:39 +0000

If you are trying to build a machine to run local AI agents, stop building it like a gaming PC.

Most people make the mistake of prioritizing a faster processor and a powerful GPU with high clock speeds. But when it comes to running local Large Language Models (LLMs), there is one metric that matters more than anything else combined: VRAM (Video RAM).

Let's break down exactly what you need to build a local AI powerhouse without overspending.

The Kitchen Analogy: Why VRAM is King

To understand local AI hardware, think of your computer as a restaurant kitchen:

The Graphics Card (GPU) is the Chef: The processing speed determines how fast the chef's hands move.
VRAM is the Kitchen Counter: This is where the recipe (the AI model) sits while the chef is cooking.
System RAM is the Back Storage Room: It’s where things go when they don't fit on the counter, but running back and forth takes time.

When you load a model, like a 7B (7 billion instruction) model, that entire recipe needs to fit on the counter. If the recipe is too big and spills over into your regular system RAM, your output speed will drop from a smooth 40 tokens (words) per second to an unusable 2-3 words per second.

The "Dishes" Problem

Even if your model fits on the counter initially, as your conversation with the AI grows, the context takes up more and more memory—think of this as dirty dishes piling up on the counter. Eventually, you run out of room, and a model that started fast will slow to a crawl.

The Golden Rule: Buy the counter space, not hand speed.

Model Sizing Cheat Sheet (at 4-bit compression)

To fit massive models on consumer hardware, we use 4-bit compression (quantization), which shrinks the model with minimal quality loss.

7B models: ~5GB VRAM
14B models: ~10GB VRAM
32B models: ~20GB VRAM
70B models: ~40GB VRAM

The Hardware Tiers

Here is exactly what you should buy based on your budget and needs.

Tier 1: The Starter Build ($1,200 - $1,500)

This is the sweet spot for running 7B and 8B models (like DeepSeek Distill 7B or Llama 8B) for coding assistance, document summaries, and light agent workflows.

GPU: RTX 4060 Ti with 16GB VRAM. (Warning: Do NOT buy the 8GB version; it will fill up immediately.)
CPU: Ryzen 5.
RAM: 64GB System RAM.
Storage: 2TB SSD (your "pantry" for storing models).
Apple Equivalent: MacBook Pro or Mac Mini with 16GB Unified Memory. Because Apple uses unified memory, the GPU and main computer share the same giant counter space.

Tier 2: The Power User Build

For multi-step AI workflows and running 32B models that rival cloud quality.

GPU Options:
- Path A: RTX 4070 Ti Super (16GB VRAM) for faster processing.
- Path B (Recommended): Used RTX 3090 (24GB VRAM). Having 24GB of counter space is a game-changer, allowing you to run 32B models with plenty of room left over for long conversations.
CPU/RAM: Ryzen 7 processor with 64GB RAM.
Apple Equivalent: Mac Mini M4 Pro with 64GB Unified Memory. It will run slightly slower than Nvidia setups (around 11-12 tokens per second), but it is quiet, efficient, and simple.

Tier 3: The Enthusiast Build

Only build this if your workflow genuinely demands it.

GPU: RTX 4090 (24GB VRAM).
CPU/RAM: Ryzen 9 processor with massive 128GB System RAM.
Capabilities: Runs 32B models like butter and can even experiment with heavily compressed 70B models (though conversation length will be limited).
Apple Equivalent: Mac Studio M3 Ultra with 96GB Unified Memory. This can hold multiple models (reasoning, embedding, coding) in memory simultaneously.

(Note: While Raspberry Pi is great for edge experiments, it is not recommended as a daily driver for running LLMs.)

The Software Stack

Hardware is only half the battle. Here is what you need to get your models running:

Ollama: A powerful, simple command-line tool. You type one command, and the model downloads and runs.
LM Studio: If you prefer a visual, ChatGPT-style interface, this tool handles downloading, GPU detection, and serving the model.

Pro-Tip: Download the Right Format

Don't just download the most popular file.

Mac Users: Download models in GGUF format.
Windows/Nvidia Users: Look for AWQ format, which can offer faster response times and better quality on Nvidia cards.

Local vs. Cloud AI

Local AI agents won't entirely replace closed frontier models like ChatGPT, Claude, or Gemini for the heaviest reasoning tasks.

Think of local AI as your home gym and cloud AI as the commercial gym downtown. Your local setup handles 80% of your daily work: it offers total privacy, no API logs, no surprise bills, and works without the internet. But when you need to do the heavy lifting, you ping the cloud. The smartest setup is a hybrid of both.

Summary: Budget for the whole kitchen, but always prioritize the counter space (VRAM) over the chef's speed.

Upscale Any Image with AI for Free (Right Inside DEV): 7 Hugging Face Enhancers

Nikoloz Turazashvili (@axrisi) — Sun, 01 Mar 2026 15:21:04 +0000

Upscale any image with AI for free, without leaving DEV. Below is a handpicked collection of 7 Hugging Face image enhancers and upscalers you can run directly in your browser. Each one supports quick uploads and instant results, so you can test multiple models and keep the best output for your use case.

1. Finegrain Image Enhancer. 🔗

Short: High-quality detail enhancement and upscaling with a simple upload workflow.

2. Image Upscaling Playground (bookbot). 🔗

Short: Compare multiple super-resolution models with common scale options like 2× and 4×.

3. Tile Upscaler (gokaygokay). 🔗

Short: Tile-based upscaling that handles larger images while keeping edges and textures crisp.

4. Thera Arbitrary-Scale Super-Resolution (prs-eth). 🔗

Short: Super-resolution with flexible scaling (not only fixed 2×/4×), useful for custom target sizes.

5. InvSR (OAOA). 🔗

Short: Diffusion-based super-resolution aimed at recovering details, with tunable sampling steps.

6. SwinIR Super-resolution (givkashi). 🔗

Short: SwinIR transformer-based upscaling for clean, sharp enhancement on general images.

7. Lambda Super Resolution (lambdalabs). 🔗

Short: Straightforward upload-and-upscale demo for quick super-resolution results.

I Built a Gemini-Powered History Narrator 6 Months Ago. Here's What I'd Tell You Now.

Nikoloz Turazashvili (@axrisi) — Sat, 28 Feb 2026 17:04:10 +0000

This is a submission for the Built with Google Gemini: Writing Challenge

What I Built with Google Gemini

What if every faded photograph in your grandmother's attic could tell its own story?

That question led me to build the Historical Photo/Video Narrator — an app where you upload a historical photo or video, and Google Gemini narrates the history behind it. Not a caption. Not a label. A full narrative — the era, the context, what was likely happening just outside the frame. Then, if you want to go further, a "Re-imagine" feature lets you edit the photo with a text prompt. Want to see that 1920s street scene on a sunny day? Add color to a black-and-white portrait? Type it and Gemini generates it.

I built this in September 2025 for the Google AI Studio Multimodal Challenge. It placed well. I deployed it to Cloud Run and moved on to the next project.

That was six months ago. The app is still running.

Most challenge submissions are about something built last weekend. This one is about what happens after the hackathon high wears off — what breaks, what surprises you, and what you learn about a model by living with it for half a year.

Two Gemini models power the two core features:

gemini-2.5-flash handles narrative generation. It takes an image or video, analyzes the visual content, and produces a historically-grounded narrative. It doesn't just describe what it sees — it infers era, cultural context, and significance. A built-in text-to-speech feature lets you listen to the story instead of reading it.
gemini-2.5-flash-image (more on that name in a moment) powers the Re-imagine feature. Pass it a source image and a text prompt, and it generates an edited version. This is the part that makes users linger — the loop of "learn the story, then reimagine the scene" turns passive viewing into active exploration.

The UX flow is intentionally simple: Upload → Narrate → Listen → Re-imagine → Save locally. Everything stays in the browser. No accounts, no sign-ups, no data collection.

Demo

Try it yourself — upload any historical photo and let Gemini tell you what it sees.

Source: AI Studio Project

What I Learned

Six months of running an AI-powered app taught me things that the first weekend of building it never could. Here are the lessons that stuck.

1. Your app will break without you touching a single line of code

One day the Re-imagine feature stopped working. No deployment. No code change. Nothing on my end.

The model name had changed upstream. What was gemini-2.5-flash-image-preview became gemini-2.5-flash-image. My API calls were hitting a name that no longer existed.

In traditional software, you pin your dependency versions. If Express 4.18.2 works, you lock it and it works forever. With Gemini model endpoints, there's no equivalent — you're always referencing a name that the provider can change. When they rename it, your production app breaks silently.

I fixed it in minutes once I found the cause. But the lesson is deeper: in AI-powered apps, your dependencies aren't just packages in a lockfile. They're model endpoints that can change names, behavior, or capabilities without warning. If you're building anything meant to last longer than a hackathon, you need monitoring on your AI calls, not just your infrastructure.

2. Where your project lives matters more than you think

When I first built this, AI Studio saved projects to Google Drive. My project was a file in my Drive folder. That worked fine — for me.

Then AI Studio changed. Projects are now saved within AI Studio itself, not Drive. This sounds like a minor infrastructure detail, but it broke something important: sharing.

During the original challenge, many builders had the same problem. Their project was saved to a private Drive folder. They'd share the AI Studio link, but viewers couldn't access it because the underlying Drive file was private. The AI Studio link worked, but the project behind it didn't.

The lesson: platform infrastructure decisions you didn't make can affect your users. When you build on a platform, you inherit not just its features but also its migrations. Keeping an app alive for months means riding through those changes.

3. Gemini thinks faster than it plans

When I first started using Gemini as a development tool — not just as the engine inside my app, but as a coding assistant while building it — I noticed a pattern. It would jump straight into writing code before fully understanding the problem. Ask it to implement a feature and it would produce something immediately, confidently, and sometimes wrong in ways that took longer to debug than writing it from scratch.

Over the past six months, this has noticeably improved. Gemini now takes a beat before responding — thinking through the approach, considering edge cases, then writing the code. It plans before it executes. The difference is real. Earlier interactions felt like pair-programming with someone who typed faster than they thought. Now it feels like working with someone who reads the whole ticket before opening their editor.

The impulse to start coding before understanding the problem is the most expensive habit in software. Watching an AI model learn to break that habit was a strange kind of validation.

4. Prompt engineering is maintenance, not a one-time task

My system prompt has stayed the same since September: "You are a historian and captivating storyteller..." — the same instruction, word for word.

But the model's interpretation of that prompt has subtly shifted as the underlying model improved. The narrations in February 2026 are richer and more nuanced than the narrations from September 2025, even though my prompt hasn't changed. The model is doing more with the same instruction.

This is actually a compliment to Gemini's backward compatibility — my prompt didn't break. But it also means the output of your app changes over time even when your code doesn't. For a history narrator, that's a good thing — better narrations for free. For an app where output consistency matters (medical, legal, financial), this invisible drift could be a problem.

The takeaway: if you're building with AI for production, you need to think about prompt behavior the way you think about database migrations — something that requires monitoring and occasional adjustment, not a set-and-forget configuration.

5. Deploying to Cloud Run was the easy part. Leaving it running was the lesson.

The app has been live on Cloud Run for six months with zero maintenance. No patches, no restarts, no scaling interventions. Cloud Run's scale-to-zero means the app costs essentially nothing when no one is using it, and spins up in seconds when someone visits.

I've shipped side projects on custom VPS setups, on Heroku, on various platforms. Most of them are dead now — killed by expired SSL certificates, crashed processes nobody restarted, or hosting bills I forgot to pay.

This one survived because I chose boring infrastructure. Cloud Run isn't exciting. It doesn't require Kubernetes knowledge or a DevOps pipeline. But it's the reason this app is still answering requests six months later instead of returning a 502 page.

The real lesson: for side projects, pick the deployment that requires the least ongoing attention. The best infrastructure is the kind you forget about.

Google Gemini Feedback

They asked for candor. Here it is.

The Good

Multimodal understanding is genuinely impressive. This isn't image labeling — Gemini infers historical context, era, cultural significance, and narrative arc from a single photograph. I've uploaded obscure family photos from the 1940s Caucasus region and gotten narrations that correctly identified the approximate era, clothing styles, and regional context. That's not pattern matching. That's understanding.

Flash model speed makes AI feel invisible. The narration generates fast enough that users don't feel like they're "waiting for AI." They click, and the story appears. That speed is the difference between a tool people use once and a tool people explore.

AI Studio as a prototyping environment is unmatched for getting to a deployed app quickly. I went from concept to deployed Cloud Run app in a single focused session. The integration between the IDE, the model playground, and the deployment pipeline is seamless.

Image editing quality is creative, not just mechanical. The Re-imagine feature doesn't just apply filters. It generates genuinely new interpretations of scenes. Users asked to "add color to this black-and-white photo" don't get colorization — they get a reimagined scene that feels alive.

The Bad

Model name changes in production are a real problem. gemini-2.5-flash-image-preview becoming gemini-2.5-flash-image broke my app with no warning, no deprecation period, and no migration guide. For hackathon projects, this is a shrug. For anything meant to stay running, it's a trust issue. I'd love to see semantic versioning or at least alias support for model endpoints — let the old name forward to the new one for 90 days.

AI Studio project storage migration caused real confusion. The shift from Google Drive storage to AI Studio-native storage happened without a clear heads-up to builders who had deployed apps. Sharing flows broke. The fix was simple once understood, but discovering it required digging.

The Honest

Running this app for six months has given me a perspective most builders don't get. The short version: Gemini is a strong foundation for building real things. The multimodal capabilities are not a gimmick. The speed is production-ready. The deployment story via Cloud Run is the best I've seen in the AI space.

Looking Forward

Six months with Gemini has made me genuinely excited about where these models are headed. The multimodal understanding is already strong — I'm looking forward to seeing what comes next with longer context windows, better video processing, and the continued improvements to image generation. If the tooling around production stability catches up (version pinning, deprecation notices, changelogs), Gemini becomes a no-brainer foundation for any AI-powered project. I'll be building with it again.

Every photo has a story. Most of them die with the people who were there. Tools like this won't bring those people back, but they can make sure the stories don't disappear with the photographs.

Try it: Historical Photo/Video Narrator

Ever Spent Hours Looking for an Open Source Issue to Contribute To? Those Days Are Over.

Nikoloz Turazashvili (@axrisi) — Sat, 28 Feb 2026 15:30:20 +0000

This is a submission for the DEV Weekend Challenge: Community

The Community

Open source contributors — specifically, developers who want to contribute but don't know where to start.

If you've ever searched GitHub for "good first issue," you know the problem: thousands of results, no way to tell which repos actually welcome newcomers, and no sense of whether an issue labeled "easy" will take you an hour or a week. Many "good first issues" sit in abandoned repos where no one will ever review your PR. Others are mislabeled — they require deep domain knowledge despite the friendly tag.

Tools like Good First Issue, Up For Grabs, and GitHub's own search help surface these issues — but they stop at listing them. They don't tell you whether the repository behind an issue is healthy, actively maintained, or will actually review your pull request. They don't estimate how difficult the work really is beyond whatever label someone applied months ago.

The open source contributor community loses potential members every day to this friction. IssueScout is built to close that gap — not by replacing these tools, but by adding the intelligence layer they're missing.

What I Built

IssueScout is an open source issue discovery platform that helps developers find their ideal first contribution. It enriches GitHub's "good first issue" results with two things no existing tool provides:

1. Community Health Score (0-100) — A 7-factor score computed per repository:

Has CONTRIBUTING.md (15 pts)
Has a license (10 pts)
Has a code of conduct (5 pts)
Recent commit activity within 30/90 days (20 pts)
Star count as a popularity signal (15 pts)
Average issue response time (20 pts)
Merged PR count as a maintenance signal (15 pts)

A repo scoring 80+ is actively maintained, welcomes contributors, and will likely review your PR. A repo scoring 20 might have a "good first issue" label but no one home.

2. AI Difficulty Estimation — Each issue gets a difficulty rating (Easy / Medium / Hard) using a two-tier approach: a fast rule-based analyzer checks keywords, labels, and comment count first. If confidence is below 80%, it falls back to GPT-4o-mini for deeper analysis. A purple sparkle icon indicates when AI was used — full transparency, no black boxes.

What else makes IssueScout different:

Personalized "For You" tab — During onboarding, IssueScout detects your GitHub languages and starred repo topics, then recommends issues matching your stack with a match score
18 label filters across 4 categories — Beginner-friendly, contribution type, issue area, and events (Hacktoberfest). Labels use OR logic so you can combine freely
Bookmarks with archiving — Save issues for later, archive completed ones. Full issue snapshots are preserved even if the original changes on GitHub
Progressive loading — Issues appear instantly from GitHub, health scores and difficulty badges fill in asynchronously as enrichment completes. No loading spinners, no waiting
Per-user API tokens — Each user's GitHub OAuth token powers their searches (5,000 req/hr per user). The app scales linearly with users instead of hitting a shared rate limit wall
Cookie-consented analytics — GA4 only loads after explicit opt-in. Decline and zero tracking scripts touch your browser
SEO-ready — Sitemap, robots.txt, JSON-LD structured data with search action, OG images, web app manifest — all driven by environment variables so self-hosters can point to their own domain
Dark mode — System preference detection with manual toggle

Demo

Live app: issuescout.dev

Sign in with GitHub to start exploring. The app uses your GitHub OAuth token for API requests, so there's no shared rate limit bottleneck.

How it works:

Sign in with GitHub — IssueScout auto-detects your languages and interests from your repos and stars
Search for issues by language, keyword, or browse "For You" recommendations matched to your stack
Filter by 18 labels across 4 categories, sort by newest/most commented/most reactions/best community
Check the health score — green (70+) means the repo actively welcomes contributors
Check the difficulty — find issues that match your experience level, with AI transparency
Click through to the issue detail page for a full community health breakdown, getting-started guide, and repo stats
Bookmark issues you want to tackle later, archive the ones you've completed

Code

The entire codebase is open source under the MIT license:

Repository:
turazashvili/issuescout.dev

Key files:

src/services/healthScore.ts — The 7-factor health scoring algorithm
src/services/difficulty.ts — Two-tier difficulty estimation (rule-based + GPT-4o-mini)
src/services/github.ts — GitHub GraphQL API integration with search query builder
src/models/IndexedRepo.ts — Permanent repo health cache with stale-while-revalidate
src/app/api/issues/enrich/route.ts — Two-level cache enrichment pipeline
src/components/IssueScoutLogo.tsx — Unified brand icon component used across all surfaces
docs/ARCHITECTURE.md — Detailed technical documentation covering every system

How I Built It

Stack: Next.js 16 (App Router) · TypeScript · Tailwind CSS v4 · shadcn/ui · NextAuth v5 · MongoDB Atlas · OpenAI GPT-4o-mini · GitHub GraphQL API · Vercel

Architecture decisions that mattered:

Per-user OAuth tokens instead of a server PAT. GitHub's API gives 5,000 requests/hour per authenticated user. Instead of burning through a single server token, each user's own OAuth token powers their searches. This means the app scales linearly with users — 100 concurrent users = 500K requests/hour of capacity. Tokens are revoked server-side on sign-out.

Two-phase progressive loading. The search API returns raw GitHub results instantly. A separate enrichment endpoint handles health scores and difficulty estimation in the background. Issue cards render immediately with skeleton badges that fill in as enrichment completes. The user never stares at a loading spinner.

Two-level caching. Issue difficulty is cached for 24 hours (L1). Repo health scores are cached permanently in an IndexedRepo collection with stale-while-revalidate at 48 hours (L2). The first user to hit a repo pays the cost; every subsequent user gets instant results. Over time, the IndexedRepo collection builds into a growing database of scored repositories.

Rule-based AI with transparent fallback. Most issues can be classified by keywords and labels alone ("typo" -> Easy, "refactor authentication" -> Hard). GPT-4o-mini only fires when the rule engine isn't confident enough. The purple sparkle on the difficulty badge tells users exactly when AI was involved. This keeps costs minimal and latency low.

Environment-driven configuration. Every deployment-specific value — site URL, NextAuth URL, GA measurement ID — lives in environment variables. Self-hosters can fork the repo, set their own env vars, and have a fully branded instance without changing a single line of code. The cookie consent banner and analytics are opt-in: leave NEXT_PUBLIC_GA_MEASUREMENT_ID unset and no tracking scripts load.

SEO from day one. Dynamic sitemap and robots.txt generated from NEXT_PUBLIC_SITE_URL. JSON-LD structured data with WebSite schema and SearchAction so Google understands the search functionality. Per-page metadata via layout wrappers (since all pages are client components). Edge-rendered OG image with branded design. Web app manifest for PWA-ready installs.

What I learned building this:

GitHub's issue search API doesn't support stars: or forks: qualifiers — those only work on repository search. Had to build difficulty filtering as a post-fetch step with over-fetching and client-side filtering.
Mongoose's { new: true } option is deprecated in v9 — use { returnDocument: "after" }.
NextAuth JWT tokens are static snapshots. Storing mutable state (like onboardingCompleted) in the JWT means it goes stale after the user completes onboarding mid-session. The fix: always verify against the database via an API call.
GitHub GraphQL reserves query as a parameter name, so you can't use it as a variable name in your operations — had to rename to $searchQuery.
MongoDB's language field name conflicts with text indexes (it's reserved for language override). Renamed to programmingLanguage in the search log model.
Radix UI Select with position="item-aligned" (the default) hijacks page scroll — fixed with position="popper" on every SelectContent.

Supported by Vexrail.