quantuminspire
Quantum Vibecoding
Documentation
AI x Quantum — TU Delft / Quantum Inspire
How might generative AI accelerate quantum computing?
An open research initiative exploring AI-driven quantum computing research. We build autonomous agents that run quantum experiments across multiple hardware backends, replicate published papers, and benchmark LLM capabilities on quantum tasks.
Live site: https://quantuminspire.vercel.app
Current results
Experiments (22 results across 7 study types)
| Study | Backends | Key result |
|---|---|---|
| Bell State Calibration | Emulator, IBM Marrakesh, IBM Torino, Tuna-9 | 100% / 99.05% / varies fidelity |
| GHZ State (3q) | Emulator, IBM Marrakesh, IBM Torino, Tuna-9 | 100% / 98.14% fidelity |
| H2 VQE (2q) | Emulator, IBM Marrakesh, IBM Torino, Tuna-9 | -1.1385 Ha emulator (chemical accuracy) |
| QRNG Certification | Tuna-9 raw + debiased, Emulator | Raw fails NIST; debiased passes all |
| Randomized Benchmarking | Emulator | 99.95% gate fidelity |
| QAOA MaxCut | Emulator | 87% approximation ratio |
| Quantum Volume | Emulator + Tuna-9 | QV 16 (4q pass, 8/10 circuits) |
Additional hardware experiments: connectivity probe (Tuna-9 topology), repetition code (3q QEC), detection code (emulator).
Paper replications (3 papers, 13 claims)
| Paper | Claims tested | Pass rate |
|---|---|---|
| Sagastizabal 2019 (H2 VQE) | 7 | 43% (emulator pass, hardware fail) |
| Peruzzo 2014 (HeH+ VQE) | 3 | 100% (emulator) |
| Cross 2019 (Quantum Volume) | 3 | 100% (emulator) |
Hardware access
| Backend | Qubits | Access |
|---|---|---|
| QI Emulator (qxelarator) | Configurable | Local, no auth needed |
| QI Tuna-9 | 9 (6 usable) | QI member 2108 |
| IBM Marrakesh | 156 | IBM Quantum (free tier, 10 min/month) |
| IBM Torino | 133 | IBM Quantum |
| IBM Fez | 156 | IBM Quantum |
Quick start
# 1. Website
npm install
npm run dev
# Deploy
vercel --prod
# 2. Python environment (Python 3.9-3.13 supported — 3.14 breaks qxelarator)
python3 -m venv .venv
source .venv/bin/activate
pip install -r mcp-servers/requirements.txt
# 3. MCP servers for Claude Code (optional — starts automatically)
# The .mcp.json in this repo configures three quantum MCP servers.
# They use .venv/bin/python, so step 2 must be done first.
# Auth setup (needed for hardware, not for emulator):
# - Quantum Inspire: qi login
# - IBM Quantum: python -c "from qiskit_ibm_runtime import QiskitRuntimeService; QiskitRuntimeService.save_account(channel='ibm_cloud', token='YOUR_TOKEN')"
# 4. Run experiments
source .venv/bin/activate
python scripts/benchmark_harness.py --limit 10
python scripts/replications/replicate_sagastizabal.py
python scripts/replications/replicate_peruzzo.py
python agents/experiment_daemon.py
# 5. Tests
npm test # JS/TS (Vitest)
python -m pytest tests/ # PythonArchitecture
Website (Next.js 14 + Tailwind + Three.js)
| Route | Description |
|---|---|
| `/` | Research home — hero, experiments overview, agent architecture |
| `/experiments` | Experiment dashboard — grouped by type, backend badges |
| `/experiments/[id]` | Study detail — abstract, research question, results, visualizations |
| `/replications` | Paper replication dashboard — claims vs measured, cross-backend |
| `/blog` | Research blog (7 posts) |
| `/learn` | Interactive quantum learning page |
| `/bloch-sphere`, `/state-vector`, etc. | Interactive quantum visualizations |
Agents (`agents/`)
| Agent | Purpose |
|---|---|
| `orchestrator.py` | Pipeline coordinator |
| `experiment_daemon.py` | Queue -> submit -> analyze -> store results |
| `benchmark_agent.py` | LLM benchmark runner |
| `replication_agent.py` | Paper registry + run/analyze replications |
| `replication_analyzer.py` | Compare results vs published claims |
| `qec_decoder.py` | Quantum error correction decoder |
MCP servers (`mcp-servers/`)
| Server | Purpose |
|---|---|
| `qi-circuits` | Submit/check circuits on Quantum Inspire hardware |
| `qrng` | Quantum random number generation |
| `ibm-quantum` | IBM Quantum hardware access |
Experiment result JSON schema (v1.0)
All result files in `experiments/results/` follow this schema:
{
"schema_version": "1.0",
"id": "bell-calibration-001-ibm",
"type": "bell_calibration",
"backend": "ibm_marrakesh",
"backend_qubits": 156,
"job_id": "d65kqpoqbmes739d1k2g",
"submitted": "2026-02-10T15:24:38Z",
"completed": "2026-02-10T15:24:38Z",
"parameters": { "shots": 4096 },
"raw_counts": { ... },
"analysis": { ... },
"circuit_cqasm": "version 3.0\n...",
"errors": null
}- `schema_version`: always "1.0"
- `backend_qubits`: qubit count of backend (null for emulators)
- `job_id`: hardware job ID (null for emulator/local runs)
Stack
- Quantum: Qiskit 2.1, PennyLane 0.44, QI SDK 3.5.1, OpenFermion, PySCF
- AI: Claude, Gemini, GPT (via respective APIs)
- Web: Next.js 14, Tailwind, Three.js
- Hardware: Quantum Inspire Tuna-9 (9q), IBM Marrakesh (156q), IBM Torino (133q), IBM Fez (156q)
- Python: 3.9-3.13 (3.14 breaks qxelarator)
Links
Frequently asked questions
What is quantuminspire?
quantuminspire is Quantum Vibecoding
How do I install quantuminspire?
Open the GitHub repository and follow its README. Most MCP servers are added to your client's MCP config, then called by your agent.
Is quantuminspire open source?
Yes — it is hosted on GitHub at https://github.com/JDerekLomas/quantuminspire.
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