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quantuminspire

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Quantum Vibecoding

0 stars TypeScriptOthers Updated May 27, 2026

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)

StudyBackendsKey result
Bell State CalibrationEmulator, IBM Marrakesh, IBM Torino, Tuna-9100% / 99.05% / varies fidelity
GHZ State (3q)Emulator, IBM Marrakesh, IBM Torino, Tuna-9100% / 98.14% fidelity
H2 VQE (2q)Emulator, IBM Marrakesh, IBM Torino, Tuna-9-1.1385 Ha emulator (chemical accuracy)
QRNG CertificationTuna-9 raw + debiased, EmulatorRaw fails NIST; debiased passes all
Randomized BenchmarkingEmulator99.95% gate fidelity
QAOA MaxCutEmulator87% approximation ratio
Quantum VolumeEmulator + Tuna-9QV 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)

PaperClaims testedPass rate
Sagastizabal 2019 (H2 VQE)743% (emulator pass, hardware fail)
Peruzzo 2014 (HeH+ VQE)3100% (emulator)
Cross 2019 (Quantum Volume)3100% (emulator)

Hardware access

BackendQubitsAccess
QI Emulator (qxelarator)ConfigurableLocal, no auth needed
QI Tuna-99 (6 usable)QI member 2108
IBM Marrakesh156IBM Quantum (free tier, 10 min/month)
IBM Torino133IBM Quantum
IBM Fez156IBM Quantum

Quick start

bash
# 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/               # Python

Architecture

Website (Next.js 14 + Tailwind + Three.js)

RouteDescription
`/`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/`)

AgentPurpose
`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/`)

ServerPurpose
`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:

json
{
  "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)

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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