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Qrouter
FreeQuantum natural-language retrieval MCP server that ranks text passages by semantic relevance using DisCoCat tensor diagrams compiled to variational circuits.
About
Quantum natural-language retrieval MCP server that ranks text passages by semantic relevance using DisCoCat tensor diagrams compiled to variational circuits.
README
title: qrouter emoji: 🔬 colorFrom: indigo colorTo: purple sdk: docker app_port: 7860 pinned: false license: mit short_description: "QNLP retrieval — DisCoCat + Born-rule overlap"
qrouter
📘 New: Build Your Own MCP Server With Auth + Billing — the 60-page guide ($29) Production stack used to ship this Space + ask-meridian.uk.
Quantum natural-language retrieval for scientific knowledge.
A research artifact: route queries to relevant text by encoding both as quantum states (DisCoCat tensor diagrams compiled to variational circuits) and ranking via Born-rule overlap. Classically simulable now; designed to also run on Quantinuum H-series and (with embedding) Xanadu photonic processors.
Live demo: https://qrouter.ask-meridian.uk
$ curl 'https://qrouter.ask-meridian.uk/rank?q=photons+going+through+barriers&top_k=3'
See docs/deploy.md for the hosting architecture
(systemd + Cloudflare Tunnel on a shared VM) and how to flip the server
between stub and lambeq backends.
Status
Working name. Day-1 scaffold. Not a product. Not stable. Not even opinionated yet.
What this is and is not
Is: an experiment in whether compositional quantum-semantic structure (à la Coecke et al.) gives meaningfully different retrieval behavior than classical dense embeddings — particularly on small corpora where the geometric structure matters more than scale.
Is not: a faster retriever, a better embedder, or anything you should
use in production. Quantum circuit simulation is slower than cosine(a, b)
on classical hardware. The point is whether the structure matters, not
whether it's fast.
Stack
- Python 3.12+
- lambeq — DisCoCat parsing + circuit compilation
- PennyLane — variational quantum circuits + autodiff
- JAX — gradients (lambeq supports this backend)
- pytest, ruff
- uv for env management
First-week plan
- Day 1-2: read Coecke "Mathematical Foundations of QNLP" (2020) + Lorenz et al. "QNLP in Practice" (2023). Run lambeq's MNIST tutorial.
- Day 3-4: 50 arXiv quant-ph abstracts → DisCoCat parses → simulated circuits → pairwise Born-rule overlap → toy retrieval demo.
- Day 5-6: wire to MCP stdio so
qrouteris callable from Claude / Cursor / Windsurf as a tool. - Day 7: decide — go deeper into pure QNLP, or branch toward photonic reservoir front-end.
References
- Coecke, B., de Felice, G., Meichanetzidis, K., Toumi, A. (2020). Foundations for Near-Term Quantum Natural Language Processing.
- Lorenz, R., Pearson, A., Meichanetzidis, K., Kartsaklis, D., Coecke, B. (2023). QNLP in Practice: Running Compositional Models of Meaning on a Quantum Computer. JAIR 76.
- Quantinuum lambeq: https://github.com/CQCL/lambeq
License
MIT (see LICENSE).
How to install
Add this to claude_desktop_config.json and restart Claude Desktop.
{
"mcpServers": {
"qrouter": {
"command": "npx",
"args": []
}
}
}
