| --- |
| title: QRoute |
| colorFrom: purple |
| colorTo: indigo |
| sdk: docker |
| app_port: 7860 |
| pinned: false |
| license: apache-2.0 |
| hardware: cpu-basic |
| tags: |
| - quantum-computing |
| - vqc |
| - mixture-of-experts |
| - pennylane |
| - llm |
| - routing |
| - auto-deploy |
| --- |
| |
| # QRoute - a quantum MoE router for LLMs |
|
|
| This Space demonstrates [QRoute](https://github.com/Quantum-Labor/qroute): a |
| variational quantum circuit (VQC) that routes tokens to experts in a |
| Mixture-of-Experts layer. It is project 3 of 3 in the Quantum Co-Processor program |
| (after QVerify and QAgent). |
|
|
| ## What you can do here |
|
|
| - **Explore expert routing.** Pick an example token (one per cluster); 8 expert |
| "lamps" light up to show which experts the VQC router and the classical baseline |
| each select (top-2), with the routing-probability bars side by side. |
| - **See training and load balance.** Training-loss curves for both routers and an |
| expert-utilization chart with the Gini coefficient (lower = more balanced). |
| - **Read the toy results.** Both routers reach 100% validation accuracy at a |
| comparable parameter count. |
|
|
| ## Design notes |
|
|
| - **Precompute and serve.** Both routers are trained offline by |
| `scripts/precompute_space.py` (the README config) and the per-epoch losses, |
| routing probabilities, utilization, and metrics are baked into |
| `precomputed/toy_results.json`. The Space renders them with matplotlib and does |
| no live training/inference, so torch / pennylane are not in the image. |
| - **Fully open.** No IBM path, no quota, no OAuth. |
| - **Honest scope.** Simulator only, 3 qubits / 8 experts, a toy task. No quantum |
| advantage is claimed; the VQC router is a trainable, integrable module that |
| matches the classical baseline. The full-scale 7-qubit / 128-expert Gemma plan |
| is in the repo's design.md and roadmap.md. |
|
|
| Auto-deployed from the `qroute` repo `main` branch via a GitHub Action |
| (`HfApi.upload_folder`); see `docs/deploy.md`. |
|
|
