| --- |
| title: QAgent |
| colorFrom: purple |
| colorTo: indigo |
| sdk: docker |
| app_port: 7860 |
| pinned: false |
| license: apache-2.0 |
| hardware: cpu-basic |
| tags: |
| - quantum-computing |
| - qaoa |
| - pennylane |
| - llm-agents |
| - tool-selection |
| - auto-deploy |
| --- |
| |
| # QAgent - quantum tool selection for LLM agents |
|
|
| This Space demonstrates [QAgent](https://github.com/Quantum-Labor/qagent): picking |
| the best subset of `k` tools from `N` candidates for an agent task, solved with the |
| Quantum Approximate Optimization Algorithm (QAOA). It is project 2 of 3 in the |
| Quantum Co-Processor program (after QVerify, alongside QRoute). |
|
|
| ## What you can do here |
|
|
| - **Explore the qagent-mini-50 tasks.** Pick any of the 50 benchmark tasks; the |
| 4x4 (or 4x2) tool grid highlights which tools the brute-force optimum, QAOA, and |
| greedy each select, with score cards and approximation ratios. |
| - **See the score landscape.** A chart plots the scores of every size-`k` subset |
| with markers showing exactly where QAOA, greedy, and the optimum fall - so you |
| can see how close QAOA gets and how far greedy misses. |
| - **Build an exploration history.** As you browse tasks, the session tracks the |
| QAOA vs greedy approximation ratio. |
| - **Verify live.** A button runs the pure-Python brute-force and greedy solvers |
| live (no precomputed lookup) and confirms they match the served numbers. |
| - **Read the leaderboard.** The qagent-mini-50 summary: exact-match and mean |
| approximation ratio for brute-force, greedy, and QAOA. |
|
|
| ## Design notes |
|
|
| - **Precomputed QAOA.** QAOA on 16 qubits is slow on CPU, so the QAOA results are |
| precomputed with the documented v0.2 config (p=4, 160 steps, 1024 shots, seed 0, |
| x mixer) and served from JSON for an instant experience. The classical solvers |
| run live (they are pure Python and finish in milliseconds), which keeps the |
| served numbers honest and the Docker image small (no torch / pennylane). |
| - **Fully open.** There is no IBM hardware path and no quota to protect, so the |
| Space has no OAuth gating - anyone can use every feature. |
| - **Honest scope.** Simulator only; no quantum advantage is claimed. QAOA matches |
| the optimum on every N=8 task and reaches a 0.915 mean approximation ratio at |
| N=16, but exact-match at N=16 remains hard (the next step is Dicke-initialised |
| XY-QAOA). Full benchmarks and source: the GitHub repo. |
|
|
| Auto-deployed from the `qagent` repository `main` branch via a GitHub Action |
| (`HfApi.upload_folder`); see `docs/deploy.md` in the repo. |
|
|
