v0.1.0

First tagged release. The repo is an honest stage-1 skeleton — exact classical
ground truth, CUDA-Q target selection, and an AI findings narrator are in place;
the hand-written CUDA kernel, the QAE circuit, and the first measured benchmark
run are roadmap Phase 1 (see docs/roadmap.md).

Added

• Exact classical ground truth — classical/ramanujan_series.py: the 1914 1/π series as exact SymPy rationals (not floats), the reference the future CUDA kernel will be benchmarked against.
• CUDA-Q target selection — quantum/backend.py: select_target() walks nvidia-mgpu → nvidia → tensornet → qpp-cpu, degrades gracefully on hosts without cudaq, and doubles as an environment diagnostic (python -m quantum.backend). GPU targets are skipped up front via cudaq.num_available_gpus() because cudaq.set_target() hard-aborts (not raises) on a driverless host — this is what lets CI run the integration suite on qpp-cpu. nvidia-mgpu is gated on 2+ visible GPUs and uses the modern cudaq.set_target("nvidia", option="mgpu,fp32") call; the single-GPU MPI-plugin failure is a normal catchable RuntimeError (the multi-GPU success path is untested — no such hardware). Verified 2026-07-10 on WSL2: cudaq 0.15 selects nvidia on the RTX 5070.
• NIM/Nemotron findings narrator (ADR 003) — analysis/narrator.py sends benchmark run files to a Nemotron model via the NVIDIA NIM chat-completions API and returns a findings draft or, given a research question, a structured Observation / Interpretation / Suggested-next-experiment answer. Every number comes from the run file, never the model. Optional: no NVIDIA_API_KEY, no narrator, nothing else breaks. main.py's status check reports its availability.
• Benchmark run-file schema — data/sample_run.json (clearly-labeled synthetic demo data) defines the schema, including the hardware field that lets consumer-RTX and datacenter-H100 results land in one analysis.
• Community benchmark submission template — .github/ISSUE_TEMPLATE/benchmark_submission.yml (GPU/VRAM, driver/CUDA version, CUDA-Q backend, environment, qubit ceiling, run file, notes).
• Documentation — docs/roadmap.md (single vision + evidence-sequenced roadmap, absorbing the former Blueprint v1.0), docs/nvidia-access.md, docs/onboarding.md, docs/setup.md, docs/adr/README.md, and a Test Coverage section in CONTRIBUTING.md — all built from commands actually run on this machine, not assumed behavior. ADRs 001 (CUDA-Q over PennyLane/Qiskit), 002 (WSL2 runtime), 003 (hybrid cloud + NIM).
• Tests — unit (series exact-value and convergence checks; backend fall-through against a fake cudaq module; narrator mocked at the httpx boundary, verifying run-file numbers travel verbatim) + integration (a real Bell-pair simulation on the selected CUDA-Q target; a live NIM smoke test) — each skips cleanly where its backend is absent. ~100% measured coverage; CI gates at 95% (the only exclusion is the JIT-compiled kernel body, exercised by an integration test).
• Repo hygiene to sibling-repo standard (continuum, bankers-wrapped): Makefile (source of truth for commands), pyproject.toml (ruff / pytest / coverage config), CI quality gate (ruff + pytest on cudaq's qpp-cpu target + Codecov), tag-driven release workflow, CLAUDE.md, LICENSE (MIT), CONTRIBUTING.md, SECURITY.md, and secret handling (.env gitignored, .env.example documents NVIDIA_API_KEY / NIM_BASE_URL / NIM_MODEL).

Removed

• Legacy PennyLane scaffold (scripts/quantum_engine.py, scripts/math_logic.py, scripts/gpu_check.py, PennyLane deps) — superseded by the CUDA-Q structure; maintaining code slated for deletion was wasted effort (ADR 001).
• Stale planning docs (docs/gpu_setup.md, docs/qrm.md, docs/ramanujan-cuda-quantum.md, docs/ramanujan-cuda-quantum-summary.md) — QRM/VQE and Qiskit-era brainstorms superseded by the roadmap; the decisions they recorded live on as ADRs.