[Innovative Application] NISQ Hardware Benchmarking Suite#19
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Adds contest2/OriginQCup_QuantumGap/, a small dependency-light suite of textbook hardware benchmarks for pyqpanda3 backends: - bell_benchmark : 2q entanglement fidelity proxy - ghz_benchmark : n-q GHZ population fidelity proxy - quantum_volume_probe : single-trial heavy-output frequency (Cross 2019) - mirror_circuit_probe : forward-then-inverse survival (Proctor 2022) Backend-agnostic by construction: every benchmark takes a runner(prog, shots) -> dict[str, int] callable, and the suite ships adapters for local CPUQVM and the Origin QCloud (WK_C180 family, full_amplitude / partial / single simulators). pyqpanda-algorithm currently ships strong application algorithms but no hardware-characterisation tooling - this PR closes that gap so users can pick between Wukong backends before committing QPU time. All four benchmarks hit their noiseless targets on a fresh CPUQVM: Bell 1.000, GHZ 1.000, Mirror 1.000, QV heavy-output 0.962 vs ideal 0.962 (pass threshold 2/3). Bell has been exercised end-to-end against the Origin full_amplitude cloud backend - task ID 2316D46C0EBBFD5C384896D0028A44C3 returned the expected [0.5, 0, 0, 0.5] amplitude vector. Submitted for the 2026 CCF Quantum Computing Programming Challenge 'OriginQ Cup' Open-Source Innovation Track by team Quantum Gap.
Runs all four benchmarks against the real Origin Wukong WK_C180 quantum processor (1024 shots each, mapping + optimisation + amend enabled) and records the task IDs in the README: bell FB6423D57AEF322DEB08FAF167DDD320 -> 0.7344 ghz(n=3) 669F8E938B280B24727D5C5385DFCEC1 -> 0.8828 mirror 4F4AE9A54111037B9C13081A8F6D0863 -> 0.5947 qv(n=3) 3C3E2DE3336878EBE061C9C73B18D973 -> 0.8242 The QV probe passes the 2/3 heavy-output threshold on width 3. The reproducible runner is at experiments/run_on_wukong.py and the raw JSON record is at experiments/wukong_results.json.
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Adds contest2/OriginQCup_QuantumGap/, a small dependency-light suite of textbook hardware benchmarks for pyqpanda3 backends:
Backend-agnostic by construction: every benchmark takes a runner(prog, shots) -> dict[str, int] callable, and the suite ships adapters for local CPUQVM and the Origin QCloud (WK_C180 family, full_amplitude / partial / single simulators).
pyqpanda-algorithm currently ships strong application algorithms but no hardware-characterisation tooling - this PR closes that gap so users can pick between Wukong backends before committing QPU time.
All four benchmarks hit their noiseless targets on a fresh CPUQVM: Bell 1.000, GHZ 1.000, Mirror 1.000, QV heavy-output 0.962 vs ideal 0.962 (pass threshold 2/3). Bell has been exercised end-to-end against the Origin full_amplitude cloud backend - task ID 2316D46C0EBBFD5C384896D0028A44C3 returned the expected [0.5, 0, 0, 0.5] amplitude vector.
Submitted for the 2026 CCF Quantum Computing Programming Challenge 'OriginQ Cup' Open-Source Innovation Track by team Quantum Gap.