QSIM-YAMATO — GPU-Accelerated Quantum Circuit Simulator

Challenge

State-vector simulation memory scales as 2^n, so single-node RAM caps practical circuits around 28 qubits. Existing Python frameworks (Qiskit Aer / Cirq) also expose gate-application overhead and host-device transfer costs when switching to GPU backends, while tensor-network contraction strategy still relied on manual choices.

Solution

A hybrid backend dispatches between cuStateVec (state vector ≤30 qubits) and cuTensorNet (sparse circuits >30 qubits). Qiskit 2.x / Cirq IR is lowered through OpenQASM 3, and contraction paths are auto-selected via opt_einsum + cuTensorNet. VQE classical optimizers (SciPy L-BFGS-B and Optuna CMA-ES) are pluggable.

Results

• 26-qubit random circuits: 14–22× faster state-vector updates vs. Qiskit Aer CPU on A100 80GB (internal benchmark, classical simulation only)
• VQE convergence on H2 / LiH cut evaluation count by ~35% with CMA-ES (in simulation)
• Shallow 40-qubit QAOA expectation values estimated via cuTensorNet auto-contraction (classical approximation, not hardware-equivalent)
• Full experiment metadata — circuits, seeds, backend — logged to MLflow for reproducibility