2026/07/20 by Chun-Yeol You
#quant-ph #physics.comp-ph
Monte-Carlo trajectory (quantum-jump) methods are the practical route to simulating noisy quantum circuits once the exact density-matrix method is precluded by its 4n memory cost. Their bottleneck is estimator variance: resolving one expectation value can demand thousands of trajectories. Recent tensor-network work shows that variance-reduced unravelings -- projector and analog sampling -- sharply cut this variance, but only on CPU matrix-product-state backends, with no path into production tooling. We implement both unravelings on a GPU dense-statevector trajectory engine and validate them against the exact density matrix (ideal-circuit fidelity 1-2.2×10-16; 1/√(N) convergence; all unravelings unbiased to trace distance <0.01). On a single consumer GPU, projector unraveling reaches a target standard error with 20.8× fewer trajectories than Qiskit-Aer's batched_shots_gpu at n=10, a factor that holds at 19--26× across n=8--20. A regime map places analog sampling optimal at weak noise and projector at strong noise, crossing near γt≈0.35. We further report a systems finding: Qiskit-Aer applies noise at the channel level and reconstructs a canonical Kraus decomposition at apply time, discarding any user-supplied unraveling, so variance-reduced unravelings cannot be delivered through its public API. Because Aer's Born-rule collapse machinery already exists, we specify a minimal change that would unlock the technique in production.