Speaker
Description
Pauli-based simulation offers a promising route to large-scale classical simulation of quantum circuits in structured regimes, but existing truncation-based Pauli simulators are mainly designed for forward expectation estimation. When embedded in gradient-driven quantum optimization, these truncations can produce systematically biased gradients and distorted optimization trajectories. We propose the stochastic Pauli-path simulator (\texttt{SPPS}), a Pauli-propagation framework for large-scale quantum optimization. \texttt{SPPS} samples from the full Pauli-path space, uses importance reweighting to obtain unbiased estimates, and applies path automatic differentiation to estimate all gradient components from sampled paths. We prove that truncation can approximate objective values while retaining non-vanishing gradient error, whereas \texttt{SPPS} gives unbiased gradients with high-probability accuracy and convergence guarantees. Experiments on VQE pre-training up to 100 qubits and QNN pre-training up to 40 qubits show faithful optimization dynamics and improved accuracy-runtime trade-offs.
| Topic | Hybrid quantum-classical approaches for learning and optimization |
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