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International Symposium on Parallel Computing and Distributed Systems (PCDS)

Hybrid Computation both Classical and Quantum Appraoch to Accelerate Quantum Monte Carlo

K. Murashima

Published Dec 11, 2025• 0 citations
Publisher
DOI
10.1109/pcds68697.2025.11415288

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The Z6 scaling of computational effort in quantum Monte Carlo (QMC) results in part from a type of critical slowing down. This insight leads to an attack based on acceleration algorithms which modify the dynamics in Langevin-based simulations without changing the steady-state. Configuration space is more rapidly sampled and steps are inherently more decorrelated. A second approach uses a hybrid Metropolis–Green function Monte Carlo method which factors the core and valence wave function, and treats the former variationally with accurate trial functions, and the latter exactly in the presence of the variationally evoling core. This allows separation of the relevant core and valence time scales, and has sped up quantum simulations by over three orders of magnitude.
Quantum Monte Carlo and quantum simulation are both important tools for understanding quantum many-body systems.As a classical algorithm, quantum Monte Carlo suffers from the sign problem, preventing its application to most fermion systems and real-time dynamics.In this paper, we introduce a novel nonvariational algorithm using quantum simulation as a subroutine to accelerate quantum Monte Carlo by easing the sign problem.The quantum subroutine can be implemented with shallow circuits and, by incorporating error mitigation, can reduce the Monte Carlo variance by several orders of magnitude even when the circuit noise is significant.As such, the proposed quantum algorithm is applicable to near-term noisy quantum hardware.