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Unbiasing time-dependent Variational Monte Carlo by projected quantum evolution

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arxiv 2305.14294 v3 pith:XO4M5SML submitted 2023-05-23 quant-ph cond-mat.otherphysics.comp-ph

classification quant-phcond-mat.otherphysics.comp-ph
keywords carlocomplexitymontequantumsamplesystemsvariationaldynamics
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We analyze the accuracy and sample complexity of variational Monte Carlo approaches to simulate the dynamics of many-body quantum systems classically. By systematically studying the relevant stochastic estimators, we are able to: (i) prove that the most used scheme, the time-dependent Variational Monte Carlo (tVMC), is affected by a systematic statistical bias or exponential sample complexity when the wave function contains some (possibly approximate) zeros, an important case for fermionic systems and quantum information protocols; (ii) show that a different scheme based on the solution of an optimization problem at each time step is free from such problems; (iii) improve the sample complexity of this latter approach by several orders of magnitude with respect to previous proofs of concept. Finally, we apply our advancements to study the high-entanglement phase in a protocol of non-Clifford unitary dynamics with local random measurements in 2D, first benchmarking on small spin lattices and then extending to large systems.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Nuclear responses with neural-network quantum states

    nucl-th 2025-04 conditional novelty 7.0 of 10

    A new variational Monte Carlo framework using neural-network wave functions and the Lorentz integral transform accurately reproduces deuteron and helium-4 photon absorption cross sections.

  2. Looking elsewhere: improving variational Monte Carlo gradients by importance sampling

    quant-ph 2025-07 conditional novelty 6.0 of 10

    Adaptively tuned overdispersed importance sampling, q_alpha proportional to |psi|^alpha, cuts the Monte Carlo sample count needed to converge neural quantum states, especially for peaked molecular wavefunctions.

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