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Unbiasing time-dependent Variational Monte Carlo by projected quantum evolution

Alessandro Sinibaldi, Clemens Giuliani, Giuseppe Carleo, Filippo Vicentini

2023Quantum45 citationsDOIOpen Access PDF

Abstract

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.

Topics & Concepts

Quantum Monte CarloMonte Carlo methodEstimatorStatistical physicsQuantum entanglementMonte Carlo integrationQuantumMonte Carlo method in statistical physicsApplied mathematicsComputer scienceMathematicsMathematical optimizationMonte Carlo molecular modelingHybrid Monte CarloMarkov chain Monte CarloPhysicsQuantum mechanicsStatisticsQuantum many-body systemsTheoretical and Computational PhysicsQuantum Computing Algorithms and Architecture