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.
Unbiasing time-dependent Variational Monte Carlo by projected quantum evolution
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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.
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Looking elsewhere: improving variational Monte Carlo gradients by importance sampling
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.