Variational optimization of neural-network representations of quantum states has been
successfully applied to solve interacting fermionic problems. Despite rapid developments …

We demonstrate a high-performance vendor-agnostic method for massively parallel solving
of ensembles of ordinary differential equations (ODEs) and stochastic differential equations …

Neural network quantum state (NNQS) has emerged as a promising candidate for quantum
many-body problems, but its practical applications are often hindered by the high cost of …

Variational Monte Carlo (VMC) methods are used to sample classically from distributions
corresponding to quantum states which have an efficient classical description. VMC …

We present a generalized framework to adapt universal quantum state approximators,
enabling them to satisfy rigorous normalization and autoregressive properties. We also …

This article aims to summarize recent and ongoing efforts to simulate continuous-variable
quantum systems using flow-based variational quantum Monte Carlo techniques, focusing …

Abstract We consider the Feynman–Kitaev formalism applied to a spin chain described by
the transverse-field Ising model. This formalism consists of building a Hamiltonian whose …

Neural-network quantum states (NQS) has emerged as a powerful application of quantum-
inspired deep learning for variational Monte Carlo methods, offering a competitive …

Capturing the correlation emerging between constituents of many-body systems accurately
is one of the key challenges for the appropriate description of various systems whose …

Motivated by close analogies between meta-reinforcement learning (Meta-RL) and
variational quantum Monte Carlo with disorder, we propose a learning problem and an …