A Neural-Network Variational Quantum Algorithm for Many-Body Dynamics
Abstract
We propose a neural-network variational quantum algorithm to simulate the time evolution of quantum many-body systems. Based on a modified restricted Boltzmann machine (RBM) wavefunction ansatz, the proposed algorithm can be efficiently implemented in near-term quantum computers with low measurement cost. Using a qubit recycling strategy, only one ancilla qubit is required to represent all the hidden spins in an RBM architecture. The variational algorithm is extended to open quantum systems by employing a stochastic Schrodinger equation approach. Numerical simulations of spin-lattice models demonstrate that our algorithm is capable of capturing the dynamics of closed and open quantum many-body systems with high accuracy without suffering from the vanishing gradient (or 'barren plateau') issue for the considered system sizes.
Cite
@article{arxiv.2008.13329,
title = {A Neural-Network Variational Quantum Algorithm for Many-Body Dynamics},
author = {Chee-Kong Lee and Pranay Patil and Shengyu Zhang and Chang-Yu Hsieh},
journal= {arXiv preprint arXiv:2008.13329},
year = {2021}
}