A Neural-Network Variational Quantum Algorithm for Many-Body Dynamics

TL;DR

This paper introduces a neural-network variational quantum algorithm utilizing a modified RBM wavefunction to efficiently simulate the dynamics of quantum many-body systems on near-term quantum computers, including open systems.

Contribution

It presents a novel variational algorithm based on RBM wavefunctions that reduces measurement costs and addresses barren plateau issues in quantum dynamics simulations.

Findings

Accurately simulates closed and open quantum many-body dynamics

Requires only one ancilla qubit due to qubit recycling

Effective for near-term quantum hardware

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.