Year: 2021
Type: article
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) wave function 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 Schrödinger 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.
Source: Physical Review Research
Authors: Chee Kong Lee, Pranay Patil, Shengyu Zhang, Chang Yu Hsieh
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,625
OpenAlex ID: W3082945529