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Journal Articles Phys.Rev.Res. Year : 2021

Neural-network variational quantum algorithm for simulating 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) 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.

Dates and versions

hal-03229353 , version 1 (18-05-2021)

Identifiers

Cite

Chee Kong Lee, Pranay Patil, Shengyu Zhang, Chang Yu Hsieh. Neural-network variational quantum algorithm for simulating many-body dynamics. Phys.Rev.Res., 2021, 3 (2), pp.023095. ⟨10.1103/PhysRevResearch.3.023095⟩. ⟨hal-03229353⟩
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