Neural-network variational quantum algorithm for simulating many-body dynamics

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 measuremen...

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Bibliographic Details
Published inPhysical review research Vol. 3; no. 2; p. 023095
Main Authors Lee, Chee Kong, Patil, Pranay, Zhang, Shengyu, Hsieh, Chang Yu
Format Journal Article
LanguageEnglish
Published American Physical Society 05.05.2021
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Summary: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.
ISSN:2643-1564
2643-1564
DOI:10.1103/PhysRevResearch.3.023095