A Neural-Network Variational Quantum Algorithm for Many-Body Dynamics
- URL: http://arxiv.org/abs/2008.13329v3
- Date: Mon, 19 Apr 2021 10:49:26 GMT
- Title: A Neural-Network Variational Quantum Algorithm for Many-Body Dynamics
- Authors: Chee-Kong Lee, Pranay Patil, Shengyu Zhang, Chang-Yu Hsieh
- Abstract summary: We propose a neural-network variational quantum algorithm to simulate the time evolution of quantum many-body systems.
The proposed algorithm can be efficiently implemented in near-term quantum computers with low measurement cost.
- Score: 15.435967947933404
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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