Quantum neural networks to simulate many-body quantum systems

TL;DR

This paper demonstrates how hybrid classical-quantum algorithms, utilizing neural networks and quantum samplers, can effectively simulate complex many-body quantum systems, showcasing early quantum computer capabilities.

Contribution

It introduces a novel hybrid approach combining neural networks and quantum sampling to simulate many-body quantum systems, specifically the transverse field quantum Ising model.

Findings

Quantum computers can be used to find ground states of complex quantum systems.

Neural network wave functions can be trained with quantum-assisted Monte Carlo.

First-generation quantum devices are capable of addressing non-trivial physics problems.

Abstract

We conduct experimental simulations of many body quantum systems using a \emph{hybrid} classical-quantum algorithm. In our setup, the wave function of the transverse field quantum Ising model is represented by a restricted Boltzmann machine. This neural network is then trained using variational Monte Carlo assisted by a D-Wave quantum sampler to find the ground state energy. Our results clearly demonstrate that already the first generation of quantum computers can be harnessed to tackle non-trivial problems concerning physics of many body quantum systems.