Neural Error Mitigation of Near-Term Quantum Simulations

TL;DR

This paper introduces neural error mitigation, a neural network-based method to enhance the accuracy of near-term quantum simulations for finding ground states, improving results across different systems and hardware without extra quantum resources.

Contribution

We propose neural error mitigation as a versatile, hardware-agnostic technique to improve quantum simulation accuracy without additional quantum resources.

Findings

Neural error mitigation reduces energy errors in VQE calculations.

It achieves high fidelities and accurate observable estimations.

The method is effective across different quantum systems and noise channels.

Abstract

Near-term quantum computers provide a promising platform for finding ground states of quantum systems, which is an essential task in physics, chemistry, and materials science. Near-term approaches, however, are constrained by the effects of noise as well as the limited resources of near-term quantum hardware. We introduce "neural error mitigation," which uses neural networks to improve estimates of ground states and ground-state observables obtained using near-term quantum simulations. To demonstrate our method's broad applicability, we employ neural error mitigation to find the ground states of the H$_2$ and LiH molecular Hamiltonians, as well as the lattice Schwinger model, prepared via the variational quantum eigensolver (VQE). Our results show that neural error mitigation improves numerical and experimental VQE computations to yield low energy errors, high fidelities, and accurate estimations of more-complex observables like order parameters and entanglement entropy, without requiring additional quantum resources. Furthermore, neural error mitigation is agnostic with respect to the quantum state preparation algorithm used, the quantum hardware it is implemented on, and the particular noise channel affecting the experiment, contributing to its versatility as a tool for quantum simulation.