Quantum imaginary time evolution steered by reinforcement learning

TL;DR

This paper introduces a reinforcement learning approach to optimize quantum imaginary time evolution, significantly reducing errors and improving state preparation fidelity on near-term quantum devices.

Contribution

It presents a novel deep reinforcement learning method to steer quantum imaginary time evolution, effectively mitigating algorithmic errors caused by Trotterization.

Findings

Enhanced fidelity in state preparation demonstrated on models.

Validated method through numerical simulations and NMR experiments.

Error cancellation achieved by learned evolution paths.

Abstract

The quantum imaginary time evolution is a powerful algorithm for preparing the ground and thermal states on near-term quantum devices. However, algorithmic errors induced by Trotterization and local approximation severely hinder its performance. Here we propose a deep reinforcement learning-based method to steer the evolution and mitigate these errors. In our scheme, the well-trained agent can find the subtle evolution path where most algorithmic errors cancel out, enhancing the fidelity significantly. We verified the method's validity with the transverse-field Ising model and the Sherrington-Kirkpatrick model. Numerical calculations and experiments on a nuclear magnetic resonance quantum computer illustrate the efficacy. The philosophy of our method, eliminating errors with errors, sheds light on error reduction on near-term quantum devices.