Effective Noise Mitigation via Quantum Circuit Learning in Quantum Simulation of Integrable Spin Chains

TL;DR

This paper introduces a noise-mitigation method using Quantum Circuit Learning for simulating integrable spin chains on near-term quantum devices, improving accuracy and robustness.

Contribution

It presents a physics-informed error mitigation approach that trains shallow circuits to approximate deep evolutions, preserving conserved quantities under noise.

Findings

Learned circuits maintain conserved quantities closer to true values.

QCL reduces errors in dynamical observables under realistic noise models.

Method produces shorter, more robust circuits without exponential overhead.

Abstract

We propose a noise-mitigation quantum simulation strategy for near-term quantum devices based on Quantum Circuit Learning (QCL), which is in particular effective for integrable quantum spin chains. The method trains a shallow variational circuit to approximate a deeper time-evolution circuit by learning the conserved charges and only a small amount of dynamical information in the system. Under realistic noise models, the learned circuit maintains both conserved quantities and dynamical observables significantly closer to their true values than the noisy simulation of the original circuit. This demonstrates QCL as an effective, physics-informed error mitigation strategy, producing shorter, more robust circuits without exponential sampling overhead.