Effective Noise Mitigation via Quantum Circuit Learning in Quantum Simulation of Integrable Spin Chains
arXiv:2604.27648
The paper introduces a noise‑mitigation approach for near‑term quantum devices that uses Quantum Circuit Learning to train shallow variational circuits to approximate deeper time‑evolution circuits of integrable spin chains, preserving conserved quantities and improving observable accuracy under realistic noise.
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. We demonstrate, on small-scale prototypes, that QCL can act as an effective, physics-informed error mitigation strategy, producing shorter, more robust circuits without exponential sampling overhead.
12 pages, 18 figures