Effective Noise Mitigation via Quantum Circuit Learning in Quantum Simulation of Integrable Spin Chains
Wenlong Zhao, Yimeng Zhang, Yan Guo, Yufan Cui, Zhuohang Wang + 1 more
TLDR
This paper introduces Quantum Circuit Learning (QCL) as an effective noise mitigation strategy for quantum simulation of integrable spin chains.
Key contributions
- QCL trains shallow variational circuits to approximate deeper time-evolution circuits.
- Learns conserved charges and minimal dynamical information for integrable spin chains.
- Significantly improves accuracy of conserved quantities and dynamical observables under noise.
- Offers a robust, physics-informed error mitigation without exponential sampling overhead.
Why it matters
This paper offers a practical noise mitigation strategy for near-term quantum devices, a critical challenge for reliable quantum simulation. Its QCL method produces more accurate and robust simulations of integrable spin chains, accelerating the path to useful quantum computers.
Original 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.
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