Classification of open-boundary integrable Yang-Baxter quantum circuits with arbitrary geometries via staggered inhomogeneities, a conjecture on time-periodic integrability, and introduction of ρ-inhomogeneities enabling minimum depth four.
Effective Noise Mitigation via Quantum Circuit Learning in Quantum Simulation of Integrable Spin Chains
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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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2026 1verdicts
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Open-boundary integrable quantum circuits with different geometries
Classification of open-boundary integrable Yang-Baxter quantum circuits with arbitrary geometries via staggered inhomogeneities, a conjecture on time-periodic integrability, and introduction of ρ-inhomogeneities enabling minimum depth four.