Title: Bayesian Variational Quantum Error Mitigation with Adaptive Kernel Selection (BVQ-AKS)

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

Quantum computation promises unprecedented computational power, but inherent quantum errors pose a significant barrier to widespread adoption. Variational Quantum Error Mitigation (VQEM) is a crucial technique for reducing these errors, but its effectiveness is heavily dependent on the specific error profile of the quantum circuit. Traditional VQEM methods often rely on a fixed kernel, failing to adequately address the diverse error characteristics across different quantum systems. This paper proposes a novel Bayesian Variational Quantum Error Mitigation (BVQ-AKS) strategy that dynamically adapts the kernel selection process based on the circuit's error profile. The algorithm learns an optimal kernel for each circuit type, enhancing mitigation effectiveness across a broader range of errors. This approach offers a tailored and adaptive method to address the challenges of VQEM, paving the way for more robust and reliable quantum computations.

DOI: 10.5281/zenodo.22250991

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