Scalable quantum measurement error mitigation via conditional independence and transfer learning

TL;DR

This paper introduces a scalable quantum measurement error mitigation technique that uses conditional independence and transfer learning to reduce neural network complexity and training data needs, validated on IBM quantum devices.

Contribution

The paper presents a novel scalable method combining conditional independence and transfer learning for quantum error mitigation, improving efficiency and performance over existing approaches.

Findings

Exponential reduction in neural network size.

Reduced training data requirements.

Effective error mitigation on 7 and 13 qubit IBM devices.

Abstract

Mitigating measurement errors in quantum systems without relying on quantum error correction is of critical importance for the practical development of quantum technology. Deep learning-based quantum measurement error mitigation has exhibited advantages over the linear inversion method due to its capability to correct non-linear noise. However, scalability remains a challenge for both methods. In this study, we propose a scalable quantum measurement error mitigation method that leverages the conditional independence of distant qubits and incorporates transfer learning techniques. By leveraging the conditional independence assumption, we achieve an exponential reduction in the size of neural networks used for error mitigation. This enhancement also offers the benefit of reducing the number of training data needed for the machine learning model to successfully converge. Additionally, incorporating transfer learning provides a constant speedup. We validate the effectiveness of our approach through experiments conducted on IBM quantum devices with 7 and 13 qubits, demonstrating excellent error mitigation performance and highlighting the efficiency of our method.