A Machine Learning-Based Error Mitigation Approach For Reliable Software Development On IBM'S Quantum Computers

TL;DR

This paper introduces Q-LEAR, a machine learning-based error mitigation technique for IBM's quantum computers, improving output accuracy by 25% and addressing limitations of previous methods for reliable quantum software development.

Contribution

The paper presents Q-LEAR, a novel ML-based error mitigation approach with a unique feature set, enhancing noise reduction across various quantum circuits and hardware.

Findings

Q-LEAR achieved a 25% average error mitigation improvement.

Evaluations on eight IBM quantum computers and simulators.

Q-LEAR outperforms existing state-of-the-art ML-based methods.

Abstract

Quantum computers have the potential to outperform classical computers for some complex computational problems. However, current quantum computers (e.g., from IBM and Google) have inherent noise that results in errors in the outputs of quantum software executing on the quantum computers, affecting the reliability of quantum software development. The industry is increasingly interested in machine learning (ML)--based error mitigation techniques, given their scalability and practicality. However, existing ML-based techniques have limitations, such as only targeting specific noise types or specific quantum circuits. This paper proposes a practical ML-based approach, called Q-LEAR, with a novel feature set, to mitigate noise errors in quantum software outputs. We evaluated Q-LEAR on eight quantum computers and their corresponding noisy simulators, all from IBM, and compared Q-LEAR with a state-of-the-art ML-based approach taken as baseline. Results show that, compared to the baseline, Q-LEAR achieved a 25% average improvement in error mitigation on both real quantum computers and simulators. We also discuss the implications and practicality of Q-LEAR, which, we believe, is valuable for practitioners.