A Machine Learning-Based Error Mitigation Approach For Reliable Software Development On IBM'S Quantum Computers
This addresses reliability issues in quantum software development for practitioners, but it is incremental as it builds on existing ML-based techniques with a novel feature set.
The paper tackled the problem of noise-induced errors in quantum software outputs on IBM's quantum computers by proposing Q-LEAR, a machine learning-based error mitigation approach, which achieved a 25% average improvement in error mitigation compared to a state-of-the-art baseline on eight quantum computers and simulators.
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.