Machine Learning for Practical Quantum Error Mitigation (2309.17368v2)

Abstract: Quantum computers progress toward outperforming classical supercomputers, but quantum errors remain their primary obstacle. The key to overcoming errors on near-term devices has emerged through the field of quantum error mitigation, enabling improved accuracy at the cost of additional run time. Here, through experiments on state-of-the-art quantum computers using up to 100 qubits, we demonstrate that without sacrificing accuracy machine learning for quantum error mitigation (ML-QEM) drastically reduces the cost of mitigation. We benchmark ML-QEM using a variety of machine learning models -- linear regression, random forests, multi-layer perceptrons, and graph neural networks -- on diverse classes of quantum circuits, over increasingly complex device-noise profiles, under interpolation and extrapolation, and in both numerics and experiments. These tests employ the popular digital zero-noise extrapolation method as an added reference. Finally, we propose a path toward scalable mitigation by using ML-QEM to mimic traditional mitigation methods with superior runtime efficiency. Our results show that classical machine learning can extend the reach and practicality of quantum error mitigation by reducing its overheads and highlight its broader potential for practical quantum computations.

• The paper demonstrates that ML-driven quantum error mitigation can reduce runtime overhead by at least twofold compared to traditional ZNE methods.
• It benchmarks various ML models including random forests, MLPs, and GNNs across simulations and experiments on circuits with up to 100 qubits.
• The results reveal that ML-QEM techniques scale effectively and maintain or improve output fidelity, paving the way for practical near-term quantum computation.

Analyzing "Machine Learning for Practical Quantum Error Mitigation"

The paper, "Machine Learning for Practical Quantum Error Mitigation," addresses a significant challenge within the domain of quantum computing: mitigating quantum errors that hinder the effective use of quantum processors. The research outlines the utilization of classical machine learning techniques to tackle quantum error mitigation (QEM), an essential process for refining the output fidelity of near-term quantum devices without incurring the prohibitive overheads typically associated with quantum error correction (QEC).

Achievements and Methodologies

The authors embark on a comprehensive analysis, utilizing ML to enhance the QEM process. They demonstrate, through a series of simulations and practical experiments on state-of-the-art quantum computers, that ML-based QEM (ML-QEM) can efficiently reduce overheads while maintaining or improving accuracy compared to conventional QEM methods. One of the notable aspects is the benchmarking of several ML models, including linear regression, random forests, multi-layer perceptrons (MLP), and graph neural networks (GNN), across various quantum circuits and noise profiles.

The authors investigate the application of these models using random circuits and Trotterized Ising model circuits. The experiments are benchmarked against popular digital zero-noise extrapolation (ZNE) methods, providing a comparative analysis of ML models versus traditional approaches. The paper's experiments employ both simulated and physical quantum processors, including circuits utilizing up to 100 qubits.

Key Numerical Results

Several impressive numerical results are highlighted:

• Runtime Efficiency: The research finds that ML-QEM methods, particularly using the random forests model, provide results that are competitive with ZNE methods while requiring at least a twofold reduction in runtime overhead.
• Scalability: The research outlines strategies for scaling ML-QEM to quantum circuits of high complexity, effectively mimicking the outcomes of traditional mitigation techniques with significantly less overhead.
• Accuracy: In different contexts, including varying quantum circuits and noise profiles, ML-QEM maintained or improved the accuracy of QEM results.

Implications and Future Outlook

The implications of this work are both theoretical and practical. Theoretically, it contributes to the understanding of how classical computing paradigms like machine learning can be adapted for quantum computational challenges. Practically, it suggests a pathway towards making quantum computing more viable in the near term by reducing the computational resources needed for error mitigation, a crucial barrier to achieving quantum advantage.

For future developments, this research indicates the potential for further integration of ML techniques in quantum computing, beyond error mitigation. The ability of ML to learn and adapt potentially complex noise patterns presents an interesting angle for advancements in quantum algorithms and hardware design.

Moreover, while the paper demonstrates a significant application of ML to quantum hardware, future work might focus on optimizing ML model selection and feature engineering specific to quantum processes, which could enhance performance even further. Investigating the impact of noise drift on model performance and developing strategies for real-time adaption of models to changing noise conditions would represent a logical progression of this research.

In conclusion, this work presents a compelling case for leveraging classical machine learning to resolve outstanding challenges in quantum computing, presenting a vision of practical quantum computation that is supported by robust, efficient error mitigation techniques.