Machine Learning for Practical Quantum Error Mitigation

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.

11 pages, 7 figures (main text) + 9 pages, 4 figures (supplementary information)

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• Noise-Agnostic Quantum Error Mitigation with Data Augmented Neural Models
• Synergy between noisy quantum computers and scalable classical deep learning
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• Harnessing Quantum Computing for Energy Materials: Opportunities and Challenges
• Sequency Hierarchy Truncation (SeqHT) for Adiabatic State Preparation and Time Evolution in Quantum Simulations
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• Achieving computational gains with quantum error-correction primitives: Generation of long-range entanglement enhanced by error detection
• Circuit-Noise-Resilient Virtual Distillation
• Digital Zero-Noise Extrapolation with Quantum Circuit Unoptimization
• Noise tolerance via reinforcement: Learning a reinforced quantum dynamics
• Artificial intelligence for representing and characterizing quantum systems
• Noisy Monitored Quantum Circuits
• Challenges and opportunities in the supervised learning of quantum circuit outputs
• Physics-Guided Linear Mapper for Quantum Error Mitigation
• Quantum Minimal Learning Machine: A Fidelity-Based Approach to Error Mitigation
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• A Study on Stabilizer Rényi Entropy Estimation using Machine Learning