Towards Training Robustness Against Dynamic Errors in Quantum Machine Learning

Quantum machine learning, crucial in the noisy intermediate-scale quantum (NISQ) era, confronts challenges in error mitigation. Current noise-aware training (NAT) methods often assume static error rates in quantum neural networks (QNNs), overlooking the dynamic nature of quantum noise. Our work high...

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Bibliographic Details
Published in:	2025 62nd ACM/IEEE Design Automation Conference (DAC) pp. 1 - 7
Main Authors:	Duan, Shijin, Liu, Gaowen, Fleming, Charles, Kompella, Ramana, Xu, Xiaolin, Ren, Shaolei
Format:	Conference Proceeding
Language:	English
Published:	IEEE 22.06.2025
Subjects:	Error analysis Machine learning Noise Optimization Prevention and mitigation Qubit Robustness Search methods Training
Online Access:	Get full text
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Summary:	Quantum machine learning, crucial in the noisy intermediate-scale quantum (NISQ) era, confronts challenges in error mitigation. Current noise-aware training (NAT) methods often assume static error rates in quantum neural networks (QNNs), overlooking the dynamic nature of quantum noise. Our work highlights how error rates fluctuate over time and across different qubits, affecting QNN performance even when overall error rates are similar. We introduce a novel NAT strategy that dynamically adjusts to standard and fatal error conditions, incorporating a low-complexity search method to identify fatal errors during optimization. This strategy significantly improves robustness, maintaining competitive performance with leading NAT methods across varying error scenarios.
DOI:	10.1109/DAC63849.2025.11133272