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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Veröffentlicht in:2025 62nd ACM/IEEE Design Automation Conference (DAC) S. 1 - 7
Hauptverfasser: Duan, Shijin, Liu, Gaowen, Fleming, Charles, Kompella, Ramana, Xu, Xiaolin, Ren, Shaolei
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 22.06.2025
Schlagworte:
Error analysis
Machine learning
Noise
Optimization
Prevention and mitigation
Qubit
Robustness
Search methods
Training
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Abstract 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.
AbstractList 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.
Author Xu, Xiaolin
Kompella, Ramana
Duan, Shijin
Fleming, Charles
Liu, Gaowen
Ren, Shaolei
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  givenname: Shaolei
  surname: Ren
  fullname: Ren, Shaolei
  email: shaolei@ucr.edu
  organization: University of California,Riverside
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SubjectTerms Error analysis
Machine learning
Noise
Optimization
Prevention and mitigation
Qubit
Robustness
Search methods
Training
Title Towards Training Robustness Against Dynamic Errors in Quantum Machine Learning
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