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QuantumNAT: Quantum Noise-Aware Training with Noise Injection, Quantization and Normalization
TimeTuesday, July 12th10:30am - 10:53am PDT
Location3005, Level 3
Event Type
Research Manuscript
Keywords
Quantum Computing
Topics
Design
DescriptionQNN is a promising application towards quantum advantage on near-term quantum hardware. However, due to the large quantum noises (errors), the performance of QNN models has a severe degradation on real quantum devices. Accuracy gap between noise-free simulation and noisy results on IBMQ-Yorktown for MNIST-4 classification is over 60%. Existing noise mitigation methods are general ones without leveraging unique characteristics of QNN and are only applicable to inference; on the other hand, existing QNN work does not consider noise effect. To this end, we present RoQNN, a QNN-specific framework to perform noise-aware optimizations in both training and inference stages.