Presentation
Late Breaking Results: Quantum-Aware Model Compression Techniques for Scalable Quantum Machine Learning
DescriptionVariational quantum circuits (VQCs) are promising yet costly to deploy because redundant parameters and deep entangling layers amplify noise and runtime. We present Q-Compression, a post-training pipeline that masks small-magnitude angles, snaps low-sensitivity parameters to a low-bit grid using a curvature proxy, and freezes noise-dominated updates via gradient-variance tracking. Across MNIST, FashionMNIST, and KMNIST (binary 0-vs-rest), Q-Compression preserves accuracy and often slightly improves it while reducing circuit operations and depth. Best settings (e.g., keep ratio 0.75, 16 levels) maintain $>0.997$ accuracy with substantial complexity savings. These reductions yield shallower, uniform circuits that are better suited to NISQ execution and tuning.
Event Type
Work in Progress
TimeMonday, July 275:54pm - 5:55pm PDT
LocationExhibit Hall
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