QOC: Quantum On-Chip Training with Parameter Shift and Gradient Pruning
TimeWednesday, July 13th1:30pm - 1:52pm PDT
Location3000, Level 3
Event Type
Research Manuscript
ML Algorithms and Applications
DescriptionQuantum Neural Network (QNN) is drawing increasingly more research interest thanks to its potential to achieve quantum advantage on NISQ) hardware. Training process needs to be offloaded to real quantum machines instead of using exponential-cost classical simulators. One common approach to obtain QNN gradients is parameter shift whose cost scales linearly with the number of qubits. This work presents the first experimental demonstration of practical on-chip QNN training with parameter shift. Nevertheless, we find that due to the significant quantum errors (noises) on real machines, gradients obtained from naive parameter shift have low fidelity and thus degrade the training accuracy.