QOC: Quantum On-Chip Training with Parameter Shift and Gradient Pruning

TL;DR

This paper demonstrates practical on-chip training of parameterized quantum circuits using parameter shift and introduces probabilistic gradient pruning to mitigate noise, achieving high accuracy on real quantum hardware.

Contribution

First experimental demonstration of on-chip PQC training with parameter shift and a novel probabilistic gradient pruning method to improve training accuracy under noise.

Findings

Achieved over 90% accuracy on 2-class classification tasks.

Gradient pruning improves PQC accuracy by up to 7%.

On-chip training matches noise-free simulation accuracy with better scalability.

Abstract

Parameterized Quantum Circuits (PQC) are drawing increasing research interest thanks to its potential to achieve quantum advantages on near-term Noisy Intermediate Scale Quantum (NISQ) hardware. In order to achieve scalable PQC learning, the training process needs to be offloaded to real quantum machines instead of using exponential-cost classical simulators. One common approach to obtain PQC gradients is parameter shift whose cost scales linearly with the number of qubits. We present QOC, the first experimental demonstration of practical on-chip PQC 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 degrading the training accuracy. To this end, we further propose probabilistic gradient pruning to firstly identify gradients with potentially large errors and then remove them. Specifically, small gradients have larger relative errors than large ones, thus having a higher probability to be pruned. We perform extensive experiments with the Quantum Neural Network (QNN) benchmarks on 5 classification tasks using 5 real quantum machines. The results demonstrate that our on-chip training achieves over 90% and 60% accuracy for 2-class and 4-class image classification tasks. The probabilistic gradient pruning brings up to 7% PQC accuracy improvements over no pruning. Overall, we successfully obtain similar on-chip training accuracy compared with noise-free simulation but have much better training scalability. The QOC code is available in the TorchQuantum library.