QOC: Quantum On-Chip Training with Parameter Shift and Gradient Pruning
This work addresses the scalability problem for quantum machine learning on near-term hardware, offering a practical solution for researchers in quantum computing, though it is incremental in improving existing methods.
The paper tackles the challenge of training Parameterized Quantum Circuits on real quantum machines by introducing QOC, the first experimental demonstration of on-chip training with parameter shift, and proposes probabilistic gradient pruning to mitigate noise-induced errors. The results show over 90% accuracy for 2-class and 60% for 4-class image classification tasks, with pruning improving accuracy by up to 7%.
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.