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Post-Variational Quantum Neural Networks on a Hybrid HPC-QC System
DescriptionWe implement a post-variational quantum neural network on a real HPC-QC system and show the feasibility of fully training this class of algorithms on current Noisy Intermediate-Scale Quantum (NISQ) devices, which are limited by noise, low number of qubits, and scarcity. Post-variational methods are hybrid classical-quantum Machine Learning algorithms that remove the need for quantum circuits evaluations during training, thus making them more suited to the availability constraints of physical quantum devices. We investigate the scalability of the algorithm to a higher number of qubits, larger datasets, and more elaborate models, giving insight for more efficient implementation. Experiments for an image classification task on a cutting-edge HPC-QC system show that post-variational quantum neural networks are fully trainable in reasonable times on a superconducting device. The models trained also show performance at least comparable to a variational approach, with one configuration showing a significant improvement in classification accuracy.