Optimized Quantum Generative Adversarial Networks for Distribution Loading

Loading data efficiently from classical memories to quantum computers is a key challenge in the current era of quantum computing. Approximate techniques, including Generative Adversarial Networks (qGANs), were proposed in literature to reduce the depth of data loading circuits. Tuning a qGAN to balance accuracy and training time is a hard task, that becomes paramount when target distributions are multivariate. Thanks to our tuning of the hyper-parameters and of the optimizer, the Kolmogorov-Smirnov statistic was reduced of 43 - 64% with respect to the state of the art. The ability to reach optima is nontrivially affected by the starting point of the search algorithm. It also becomes manifest, after our testing campaign, that a gap arises between the training accuracies achieved by nearly-optimal and non-optimal runs. We finally point out that the Simultaneous Perturbation Stochastic Approximation (SPSA) optimizer does not provide the same accuracy as Adam AMSGRAD in our conditions, therefore calling for new advancements to support scaling capability of qGANs.