Optimal Tuning of Quantum Generative Adversarial Networks for Multivariate Distribution Loading
Articolo
Data di Pubblicazione:
2022
Citazione:
Optimal Tuning of Quantum Generative Adversarial Networks for Multivariate Distribution Loading / G. Agliardi, E. Prati. - In: QUANTUM REPORTS. - ISSN 2624-960X. - 4:1(2022), pp. 75-105. [10.3390/quantum4010006]
Abstract:
Loading data efficiently from classical memories to quantum computers is a key challenge
of noisy intermediate-scale quantum computers. Such a problem can be addressed through quantum
generative adversarial networks (qGANs), which are noise tolerant and agnostic with respect to
data. 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 training of qGAN reduces, on average, the Kolmogorov–Smirnov statistic of 43–64%
with respect to the state of the art. The ability to reach optima is non-trivially affected by the starting
point of the search algorithm. A gap arises between the optimal and sub-optimal training accuracy.
We also point out that the simultaneous perturbation stochastic approximation (SPSA) optimizer
does not achieve the same accuracy as the Adam optimizer in our conditions, thus calling for new
advancements to support the scaling capability of qGANs.
Tipologia IRIS:
01 - Articolo su periodico
Keywords:
uantum machine learning; quantum generative adversarial networks; multivariate quantum distributions; quantum data loading; quantum data encoding; quantum finance
Elenco autori:
G. Agliardi, E. Prati
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