How to enhance quantum generative adversarial learning of noisy information

TL;DR

This paper investigates the convergence issues in quantum generative adversarial learning with noisy quantum states and proposes strategies to improve training speed, facilitating practical quantum machine learning applications.

Contribution

It identifies convergence problems like limit cycles in quantum GAN training and introduces methods to accelerate convergence on noisy quantum hardware.

Findings

Limit cycles can significantly delay convergence.

Proposed strategies improve training speed.

Results support potential quantum advantage in generative tasks.

Abstract

Quantum Machine Learning is where nowadays machine learning meets quantum information science. In order to implement this new paradigm for novel quantum technologies, we still need a much deeper understanding of its underlying mechanisms, before proposing new algorithms to feasibly address real problems. In this context, quantum generative adversarial learning is a promising strategy to use quantum devices for quantum estimation or generative machine learning tasks. However, the convergence behaviours of its training process, which is crucial for its practical implementation on quantum processors, have not been investigated in detail yet. Indeed here we show how different training problems may occur during the optimization process, such as the emergence of limit cycles. The latter may remarkably extend the convergence time in the scenario of mixed quantum states playing a crucial role in the already available noisy intermediate scale quantum devices. Then, we propose new strategies to achieve a faster convergence in any operating regime. Our results pave the way for new experimental demonstrations of such hybrid classical-quantum protocols allowing to evaluate the potential advantages over their classical counterparts.