Mutual Reinforcement between Neural Networks and Quantum Physics

TL;DR

This paper reviews how neural networks and quantum physics mutually enhance each other, showcasing applications in quantum sensing and the development of robust quantum neural networks.

Contribution

It provides a comprehensive overview of the interplay between neural networks and quantum physics, highlighting novel applications and design strategies in quantum machine learning.

Findings

Classical neural networks can design quantum sensing protocols.

Quantum neural networks can achieve short operation times and robustness.

Mutual reinforcement improves quantum machine learning performance.

Abstract

Quantum machine learning emerges from the symbiosis of quantum mechanics and machine learning. In particular, the latter gets displayed in quantum sciences as: (i) the use of classical machine learning as a tool applied to quantum physics problems, (ii) or the use of quantum resources such as superposition, entanglement, or quantum optimization protocols to enhance the performance of classification and regression tasks compare to their classical counterparts. This paper reviews examples in these two scenarios. On the one hand, a classical neural network is applied to design a new quantum sensing protocol. On the other hand, the design of a quantum neural network based on the dynamics of a quantum perceptron with the application of shortcuts to adiabaticity gives rise to a short operation time and robust performance. These examples demonstrate the mutual reinforcement of both neural networks and quantum physics.