The quantum cost function concentration dependency on the parametrization expressivity

TL;DR

This paper investigates how the expressiveness of quantum parametrizations influences the concentration of the cost function, revealing that higher expressiveness leads to greater concentration around a value dependent on the observable and qubits.

Contribution

It analytically connects the expressiveness of quantum parametrizations with the concentration behavior of the cost function, supported by numerical simulations.

Findings

More expressive parametrizations cause the cost function to concentrate more.

The concentration depends on the observable and the number of qubits.

Numerical results confirm the analytical predictions.

Abstract

Although we are currently in the era of noisy intermediate scale quantum devices, several studies are being conducted with the aim of bringing machine learning to the quantum domain. Currently, quantum variational circuits are one of the main strategies used to build such models. However, despite its widespread use, we still do not know what are the minimum resources needed to create a quantum machine learning model. In this article, we analyze how the expressiveness of the parametrization affects the cost function. We analytically show that the more expressive the parametrization is, the more the cost function will tend to concentrate around a value that depends both on the chosen observable and on the number of qubits used. For this, we initially obtain a relationship between the expressiveness of the parametrization and the mean value of the cost function. Afterwards, we relate the expressivity of the parametrization with the variance of the cost function. Finally, we show some numerical simulation results that confirm our theoretical-analytical predictions. To the best of our knowledge, this is the first time that these two important aspects of quantum neural networks are explicitly connected.