The effect of the processing and measurement operators on the expressive power of quantum models

TL;DR

This paper investigates how the choice of processing and measurement operators in simple quantum machine learning models affects their expressive power, using analytical and numerical tools to analyze the impact of circuit structure and measurement placement.

Contribution

It introduces new analytical and numerical tools to evaluate how circuit structure and measurement choices influence the expressivity of quantum models.

Findings

Increasing parameterized and entangling gates enhances expressivity.

Measurement qubit location affects learnable functions.

Circuit structure significantly impacts model expressiveness.

Abstract

There is an increasing interest in Quantum Machine Learning (QML) models, how they work and for which applications they could be useful. There have been many different proposals on how classical data can be encoded and what circuit ans\"atze and measurement operators should be used to process the encoded data and measure the output state of an ansatz. The choice of the aforementioned operators plays a determinant role in the expressive power of the QML model. In this work we investigate how certain changes in the circuit structure change this expressivity. We introduce both numerical and analytical tools to explore the effect that these operators have in the overall performance of the QML model. These tools are based on previous work on the teacher-student scheme, the partial Fourier series and the averaged operator size. We focus our analysis on simple QML models with two and three qubits and observe that increasing the number of parameterized and entangling gates leads to a more expressive model for certain circuit structures. Also, on which qubit the measurement is performed affects the type of functions that QML models could learn. This work sketches the determinant role that the processing and measurement operators have on the expressive power of simple quantum circuits.