Ideal Parametrisation Estimation for Variational Quantum Circuit Classifiers Using Machine Learning

Abstract

Quantum neural network implementation through variational quantum circuits is getting attention due to their applicability to the existing quantum computer technology and significant model parameters reduction. This paper considers the problem of automatically transforming existing neural networks into corresponding variational quantum circuits. In particular, given a trained neural network, the paper considers estimating the number of ansatz layers so that the corresponding circuit is not over or under-parametrized. This is achieved by constructing a dataset containing many trained neural networks each labelled with the corresponding optimal number of variational quantum layers, obtained through experimentation. The paper further performs dataset augmentation using an LLM model to speed up dataset construction. The paper then selects a set of features based on both the network architecture and the trained parameters, and then trains a feed-forward neural network. The trained network achieves 86.7% accuracy on unseen unaugmented test circuits.