New quantum neural network designs

TL;DR

This paper introduces a novel quantum neural network design that merges feature mapping and variational circuits into a single parameterized circuit, enhancing performance over traditional methods.

Contribution

The paper proposes a new integrated quantum neural network architecture combining feature map and variational circuit, leading to improved accuracy and convergence.

Findings

Outperforms traditional separated feature map and variational circuit methods

Achieves lower loss and better accuracy

Demonstrates faster convergence on various datasets

Abstract

Quantum computers promise improving machine learning. We investigated the performance of new quantum neural network designs. Quantum neural networks currently employed rely on a feature map to encode the input into a quantum state. This state is then evolved via a parameterized variational circuit. Finally, a measurement is performed and post-processed on a classical computer to extract the prediction of the quantum model. We develop a new technique, where we merge feature map and variational circuit into a single parameterized circuit and post-process the results using a classical neural network. On a variety of real and generated datasets, we show that the new, combined approach outperforms the separated feature map & variational circuit method. We achieve lower loss, better accuracy, and faster convergence.