A practical overview of image classification with variational tensor-network quantum circuits

TL;DR

This paper provides a comprehensive overview of tensor-network quantum circuits, detailing their design, simulation, and application to image classification tasks in quantum machine learning.

Contribution

It introduces a practical method for designing tensor-network based variational quantum circuits and demonstrates their application to complex image processing tasks.

Findings

Tensor-network quantum circuits can be effectively simulated using PennyLane.

These circuits are applicable to industrially-relevant image classification tasks.

Circuit cutting enables evaluation of larger circuits than current hardware allows.

Abstract

Circuit design for quantum machine learning remains a formidable challenge. Inspired by the applications of tensor networks across different fields and their novel presence in the classical machine learning context, one proposed method to design variational circuits is to base the circuit architecture on tensor networks. Here, we comprehensively describe tensor-network quantum circuits and how to implement them in simulations. This includes leveraging circuit cutting, a technique used to evaluate circuits with more qubits than those available on current quantum devices. We then illustrate the computational requirements and possible applications by simulating various tensor-network quantum circuits with PennyLane, an open-source python library for differential programming of quantum computers. Finally, we demonstrate how to apply these circuits to increasingly complex image processing tasks, completing this overview of a flexible method to design circuits that can be applied to industrially-relevant machine learning tasks.