Patch-Based End-to-End Quantum Learning Network for Reduction and Classification of Classical Data

TL;DR

This paper introduces a novel patch-based quantum learning network that reduces classical data size dynamically using a quantum and classical hybrid approach, enabling efficient classification on NISQ devices with fewer parameters.

Contribution

It proposes a dynamic, patch-based quantum data reduction method combined with a classical attention mechanism, improving efficiency and reducing parameters in quantum classification tasks.

Findings

Achieves high classification accuracy on Fashion MNIST.

Uses significantly fewer training parameters than traditional methods.

Demonstrates effective quantum-classical hybrid architecture.

Abstract

In the noisy intermediate scale quantum (NISQ) era, the control over the qubits is limited due to the errors caused by quantum decoherence, crosstalk, and imperfect calibration. Hence, it is necessary to reduce the size of the large-scale classical data, such as images, when they are to be processed by quantum networks. Conventionally input classical data are reduced in the classical domain using classical networks such as autoencoders and, subsequently, analyzed in the quantum domain. These conventional techniques involve training an enormous number of parameters, making them computationally costly. In this paper, a dynamic patch-based quantum domain data reduction network with a classical attention mechanism is proposed to avoid such data reductions, and subsequently coupled with a novel quantum classifier to perform classification tasks. The architecture processes the classical data sequentially in patches and reduces them using a quantum convolutional-inspired reduction network and further enriches them using a self-attention technique, which utilizes a classical mask derived from simple statistical operations on the native classical data, after measurement. The reduced representation is passed through a quantum classifier, which re-encodes it into quantum states, processes them through quantum ansatzes, and finally measures them to predict classes. This training process involves a joint optimization scheme that considers both the reduction and classifier networks, making the reduction operation dynamic. The proposed architecture has been extensively tested on the publicly available Fashion MNIST dataset, and it has excellent classification performance using significantly reduced training parameters.