Research of the Variational Shadow Quantum Circuit Based on the Whale Optimization Algorithm in Image Classification

TL;DR

This paper introduces an improved quantum neural network model combining shadow quantum circuits and whale optimization to enhance image classification accuracy on the MNIST dataset.

Contribution

It proposes a novel VSQC-WOA model that integrates local shadow circuits with whale optimization for better quantum feature extraction and neural network training.

Findings

Strongly entangled shadow circuits outperform others in accuracy

The WOA-optimized model improves classification performance

The model demonstrates robustness and generalization on MNIST

Abstract

In order to explore the possibility of cross-fertilization between quantum computing and neural networks as well as to improve the classification performance of quantum neural networks, this paper proposes an improved Variable Split Shadow Quantum Circuit (VSQC-WOA) model based on the Whale Optimization Algorithm. In this model, we design a strongly entangled local shadow circuit to achieve efficient characterization of global features through local shadow feature extraction and a sliding mechanism, which provides a rich quantum feature representation for the classification task. The gradient is then computed by the parameter-shifting method, and finally the features processed by the shadow circuit are passed to the classical fully connected neural network (FCNN) for processing and classification. The model also introduces the Whale Optimization Algorithm (WOA) to further optimize the weights and biases of the fully connected neural network, which improves the expressive power and classification accuracy of the model. In this paper, we firstly use different localized shadow circuit VSQC models to achieve the binary classification task on the MNIST dataset, and our design of strongly entangled shadow circuits performs the best in terms of classification accuracy. The VSQC-WOA model is then used to multi-classify the MNIST dataset (three classifications as an example), and the effectiveness of the proposed VSQC-WOA model as well as the robustness and generalization ability of the model are verified through various comparison experiments.