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In order to explore the possibility of cross-fertilization between quantumcomputing and neural networks as well as to improve the classificationperformance of quantum neural networks, this paper proposes an improvedVariable Split Shadow Quantum Circuit (VSQC-WOA) model based on the WhaleOptimization Algorithm. In this model, we design a strongly entangled localshadow circuit to achieve efficient characterization of global features throughlocal shadow feature extraction and a sliding mechanism, which provides a richquantum feature representation for the classification task. The gradient isthen computed by the parameter-shifting method, and finally the featuresprocessed by the shadow circuit are passed to the classical fully connectedneural network (FCNN) for processing and classification. The model alsointroduces the Whale Optimization Algorithm (WOA) to further optimize theweights and biases of the fully connected neural network, which improves theexpressive power and classification accuracy of the model. In this paper, wefirstly use different localized shadow circuit VSQC models to achieve thebinary classification task on the MNIST dataset, and our design of stronglyentangled shadow circuits performs the best in terms of classificationaccuracy. The VSQC-WOA model is then used to multi-classify the MNIST dataset(three classifications as an example), and the effectiveness of the proposedVSQC-WOA model as well as the robustness and generalization ability of themodel are verified through various comparison experiments.

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