Quantum-Inspired Neural Networks: A Hybrid Approach To Enhance Computational Efficiency

Abstract

Inspired by the success of classical neural networks, there has been tremendous effort to develop classical effective neural networks into quantum concept. In this paper, a novel hybrid quantum– classical neural network with deep residual learning (Res-HQCNN) is proposed. We firstly analyse how to connect residual block structure with a quantum neural network, and give the corresponding training algorithm. At the same time, the advantages and disadvantages of transforming deep residual learning into quantum concept are provided. This research proposes a hybrid computational approach aimed at enhancing efficiency through the integration of classical and quantum-inspired techniques. The core concept involves the utilization of quantum-inspired natural networks to optimize computational processes. By amalgamating classical computing with quantum-inspired algorithms, we seek to exploit the benefits of quantum-inspired features for improved computational efficiency. The abstract outlines the motivation, methodology, and expected outcomes of this hybrid model, emphasizing its potential to accelerate problem-solving and computation-intensive tasks. The report further delves into detailed implementation and performance assessments to validate the effectiveness of the proposed hybrid approach.

DOI - https://doi.org/10.65525/SVUP.9788199778009.2026.235-252