Design and Optimization of Multimodal Hybrid Architectures Based on Quantum Computing

Abstract

In this paper, a hybrid model framework based on quantum computing optimization is proposed for resource demand forecasting and data classification tasks. The study first constructs an AR model for demand forecasting, determines the lag order by PACF and ACF, and transforms the model parameters into QUBO form for optimization and solution after discretization. Secondly, for SVM classification, the penalty function method is adopted to transform the constraints into unconstrained optimization, and the QUBO model is constructed to achieve classification boundary optimization by discretizing the decision variables and introducing penalty function terms. Finally, in the field of deep learning, a parameter optimization method based on Adam's algorithm is proposed to construct a CNN model by combining the ReLU activation function, and the loss function is transformed into a QUBO matrix by parameter quantization technique, and the optimal parameter configurations are solved by using quantum computation. The results show that the framework achieves good results in both prediction accuracy and classification performance, providing a new solution for complex optimization situations.