HQSI: Hybrid Quantum Swarm Intelligence -- A Case Study of Online Certificate Status Protocol Request Flow Prediction

TL;DR

This paper introduces HQSI, a hybrid quantum-classical approach combining quantum neural networks and swarm intelligence to improve online certificate status protocol request flow prediction, achieving over 50% error reduction.

Contribution

The study presents a novel hybrid quantum swarm intelligence framework that integrates quantum neural networks with classical swarm algorithms for optimized prediction tasks.

Findings

Over 50% error reduction compared to existing quantum optimization methods

Effective integration of quantum and classical computing for neural network training

Improved prediction accuracy in online certificate status protocol requests

Abstract

As quantum computing technology continues to advance, various sectors, including industry, government, academia, and research, have increasingly focused on its future applications. With the integration of artificial intelligence techniques, multiple Quantum Neural Network (QNN) models have been proposed, including quantum convolutional neural networks, quantum long short-term memory networks, and quantum generative adversarial networks. Furthermore, optimization methods such as constrained optimization by linear approximation and simultaneous perturbation stochastic approximation have been explored. Therefore, this study proposes Hybrid Quantum Swarm Intelligence (HQSI), which constructs a QNN model as a forward propagation neural network. After measuring quantum states and obtaining prediction results, a classical computer-based swarm intelligence algorithm is employed for weight optimization. The training process iterates between quantum and classical computing environments. During the experimental phase, the proposed HQSI method is evaluated using an online certificate status protocol request traffic prediction task. Comparative analysis against state-of-the-art quantum optimization algorithms demonstrates that the proposed HQSI approach achieves more than a 50% reduction in error.