Variational Quantum-Classical Algorithms: A Review of Theory, Applications, and Opportunities

Abstract

Variational Quantum-Classical Algorithm (VQCA) is a potential tool for machine learning (ML) prediction tasks, but its efficacy, adaptability to big datasets, and optimization for noise reduction on quantum hardware are not clear. We aim to accomplish three study goals in this literature review. We begin by reviewing the justifications for ML practitioners' use of VQCA. Second, we compare the accuracy and effectiveness of VQCA in diverse domains to see whether it has a performance advantage over other ML methods. Finally, we evaluate VQCA's immediate and long-term effects on quantum ML and how well it performs compared to ML techniques for prediction tasks across various applications or domains. Our findings show that VQCA can be significantly more accurate and efficient than conventional algorithms. We also compare traditional ML algorithms with VQCA on various datasets and examine their theoretical guarantees. We equally look into how VQCA might be used practically to address problems in a variety of industries, including banking, healthcare, and energy. In various datasets, we assess the performance and efficacy of VQCA for unsupervised learning tasks. Finally, we go through ways to improve VQCA, particularly for big and complicated problems, to lessen the effect of noise and other sources of error in quantum hardware. Overall, we looked at VQCA’s advantages and disadvantages for ML prediction tasks, including possible directions for future study. Our findings show that VQCA has the potential to completely transform the ML industry, particularly in this emerging era of quantum computing.