Abstract

This comprehensive review explores recent advancements in quantum machine learning algorithms and their implications for financial market prediction. Quantum computing, with its unique principles like superposition and entanglement, holds great promise for transforming predictive modeling and decision-making in finance. After elucidating the fundamental principles of quantum computing relevant to machine learning, the review introduces quantum machine learning and its applicability in various domains, including finance. It highlights challenges in integrating quantum computing with machine learning, especially in the context of financial markets, and provides an overview of traditional machine learning methodologies in finance, emphasizing their limitations. The review then discusses quantum machine learning algorithms such as quantum support vector machines, quantum neural networks, and quantum principal component analysis, along with their advantages and potential applications in financial market prediction. Recent advancements in quantum computing, such as achieving quantum supremacy and developing hybrid quantum-classical methodologies, are also discussed in the context of financial applications. The review concludes by addressing current challenges and future prospects in quantum machine learning for financial markets, emphasizing the need for interdisciplinary collaboration and ongoing research efforts to fully leverage the potential of quantum computing in finance.

Description