Quantum machine learning in finance: A brief review

The synergy between quantum computing and machine learning gives future potential in the financial sector. The unique features such as superposition and entanglement in quantum computing are utilized in Quantum Machine Learning (QML) to solve the challenges that dominate the modern era of finance. The subject of this paper explains the use of Quantum Machine Learning (QML) in finance including risk management, portfolio optimization, market trend forecasting, and fraud detection. In portfolio optimization, the aim is to reduce risk at the same time maximizing return, the quantum-enhanced methodology does it better than the classical method. QML is applied for risk analysis because it efficiently computes values such as Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR). In finance, improved sentiment analysis, anomaly detection to prevent fraud, and prediction of market trends are enabled with QML algorithms. QML improves credit rating, financial time series forecasting, and derivative pricing with its ability to process large amounts of data.