This review paper explores the potential implications of quantum computing on financial risk management. It begins by introducing the fundamental principles of quantum computing, including qubits, superposition, and entanglement, and discusses its advantages over classical computing in terms of speed and computational power. The paper then outlines traditional approaches to financial risk management, highlighting their limitations in handling the complexity and scale of modern financial markets. The authors delve into how quantum algorithms, such as quantum Monte Carlo methods and quantum annealing, can enhance risk assessment and mitigation strategies. These algorithms can enable more efficient simulations and optimization techniques, potentially improving portfolio optimization, risk assessment, stress testing, derivative pricing, and credit risk modeling. However, the paper also identifies significant challenges and barriers to the adoption of quantum computing in the financial industry, including technical issues related to qubit stability, coherence time, and error correction, as well as regulatory and resource constraints. Despite these challenges, the authors emphasize the potential benefits of quantum computing in enhancing financial risk management capabilities and making more informed investment decisions. Finally, the paper suggests future research directions, including the development of more robust quantum algorithms, improvements in quantum hardware technology, and interdisciplinary collaboration to bridge the gap between quantum physics and financial mathematics. The authors conclude that while quantum computing faces significant hurdles, its integration into financial risk management holds promise for improving risk assessment, portfolio optimization, and derivative pricing in today's complex financial markets.This review paper explores the potential implications of quantum computing on financial risk management. It begins by introducing the fundamental principles of quantum computing, including qubits, superposition, and entanglement, and discusses its advantages over classical computing in terms of speed and computational power. The paper then outlines traditional approaches to financial risk management, highlighting their limitations in handling the complexity and scale of modern financial markets. The authors delve into how quantum algorithms, such as quantum Monte Carlo methods and quantum annealing, can enhance risk assessment and mitigation strategies. These algorithms can enable more efficient simulations and optimization techniques, potentially improving portfolio optimization, risk assessment, stress testing, derivative pricing, and credit risk modeling. However, the paper also identifies significant challenges and barriers to the adoption of quantum computing in the financial industry, including technical issues related to qubit stability, coherence time, and error correction, as well as regulatory and resource constraints. Despite these challenges, the authors emphasize the potential benefits of quantum computing in enhancing financial risk management capabilities and making more informed investment decisions. Finally, the paper suggests future research directions, including the development of more robust quantum algorithms, improvements in quantum hardware technology, and interdisciplinary collaboration to bridge the gap between quantum physics and financial mathematics. The authors conclude that while quantum computing faces significant hurdles, its integration into financial risk management holds promise for improving risk assessment, portfolio optimization, and derivative pricing in today's complex financial markets.