Real-Time Threat Mitigation In Financial IT Infrastructure Using Quantum Computing
Abstract
Financial institutions continue to face evolving cyber security threats that require immediate detection and mitigation to prevent significant damage. Classical-based cyber-security mechanisms struggle to keep up with these emerging threats due to their limitations in processing power and scalability, especially when dealing with distributed attacks. Quantum computing promises an unmatched level of scalable parallel processing with increased accuracy, speed, and timely response to real-time threats. This research evaluates the application of quantum computing algorithms, specifically Continuous-Variable Quantum Neural Networks (CV-QNN), Crystals-Kyber cryptographic methods, and Quantum-enhanced Monte Carlo simulations, within financial IT infrastructures. Our findings indicate that quantum algorithms substantially enhance threat detection accuracy, reduce response latency, and ensure secure communication against quantum-powered threats. However, practical implementation of quantum computing solutions faces challenges such as high error rates, environmental sensitivity, and integration complexities. Addressing these issues requires further technological advancement and strategic planning. This research contributes actionable insights for financial institutions, guiding the strategic adoption of quantum technologies to strengthen cybersecurity resilience.
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