Page 75 - CMA Journal (Nov-Dec 2024)

TOP TECHNOLOGY TREND

T T T

By ICMA Research and Publica ons Department

Quantum-Inspired Computing (QIC)

for High-Speed Financial Risk Modeling

Risk modeling and decision-making require ultra-fast risk, demonstrating their integration with QAE for
computation, real-time data processing, and efficient market and credit risk assessment.
predictive accuracy in today's fast-paced financial
landscape. Even with high-performance computing • Portfolio Optimization
(HPC) and AI-driven analytics, traditional computing Optimizing investment portfolios involves balancing
methods struggle to process the sheer complexity of
expected returns against associated risks, a complex
financial risk assessment in real-time.
problem with numerous variables. Quantum-inspired
Financial risk modeling is being transformed by computing techniques, like quantum annealing, have
Quantum-Inspired Computing (QIC) which is an been applied to solve these optimization problems
advanced approach that enables classical systems to more efficiently. Research indicates that these
replicate certain quantum behaviors while methods can enhance the efficiency of solving
overcoming the complexity and cost barriers of true complex optimization problems in finance,
quantum computing. QIC utilizes classical bits with potentially leading to more robust portfolio
quantum-inspired techniques such as quantum strategies.
annealing, tensor networks, and hybrid • Credit Risk Assessment
quantum-classical algorithms to enhance
computational efficiency, enabling faster data Accurately assessing credit risk is crucial for financial
processing and complex system modeling. These institutions to prevent loan defaults and maintain
methods significantly accelerate computations financial stability. Quantum-inspired machine
compared to traditional systems, making them highly learning models have been developed to predict
effective in financial risk analysis. credit rating downgrades, known as fallen-angels

Practical Applications and Statistics forecasting. A study demonstrated that such models
could achieve competitive performance against
• Monte Carlo Simulations traditional methods, offering better interpretability
and comparable training times.
Monte Carlo (MC) simulations are essential in financial
risk management, from estimating value-at-risk (VaR)
to pricing over-the-counter derivatives, but they incur
high computational costs due to extensive scenario
generation. Quantum Amplitude Estimation (QAE)
offers a quadratic speed-up by reducing the required
simulations. While pre-computed probability
distributions can optimize QAE, their numerical
generation may offset quantum advantages. To
address this, scenario generation is integrated within
quantum computation, termed Quantum MC (QMC)
simulations. Quantum circuits are developed for

stochastic models in equity, interest rate, and credit

BACK TO CONTENTS PAGE ICMA’s Chartered Management Accountant, Jan-Feb 2025 73