Quantum-Inspired Optimization for Computational Finance
Abstract:
This challenge invites teams to develop innovative financial applications that leverage
quantum-inspired optimization techniques. The objective is to solve complex optimization
problems in finance more efficiently than traditional methods by utilizing algorithms
inspired by quantum computing but executable on classical hardware.
Business Problem:
The financial industry faces increasingly complex optimization challenges, such as
portfolio optimization, risk management, and resource allocation, which involve
processing vast amounts of data with numerous constraints. Traditional optimization
algorithms often struggle with these high-dimensional, combinatorial problems, leading to
suboptimal solutions and prolonged computation times.
Quantum computing offers the potential to solve certain optimization problems more
efficiently than classical computing. However, practical quantum computers are still in
developmental stages and are not widely accessible. Quantum-inspired optimization
techniques mimic quantum behaviors using classical hardware, providing a practical
means to achieve enhanced computational performance today.
The inspiration behind this challenge is to harness quantum-inspired optimization
algorithms to revolutionize financial services by enabling faster, more accurate decision-
making processes, ultimately leading to better financial outcomes and competitive
advantages.
Technical Problem Statement:
Develop a financial application that employs quantum-inspired optimization techniques to
address a specific complex optimization problem within the financial sector. The
application should demonstrate measurable improvements in performance - such as
increased computational speed, enhanced solution quality, or better scalability - compared to existing classical optimization methods. Possible focus areas include, but
are not limited to:
- Real World Portfolio Optimization: Optimize large-scale investment portfolios
considering factors like risk tolerance, regulatory requirements, transaction costs, and
liquidity constraints using the TitanQ Optimization platform
- Risk Management Optimization: Use quantum-inspired optimization to improve the
efficiency of stress testing and scenario analysis in risk management such as computation
of Value at Risk (VaR) or Conditional Value at Risk (CVaR)
- Credit Scoring and Loan Portfolio Optimization: Enhance credit risk assessment
models and optimize loan portfolios for maximum return and minimal risk.
- Optimization in High-Frequency Trading Strategies: Develop algorithms that optimize
trade execution order flows to take advantage of statistical arbitrage opportunities on a
high frequency scale.
Current Classical Approaches:
Traditional financial optimization problems are typically addressed using classical
algorithms such as:
- Linear Programming (LP) , Quadratic Programming (QP) and Mixed-Integer
Programming (MIP) : Used for problems that can be expressed with linear or quadratic
objective functions and constraints, with the ability to handle mixed binary integer and
continuous systems.
- Heuristic and Metaheuristic Algorithms: Techniques like Genetic Algorithms, Simulated Annealing, Tabu Search, Particle Swarm Optimization, and many others provide approximate solutions but may not guarantee optimality and can be slow to converge.
These classical approaches often face limitations in terms of scalability, computational
speed, and the ability to find global optima in complex, high-dimensional spaces typical of
financial optimization problems. This is especially true when incorporating real world
trading constraints and regulations. Part of a successful challenge submission will involve
demonstration of potential improvements a quantum-inspired technique could have over
the classical and available systems.
Desired Outcomes & Interested:
- Real World Application: Develop a novel financial optimization tool or solution that
leverages the TitanQ optimization framework to address a real world application. A
successful submission will go beyond the standard academic problems and attempt to
solve a real problem.
- Advantage: Provide quantitative evidence of performance improvements over classical
optimization methods in terms of speed, solution quality, or scalability.
- Functioning Prototype: Deliver a working prototype that can be tested and evaluated
under realistic financial conditions.
- Scalability: Demonstration of the possibility that this solution can scale above and
beyond what classical approaches currently offer.
- Documentation: Include detailed methodology, implementation process, performance
analysis, and discussion on potential industry impact.
References for further information:
- Herman, Dylan, et al. "A survey of quantum computing for finance." arXiv:2201.02773 (2022).
- Egger, Daniel J., et al. "Quantum computing for finance: State-of-the-art and future prospects." IEEE
Transactions on Quantum Engineering 1 (2020): 1-24
- Albareti, Franco D., et al. "A structured survey of quantum computing for the financial industry." arXiv:2204.10026 (2022).
