Portfolio Optimization using Accelerated Quantum Simulation

Identifying the most suitable stocks for Portfolio stands as a critical challenge in the financial sector especially for a larger pool of assets. Quantum algorithms have emerged as a promising solution to the inherent challenges and offering the potential for exponential speedups in solving complex optimization problems. These algorithms are particularly suited for navigating the vast solution spaces associated with large asset pools, thus enabling more effective diversification strategies. Within this context, we explore the application of CUDA Quantum, an advanced quantum simulation framework and a quantum ready platform. Our study demonstrates that when applied to a set of 15 assets and more, CUDA Quantum significantly outperforms traditional CPU-based simulations in terms of computational speed and increases as the number of assets increase, highlighting the advantages of GPU acceleration in processing complex quantum algorithms. By extending our implementation to larger asset pools upto 30 assets, we provide empirical evidence of the substantial performance gains achievable with CUDA Quantum. Our findings illustrate the transformative potential of integrating quantum algorithms and GPU computing in addressing the challenge of portfolio optimization.