Solving Portfolio Optimization Problems using Adiabatic Quantum Computing
Abstract
Abstract This study explores the application of adiabatic quantum computing to portfolio optimization, a critical problem in finance. By formulating the problem as a Quadratic Unconstrained Binary Optimization (QUBO) model, the study integrates constraints such as expected return, transaction cost, and Environmental, Social, and Governance (ESG) scores. Simulations using D-Wave’s quantum annealer demonstrate the feasibility of optimizing portfolios comprising hundreds of assets with reduced computational time compared to classical methods. While current quantum devices face limitations in precision, this work lays the foundation for leveraging quantum technologies in financial optimization.
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