Year: 2024
Type: article
Abstract: Portfolio optimization plays a central role in finance to obtain optimal portfolio allocationsthat aim to achieve certain investment goals. Over the years, many works have investigateddifferent variants of portfolio optimization. Portfolio optimization also provides a rich area tostudy the application of quantum computers to obtain advantages over classical computers.In this work, we give a sampling version of an existing classical online portfolio optimizationalgorithm by Helmbold et al., for which we in turn develop a quantum version. The quantumadvantage is achieved by using techniques such as quantum state preparation, inner productestimation and multi-sampling. Our quantum algorithm provides a quadratic speedup inthe time complexity, in terms of n, where n is the number of assets in the portfolio. Thetransaction cost of both of our classical and quantum algorithms is independent of n whichis especially useful for practical applications with a large number of assets.
Source: Quantum Information Processing
Authors: Debbie Lim, Patrick Rebentrost
Institutions: Centre for Quantum Technologies, University of Latvia
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Award: G1165633651
OpenAlex ID: W4391879783