PO-QA: A Framework for Portfolio Optimization using Quantum Algorithms

• URL: http://arxiv.org/abs/2407.19857v1
• Date: Mon, 29 Jul 2024 10:26:28 GMT
• Title: PO-QA: A Framework for Portfolio Optimization using Quantum Algorithms
• Authors: Kamila Zaman, Alberto Marchisio, Muhammad Kashif, Muhammad Shafique,
• Abstract summary: Portfolio Optimization (PO) is a financial problem aiming to maximize the net gains while minimizing the risks in a given investment portfolio. We propose a novel scalable framework, denoted PO-QA, to investigate the variation of quantum parameters. Our results provide effective insights into comprehending PO from the lens of Quantum Machine Learning.
• Score: 4.2435928520499635
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Portfolio Optimization (PO) is a financial problem aiming to maximize the net gains while minimizing the risks in a given investment portfolio. The novelty of Quantum algorithms lies in their acclaimed potential and capability to solve complex problems given the underlying Quantum Computing (QC) infrastructure. Utilizing QC's applicable strengths to the finance industry's problems, such as PO, allows us to solve these problems using quantum-based algorithms such as Variational Quantum Eigensolver (VQE) and Quantum Approximate Optimization Algorithm (QAOA). While the Quantum potential for finance is highly impactful, the architecture and composition of the quantum circuits have not yet been properly defined as robust financial frameworks/algorithms as state of the art in present literature for research and design development purposes. In this work, we propose a novel scalable framework, denoted PO-QA, to systematically investigate the variation of quantum parameters (such as rotation blocks, repetitions, and entanglement types) to observe their subtle effect on the overall performance. In our paper, the performance is measured and dictated by convergence to similar ground-state energy values for resultant optimal solutions by each algorithm variation set for QAOA and VQE to the exact eigensolver (classical solution). Our results provide effective insights into comprehending PO from the lens of Quantum Machine Learning in terms of convergence to the classical solution, which is used as a benchmark. This study paves the way for identifying efficient configurations of quantum circuits for solving PO and unveiling their inherent inter-relationships.

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