Comparative analysis of diverse methodologies for portfolio optimization leveraging quantum annealing techniques

• URL: http://arxiv.org/abs/2403.02599v3
• Date: Mon, 8 Jul 2024 06:50:53 GMT
• Title: Comparative analysis of diverse methodologies for portfolio optimization leveraging quantum annealing techniques
• Authors: Zhijie Tang, Alex Lu Dou, Arit Kumar Bishwas,
• Abstract summary: As the number of involved assets and constraints increases, the portfolio optimization problem can become increasingly difficult to solve. Quantum annealing algorithm holds promise for solving complex portfolio optimization problems in the NISQ era.
• Score: 3.296670045513668
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Portfolio optimization (PO) is extensively employed in financial services to assist in achieving investment objectives. By providing an optimal asset allocation, PO effectively balances the risk and returns associated with investments. However, it is important to note that as the number of involved assets and constraints increases, the portfolio optimization problem can become increasingly difficult to solve, falling into the category of NP-hard problems. In such scenarios, classical algorithms, such as the Monte Carlo method, exhibit limitations in addressing this challenge when the number of stocks in the portfolio grows. Quantum annealing algorithm holds promise for solving complex portfolio optimization problems in the NISQ era. Many studies have demonstrated the advantages of various quantum annealing algorithm variations over the standard quantum annealing approach. In this work, we conduct a numerical investigation of randomly generated unconstrained single-period discrete mean-variance portfolio optimization instances. We explore the application of a variety of unconventional quantum annealing algorithms, employing both forward annealing and reverse annealing schedules. By comparing the time-to-solution(TTS) and success probabilities of diverse approaches, we show that certain methods exhibit advantages in enhancing the success probability when utilizing conventional forward annealing schedules. Furthermore, we find that the implementation of reverse annealing schedules can significantly improve the performance of select unconventional quantum annealing algorithms.

Related papers

• PO-QA: A Framework for Portfolio Optimization using Quantum Algorithms [4.2435928520499635]
  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.
  arXiv  Detail & Related papers  (2024-07-29T10:26:28Z)
• Bayesian Parameterized Quantum Circuit Optimization (BPQCO): A task and hardware-dependent approach [49.89480853499917]
  Variational quantum algorithms (VQA) have emerged as a promising quantum alternative for solving optimization and machine learning problems. In this paper, we experimentally demonstrate the influence of the circuit design on the performance obtained for two classification problems. We also study the degradation of the obtained circuits in the presence of noise when simulating real quantum computers.
  arXiv  Detail & Related papers  (2024-04-17T11:00:12Z)
• A Novel Knapsack-based Financial Portfolio Optimization using Quantum Approximate Optimization Algorithm [2.6603181502541986]
  We propose a method that uses the quantum computing capabilities of the quantum walk mixer with the quantum approximate optimization algorithm (QAOA) to address the challenges presented by the NP-hard problem. Our study successfully achieves the approximate ratio of the portfolio optimization technique using a circuit layer of p >= 3, compared to the classical best-known solution of the knapsack problem.
  arXiv  Detail & Related papers  (2024-02-11T08:20:26Z)
• Federated Conditional Stochastic Optimization [110.513884892319]
  Conditional optimization has found in a wide range of machine learning tasks, such as in-variant learning tasks, AUPRC, andAML. This paper proposes algorithms for distributed federated learning.
  arXiv  Detail & Related papers  (2023-10-04T01:47:37Z)
• A Review on Quantum Approximate Optimization Algorithm and its Variants [47.89542334125886]
  The Quantum Approximate Optimization Algorithm (QAOA) is a highly promising variational quantum algorithm that aims to solve intractable optimization problems. This comprehensive review offers an overview of the current state of QAOA, encompassing its performance analysis in diverse scenarios. We conduct a comparative study of selected QAOA extensions and variants, while exploring future prospects and directions for the algorithm.
  arXiv  Detail & Related papers  (2023-06-15T15:28:12Z)
• Learning to Optimize with Stochastic Dominance Constraints [103.26714928625582]
  In this paper, we develop a simple yet efficient approach for the problem of comparing uncertain quantities. We recast inner optimization in the Lagrangian as a learning problem for surrogate approximation, which bypasses apparent intractability. The proposed light-SD demonstrates superior performance on several representative problems ranging from finance to supply chain management.
  arXiv  Detail & Related papers  (2022-11-14T21:54:31Z)
• Decomposition of Matrix Product States into Shallow Quantum Circuits [62.5210028594015]
  tensor network (TN) algorithms can be mapped to parametrized quantum circuits (PQCs) We propose a new protocol for approximating TN states using realistic quantum circuits. Our results reveal one particular protocol, involving sequential growth and optimization of the quantum circuit, to outperform all other methods.
  arXiv  Detail & Related papers  (2022-09-01T17:08:41Z)
• Comparing Classical-Quantum Portfolio Optimization with Enhanced Constraints [0.0]
  We show how to add fundamental analysis to the portfolio optimization problem, adding in asset-specific and global constraints based on chosen balance sheet metrics. We analyze the current state-of-the-art algorithms for solving such a problem using D-Wave's Quantum Processor and compare the quality of the solutions obtained to commercially-available optimization software.
  arXiv  Detail & Related papers  (2022-03-09T17:46:32Z)
• Variational Quantum Optimization with Multi-Basis Encodings [62.72309460291971]
  We introduce a new variational quantum algorithm that benefits from two innovations: multi-basis graph complexity and nonlinear activation functions. Our results in increased optimization performance, two increase in effective landscapes and a reduction in measurement progress.
  arXiv  Detail & Related papers  (2021-06-24T20:16:02Z)
• Hybrid quantum-classical optimization for financial index tracking [0.0]
  Tracking a financial index boils down to replicating its trajectory of returns for a well-defined time span by investing in a subset of the securities included in the benchmark. Picking the optimal combination of assets becomes a challenging weighted-hard problem even for moderately large indices consisting of dozens or hundreds of assets. We introduce a pruning algorithm to find weighted combinations of assets subject to cardinality constraints.
  arXiv  Detail & Related papers  (2020-08-27T10:59:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.