Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Quantum Algorithms for Portfolio Optimisation and Risk Management: A Survey

Ria Rushin Joseph, Snia Joseph, Shiva Raj Pokhrel

Published Jan 1, 2026• 0 citations
Publisher

Abstract

Quantum Algorithms for Portfolio Optimisation and Risk Management: A Survey
DOI
10.13140/rg.2.2.25351.15527

Related papers

Portfolio Risk Management Using Quantum Annealing

Thomas Doutre, Emmanuel De Meric De Bellefon

World Academy of Science, Engineering and Technology, International Journal of Mathematical and Computational Sciences•Sep 26, 2016•0 citations

Quantum-Enhanced Financial Volatility Forecasting and Portfolio Risk Management: A Hybrid Quantum-Classical Framework

Zefree Lazarus Mayaluri, Sasmita Mishra

Computational Economics•Oct 22, 2026•0 citations

Application of Quantum Computing Algorithms for Optimization of Tactical Asset Allocation in Portfolio Management

Dmitry Garmash

•Jan 1, 2025•0 citations

Portfolio Optimization: Applications in Quantum Computing

Michael Marzec

•Jan 1, 2013•10 citations

Quantum Ensemble Optimisation: Revolutionizing Investment Portfolio Management with QAOA and VQE Integration

Yash Shah, Nitesh Bhalala, Pankaj Sonawane +3 more

Quantum computing is presenting promising prospects for financial applications, specifically in the domain of investment portfolio management. The present study investigates the capabilities of Quantum Ensemble Optimisation (QEO), an approach that integrates the Variational Quantum Eigensolver (VQE) and the Quantum Approximate Optimisation Algorithm (QAOA). Enhancing portfolio performance through a balance between diversification and risk-adjusted returns is our objective. A novel methodology has been devised to integrate the flexibility of VQE with the efficacy of QAOA, yielding an assortment of portfolio solutions. Our investigation into the performance of our model on empirical financial data has revealed that it surpasses conventional optimization techniques in terms of risk-adjusted returns and solution space exploration. The results of this study underscore the potential of our hybrid methodology, which capitalizes on the advantages of QAOA and VQE, to propel the field of quantum-based portfolio optimization forward. This may ultimately result in investors reaching more informed investment decisions and experiencing enhanced financial outcomes.

Educational Administration Theory and Practice journal•Jan 8, 2024•0 citations

QUANTUM COMPUTING IN FINANCIAL RISK MANAGEMENT AND PORTFOLIO OPTIMIZATION

Neelotpal Dey, Mr Akash Kumar, Mr Sanjay

•Jan 1, 2025•0 citations
Quantum computing is presenting promising prospects for financial applications, specifically in the domain of investment portfolio management. The present study investigates the capabilities of Quantum Ensemble Optimisation (QEO), an approach that integrates the Variational Quantum Eigensolver (VQE) and the Quantum Approximate Optimisation Algorithm (QAOA). Enhancing portfolio performance through a balance between diversification and risk-adjusted returns is our objective. A novel methodology has been devised to integrate the flexibility of VQE with the efficacy of QAOA, yielding an assortment of portfolio solutions. Our investigation into the performance of our model on empirical financial data has revealed that it surpasses conventional optimization techniques in terms of risk-adjusted returns and solution space exploration. The results of this study underscore the potential of our hybrid methodology, which capitalizes on the advantages of QAOA and VQE, to propel the field of quantum-based portfolio optimization forward. This may ultimately result in investors reaching more informed investment decisions and experiencing enhanced financial outcomes.