Portfolio optimization is one of the important problem classes in finance sector. As the world economy recovered and reaching the pre-COVID levels and is still continuing to grow, it is imperative to understand what kind of impact COVID-19 had on portfolio optimization and how quantum computers can be used in doing so. Hybrid quantum algorithms like variational quantum eigen solver (VQE), quantum approximate optimization algorithm (QAOA) have been tried for identifying optimal portfolios. In the present work, we employ these algorithms to carry out portfolio optimization for different time intervals (which includes pre and during COVID-19 periods) and unravel the impact of COVID-19 on the same, across different industries.Our results show that the companies’ stocks were growing steadily more on a sideways movement from 2016-2017. The stocks start to pick momentum from 2018-2019 and in 2020 early months as dip can be observed, however all the stocks recover to normal levels and even reaching lifetime highs from second half of 2020. Keywords—Quantum Algorithms, Portfolio Optimization, Covid-19, Markowitz model, QAOA, VQE
Portfolio optimization is one of the important problem classes in finance sector. As the world economy recovered and reaching the pre-COVID levels and is still continuing to grow, it is imperative to understand what kind of impact COVID-19 had on portfolio optimization and how quantum computers can be used in doing so. Hybrid quantum algorithms like variational quantum eigen solver (VQE), quantum approximate optimization algorithm (QAOA) have been tried for identifying optimal portfolios. In the present work, we employ these algorithms to carry out portfolio optimization for different time intervals (which includes pre and during COVID-19 periods) and unravel the impact of COVID-19 on the same, across different industries.Our results show that the companies’ stocks were growing steadily more on a sideways movement from 2016-2017. The stocks start to pick momentum from 2018-2019 and in 2020 early months as dip can be observed, however all the stocks recover to normal levels and even reaching lifetime highs from second half of 2020. Keywords—Quantum Algorithms, Portfolio Optimization, Covid-19, Markowitz model, QAOA, VQE
This research investigates the efficacy of quantum and classical algorithms in the context of portfolio optimization, focusing on a dataset comprising 20 equities from India's National Stock Exchange over a duration of three years. The study employs quantum methodologies such as the Quantum Approxi-mate Optimization Algorithm (QAOA), the Variational Quantum Eigensolver (VQE), and the Exact Minimum Eigen Solver, alongside conventional strategies including Mean-Variance Op-timization, Genetic Algorithms, and Simulated Annealing. The methodology for this study includes data preprocessing, issue designing, and performance evaluation based on metrics such as MSE, RMSE, and Mean Absolute Error. The results obtained show quantum algorithms exhibit better performance over the classical methods regarding prediction accuracy and risk-return profiles, especially the Exact Minimum Eigen hSolver and QAOA. The Exact Minimum Eigen Solver and QAOA had the lowest MSE values (0.006 and 0.005, respectively), whereas the best-performing classical technique, the Genetic Algorithm, had an MSE of 0.0105. These findings imply that quantum algorithms provide significant advantages in portfolio optimization problems, with the potential to revolutionize financial optimization tactics. The research can be further unfolded into avenues for future inquiry, including expansion of the choice of assets, alternative data sources integration, as well as hybrid quantum-classical models to efficiently enhance portfolio management strategies.
This study explores the integration of quantum computing techniques, specifically the Quantum Approximate Optimization Algorithm (QAOA), with classical portfolio optimization frameworks through the Quadratic Unconstrained Binary Optimization (QUBO) approach. Using monthly turnover data from the National Stock Exchange of India (NSE), this paper demonstrates the construction of a QUBO matrix by encoding expected returns and covariance-based risk profiles. Binary asset inclusion constraints are introduced into portfolios and then optimised using both brute force enumeration and quantum inspired solvers. The analysis reveals those combinations including Index Futures and Index Options exhibit superior risk-adjusted performance (Sharpe Ratio≈0.286). The hybrid approach suggests the promise of quantum inspired models as means to improve financial decision making under uncertainty. The results also give a robust template for scalable asset selection models that operate within the constraints of quantum computing infrastructure.
Portfolio optimization is a fundamental problem in quantitative finance that seeks to maximize risk-adjusted returns while managing investment risk. With the advent of quantum computing, there is growing interest in applying quantum algorithms to solve complex optimization problems. This paper presents a comprehensive comparative study of quantum and classical approaches to portfolio optimization, specifically comparing the Quantum Approximate Optimization Algorithm (QAOA), Variational Quantum Eigensolver (VQE), and classical mean-variance optimization. We evaluate these methods on both real market data and synthetic datasets with varying correlation structures. Our experimental results show that while classical methods currently outperform quantum approaches on near-term quantum devices in terms of solution quality and computational efficiency, quantum algorithms demonstrate conceptual viability and promise for future applications as quantum hardware improves. The classical method achieves optimal Sharpe ratios of 1.2-1.8, while QAOA and VQE achieve 80-95% and 75-90% of classical performance respectively. This work provides insights into the current state and future potential of quantum computing for financial optimization.
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.
In this paper, we will be discussing how a Hybrid Quantum/Classical algorithm enhanced with Game theory would impact the Finance world, particularly in the Portfolio Optimization Sector.For that purpose, we provide an overview of the current state of the Portfolio Optimization Sector, and an example of a Hybrid Classical/Quantum QAOA optimization algorithm capable of generating Higher Annual Expected returns in comparison to current classical models, while also taking into consideration factors such as the covariance between the assets in the portfolio, and the behavior of other investors of Investment funds using Game theory, and then compare it to current Classical Strategies.We also discuss the current limitations and problems that modern Quantum computers endure, and what impact a Quantum computer with less noise and more qubits may have in Industries such as Mutual funds, Hedge Funds, ETF, and more.
The increasing complexity of financial markets has heightened the demand for advanced computational methods capable of optimizing portfolios under multi-dimensional constraints involving risk, return, diversification, and liquidity. Traditional optimization frameworks, such as mean-variance analysis and heuristic meta-optimization, are limited by their scalability and sensitivity to non-convex, high-dimensional data structures. This study explores the application of quantum algorithms to portfolio optimization, demonstrating how Quantum Approximate Optimization Algorithm (QAOA), Quantum Annealing, and Variational Quantum Eigensolver (VQE) can address computational bottlenecks inherent in classical approaches. Quantum computation offers the potential to encode complex financial systems as Hamiltonian functions, allowing parallel exploration of vast solution spaces through superposition and entanglement. This enables faster convergence toward optimal portfolio allocations while simultaneously accounting for multi-factor correlations and dynamic market conditions. The research further investigates the role of hybrid quantum–classical optimization pipelines, in which quantum solvers perform core optimization tasks while classical systems manage data preprocessing and constraint validation. Empirical modeling and simulation indicate that quantum-based approaches can outperform conventional methods in risk-adjusted return optimization, efficient frontier computation, and real-time portfolio rebalancing. By bridging the gap between quantum computing and financial engineering, this study contributes to the development of next-generation financial analytics systems, aligning with the broader goal of achieving more adaptive, transparent, and data-resilient financial decision-making.