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

Eneko Osaba, Guillaume Gelabert, Esther Villar-Rodríguez +2 more

Narun Tripathi Narun, Divyata Sain, Harishita +2 more

Harsh Singh

Quantum Computing for Portfolio Optimization explores the application of quantum algorithms to enhance asset allocation, risk management, and investment decision-making. By leveraging quantum computing techniques, the approach can process complex financial datasets, identify optimal portfolio structures, and improve computational efficiency beyond classical methods. This emerging field has the potential to transform quantitative finance, enabling faster, more accurate, and adaptive portfolio optimization strategies.

Tanvi Hirlekar, Tanvi Nehete, Vaishnavi Patil +2 more

Haijing Wei, Yanbo J. Wang, Haoxiang Yang +12 more

Portfolio optimization (PO) plays a central role in finance, encompassing both resource allocation and risk management. While numerous algorithms have been developed for continuous-variable PO, the inclusion of large, indivisible assets such as real estate transforms the task into a combinatorial optimization problem, which is NP-hard for classical computers. This study explores using quantum-classical hybrid algorithms for multi-discretized portfolio optimization, addressing the needs of real-world applications. We propose a flexible theoretical model that can solve PO problems with various types of discrete assets and constraints, which is compatible with both quantum and classical computing paradigms. Then, we use the D-Wave quantum processor and classical solver to determine optimal investment strategies. Experimental results indicate that the hybrid quantum algorithm outperforms both classical computing methods and quantum annealing algorithms in terms of return and risk management. This research enhances quantum computing applications in finance and provides insights into future algorithm design.

Chansreynich Huot, Kimleang Kea, Tae-Kyung Kim +1 more

Portfolio optimization is a primary component of the decision-making process in finance, aiming to tactfully allocate assets to achieve optimal returns while considering various constraints. Herein, we proposed a method that uses the knapsack-based portfolio optimization problem and incorporates 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. Additionally, we present the sequential procedure of our suggested approach and demonstrate empirical proof to illustrate the effectiveness of the proposed method in finding the optimal asset allocations across various constraints and asset choices. Moreover, we discuss the effectiveness of the QAOA components in relation to our proposed method. Consequently, our study successfully achieves the approximate ratio of the portfolio optimization technique using a circuit layer of$p\geqslant 3$, compared to the classical best-known solution of the knapsack problem. Our proposed methods potentially contribute to the growing field of quantum finance by offering insights into the potential benefits of employing quantum algorithms for complex optimization tasks in financial portfolio management.