Quantum Testing of Recommender Algorithms on GPU-Based Quantum Simulators
This study explores the application of quantum computing in asset management, focusing on the use of the Quantum Approximate Optimization Algorithm (QAOA) to solve specific classes of financial asset recommendation problems. While quantum computing holds promise for combinatorial optimization tasks,...
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MDPI AG
2025-04-01
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| Series: | Computers |
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| Online Access: | https://www.mdpi.com/2073-431X/14/4/137 |
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| author | Chenxi Liu W. Bernard Lee Anthony G. Constantinides |
| author_facet | Chenxi Liu W. Bernard Lee Anthony G. Constantinides |
| author_sort | Chenxi Liu |
| collection | DOAJ |
| description | This study explores the application of quantum computing in asset management, focusing on the use of the Quantum Approximate Optimization Algorithm (QAOA) to solve specific classes of financial asset recommendation problems. While quantum computing holds promise for combinatorial optimization tasks, its application to portfolio management faces significant challenges in scalability for practical implementations. In this work, we model the problem using a graph representation where nodes represent investors, and edges reflect significant similarities in asset choices. We test the proposed method using quantum simulators, including cuQuantum, Cirq-GPU, and Cirq with IonQ, and compare the performance of quantum optimization against classical brute-force methods. Our results suggest that quantum algorithms may offer computational advantages for certain use cases, though classical heuristics also provide competitive performance for smaller datasets. This study contributes to the ongoing investigation into the potential of quantum computing for real-time financial decision-making, providing insights into both its applicability and limitations in asset management for larger and more complex investor datasets. |
| format | Article |
| id | doaj-art-db66528a4a8b4e6ba757bf6c84b1b2f6 |
| institution | OA Journals |
| issn | 2073-431X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj-art-db66528a4a8b4e6ba757bf6c84b1b2f62025-08-20T02:17:20ZengMDPI AGComputers2073-431X2025-04-0114413710.3390/computers14040137Quantum Testing of Recommender Algorithms on GPU-Based Quantum SimulatorsChenxi Liu0W. Bernard Lee1Anthony G. Constantinides2Department of Computing, Hong Kong Polytechnic University, Hong Kong, ChinaHedgeSPA Private Limited, 12 Woodlands Square #05-70 Woods Square Tower 1, Singapore 737715, SingaporeAI & Data Analytics Lab, Imperial College London, London SW7 2AZ, UKThis study explores the application of quantum computing in asset management, focusing on the use of the Quantum Approximate Optimization Algorithm (QAOA) to solve specific classes of financial asset recommendation problems. While quantum computing holds promise for combinatorial optimization tasks, its application to portfolio management faces significant challenges in scalability for practical implementations. In this work, we model the problem using a graph representation where nodes represent investors, and edges reflect significant similarities in asset choices. We test the proposed method using quantum simulators, including cuQuantum, Cirq-GPU, and Cirq with IonQ, and compare the performance of quantum optimization against classical brute-force methods. Our results suggest that quantum algorithms may offer computational advantages for certain use cases, though classical heuristics also provide competitive performance for smaller datasets. This study contributes to the ongoing investigation into the potential of quantum computing for real-time financial decision-making, providing insights into both its applicability and limitations in asset management for larger and more complex investor datasets.https://www.mdpi.com/2073-431X/14/4/137quantum computingquantum approximate optimization algorithmmax-cut problemfinancial asset recommendationIonQgraph neural networks |
| spellingShingle | Chenxi Liu W. Bernard Lee Anthony G. Constantinides Quantum Testing of Recommender Algorithms on GPU-Based Quantum Simulators Computers quantum computing quantum approximate optimization algorithm max-cut problem financial asset recommendation IonQ graph neural networks |
| title | Quantum Testing of Recommender Algorithms on GPU-Based Quantum Simulators |
| title_full | Quantum Testing of Recommender Algorithms on GPU-Based Quantum Simulators |
| title_fullStr | Quantum Testing of Recommender Algorithms on GPU-Based Quantum Simulators |
| title_full_unstemmed | Quantum Testing of Recommender Algorithms on GPU-Based Quantum Simulators |
| title_short | Quantum Testing of Recommender Algorithms on GPU-Based Quantum Simulators |
| title_sort | quantum testing of recommender algorithms on gpu based quantum simulators |
| topic | quantum computing quantum approximate optimization algorithm max-cut problem financial asset recommendation IonQ graph neural networks |
| url | https://www.mdpi.com/2073-431X/14/4/137 |
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