Backtesting Quantum Computing Algorithms for Portfolio Optimization
In portfolio theory, the investment portfolio optimization problem is one of those problems whose complexity grows exponentially with the number of assets. By backtesting classical and quantum computing algorithms, we can get a sense of how these algorithms might perform in the real world. This work...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Transactions on Quantum Engineering |
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| Online Access: | https://ieeexplore.ieee.org/document/10329473/ |
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| author | Gines Carrascal Paula Hernamperez Guillermo Botella Alberto del Barrio |
| author_facet | Gines Carrascal Paula Hernamperez Guillermo Botella Alberto del Barrio |
| author_sort | Gines Carrascal |
| collection | DOAJ |
| description | In portfolio theory, the investment portfolio optimization problem is one of those problems whose complexity grows exponentially with the number of assets. By backtesting classical and quantum computing algorithms, we can get a sense of how these algorithms might perform in the real world. This work establishes a methodology for backtesting classical and quantum algorithms in equivalent conditions, and uses it to explore four quantum and three classical computing algorithms for portfolio optimization and compares the results. Running 10 000 experiments on equivalent conditions we find that quantum can match or slightly outperform classical results, showing a better escalability trend. To the best of our knowledge, this is the first work that performs a systematic backtesting comparison of classical and quantum portfolio optimization algorithms. In this work, we also analyze in more detail the variational quantum eigensolver algorithm, applied to solve the portfolio optimization problem, running on simulators and real quantum computers from IBM. The benefits and drawbacks of backtesting are discussed, as well as some of the challenges involved in using real quantum computers of more than 100 qubits. Results show quantum algorithms can be competitive with classical ones, with the advantage of being able to handle a large number of assets in a reasonable time on a future larger quantum computer. |
| format | Article |
| id | doaj-art-1ad61b70b503494f8d84d86e1a44893b |
| institution | Kabale University |
| issn | 2689-1808 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Quantum Engineering |
| spelling | doaj-art-1ad61b70b503494f8d84d86e1a44893b2025-01-25T00:03:30ZengIEEEIEEE Transactions on Quantum Engineering2689-18082024-01-01512010.1109/TQE.2023.333732810329473Backtesting Quantum Computing Algorithms for Portfolio OptimizationGines Carrascal0https://orcid.org/0000-0001-7112-6696Paula Hernamperez1https://orcid.org/0000-0003-2201-0050Guillermo Botella2https://orcid.org/0000-0002-0848-2636Alberto del Barrio3https://orcid.org/0000-0002-6769-1200IBM Quantum, IBM Consulting España, Madrid, SpainCARTIF, Valladolid, SpainDepartment of Computer Architecture and Automation, Faculty of Informatics, Complutense University of Madrid, Madrid, SpainDepartment of Computer Architecture and Automation, Faculty of Informatics, Complutense University of Madrid, Madrid, SpainIn portfolio theory, the investment portfolio optimization problem is one of those problems whose complexity grows exponentially with the number of assets. By backtesting classical and quantum computing algorithms, we can get a sense of how these algorithms might perform in the real world. This work establishes a methodology for backtesting classical and quantum algorithms in equivalent conditions, and uses it to explore four quantum and three classical computing algorithms for portfolio optimization and compares the results. Running 10 000 experiments on equivalent conditions we find that quantum can match or slightly outperform classical results, showing a better escalability trend. To the best of our knowledge, this is the first work that performs a systematic backtesting comparison of classical and quantum portfolio optimization algorithms. In this work, we also analyze in more detail the variational quantum eigensolver algorithm, applied to solve the portfolio optimization problem, running on simulators and real quantum computers from IBM. The benefits and drawbacks of backtesting are discussed, as well as some of the challenges involved in using real quantum computers of more than 100 qubits. Results show quantum algorithms can be competitive with classical ones, with the advantage of being able to handle a large number of assets in a reasonable time on a future larger quantum computer.https://ieeexplore.ieee.org/document/10329473/Backtestingconditional value at risk variational quantum eigensolver (CVaR VQE)portfolio optimizationquantum computing (QC)variational quantum eigensolver (VQE) |
| spellingShingle | Gines Carrascal Paula Hernamperez Guillermo Botella Alberto del Barrio Backtesting Quantum Computing Algorithms for Portfolio Optimization IEEE Transactions on Quantum Engineering Backtesting conditional value at risk variational quantum eigensolver (CVaR VQE) portfolio optimization quantum computing (QC) variational quantum eigensolver (VQE) |
| title | Backtesting Quantum Computing Algorithms for Portfolio Optimization |
| title_full | Backtesting Quantum Computing Algorithms for Portfolio Optimization |
| title_fullStr | Backtesting Quantum Computing Algorithms for Portfolio Optimization |
| title_full_unstemmed | Backtesting Quantum Computing Algorithms for Portfolio Optimization |
| title_short | Backtesting Quantum Computing Algorithms for Portfolio Optimization |
| title_sort | backtesting quantum computing algorithms for portfolio optimization |
| topic | Backtesting conditional value at risk variational quantum eigensolver (CVaR VQE) portfolio optimization quantum computing (QC) variational quantum eigensolver (VQE) |
| url | https://ieeexplore.ieee.org/document/10329473/ |
| work_keys_str_mv | AT ginescarrascal backtestingquantumcomputingalgorithmsforportfoliooptimization AT paulahernamperez backtestingquantumcomputingalgorithmsforportfoliooptimization AT guillermobotella backtestingquantumcomputingalgorithmsforportfoliooptimization AT albertodelbarrio backtestingquantumcomputingalgorithmsforportfoliooptimization |