Quantum Computing for Multi Period Asset Allocation
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912073794977792 |
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| author | Sun, Queenie Grablevsky, Nicholas Deng, Huaizhang Azadi, Pooya |
| author_facet | Sun, Queenie Grablevsky, Nicholas Deng, Huaizhang Azadi, Pooya |
| contents | Portfolio construction has been a long-standing topic of research in finance. The computational complexity and the time taken both increase rapidly with the number of investments in the portfolio. It becomes difficult, even impossible for classic computers to solve. Quantum computing is a new way of computing which takes advantage of quantum superposition and entanglement. It changes how such problems are approached and is not constrained by some of the classic computational complexity. Studies have shown that quantum computing can offer significant advantages over classical computing in many fields. The application of quantum computing has been constrained by the unavailability of actual quantum computers. In the past decade, there has been the rapid development of the large-scale quantum computer. However, software development for quantum computing is slow in many fields. In our study, we apply quantum computing to a multi-asset portfolio simulation. The simulation is based on historic data, covariance, and expected returns, all calculated using quantum computing. Although technically a solvable problem for classical computing, we believe the software development is important to the future application of quantum computing in finance. We conducted this study through simulation of a quantum computer and the use of Rensselaer Polytechnic Institute's IBM quantum computer. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_11997 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Quantum Computing for Multi Period Asset Allocation Sun, Queenie Grablevsky, Nicholas Deng, Huaizhang Azadi, Pooya Computational Engineering, Finance, and Science Computational Finance Portfolio construction has been a long-standing topic of research in finance. The computational complexity and the time taken both increase rapidly with the number of investments in the portfolio. It becomes difficult, even impossible for classic computers to solve. Quantum computing is a new way of computing which takes advantage of quantum superposition and entanglement. It changes how such problems are approached and is not constrained by some of the classic computational complexity. Studies have shown that quantum computing can offer significant advantages over classical computing in many fields. The application of quantum computing has been constrained by the unavailability of actual quantum computers. In the past decade, there has been the rapid development of the large-scale quantum computer. However, software development for quantum computing is slow in many fields. In our study, we apply quantum computing to a multi-asset portfolio simulation. The simulation is based on historic data, covariance, and expected returns, all calculated using quantum computing. Although technically a solvable problem for classical computing, we believe the software development is important to the future application of quantum computing in finance. We conducted this study through simulation of a quantum computer and the use of Rensselaer Polytechnic Institute's IBM quantum computer. |
| title | Quantum Computing for Multi Period Asset Allocation |
| topic | Computational Engineering, Finance, and Science Computational Finance |
| url | https://arxiv.org/abs/2410.11997 |