Improved Financial Forecasting via Quantum Machine Learning
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866916191721750528 |
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| author | Thakkar, Sohum Kazdaghli, Skander Mathur, Natansh Kerenidis, Iordanis Ferreira-Martins, André J. Brito, Samurai |
| author_facet | Thakkar, Sohum Kazdaghli, Skander Mathur, Natansh Kerenidis, Iordanis Ferreira-Martins, André J. Brito, Samurai |
| contents | Quantum algorithms have the potential to enhance machine learning across a variety of domains and applications. In this work, we show how quantum machine learning can be used to improve financial forecasting. First, we use classical and quantum Determinantal Point Processes to enhance Random Forest models for churn prediction, improving precision by almost 6%. Second, we design quantum neural network architectures with orthogonal and compound layers for credit risk assessment, which match classical performance with significantly fewer parameters. Our results demonstrate that leveraging quantum ideas can effectively enhance the performance of machine learning, both today as quantum-inspired classical ML solutions, and even more in the future, with the advent of better quantum hardware. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_12965 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Improved Financial Forecasting via Quantum Machine Learning Thakkar, Sohum Kazdaghli, Skander Mathur, Natansh Kerenidis, Iordanis Ferreira-Martins, André J. Brito, Samurai Statistical Finance Machine Learning Quantum Physics Quantum algorithms have the potential to enhance machine learning across a variety of domains and applications. In this work, we show how quantum machine learning can be used to improve financial forecasting. First, we use classical and quantum Determinantal Point Processes to enhance Random Forest models for churn prediction, improving precision by almost 6%. Second, we design quantum neural network architectures with orthogonal and compound layers for credit risk assessment, which match classical performance with significantly fewer parameters. Our results demonstrate that leveraging quantum ideas can effectively enhance the performance of machine learning, both today as quantum-inspired classical ML solutions, and even more in the future, with the advent of better quantum hardware. |
| title | Improved Financial Forecasting via Quantum Machine Learning |
| topic | Statistical Finance Machine Learning Quantum Physics |
| url | https://arxiv.org/abs/2306.12965 |