Statistics-Informed Parameterized Quantum Circuit via Maximum Entropy Principle for Data Science and Finance
Fuente:
arXiv
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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866909226718199808 |
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| author | Zhuang, Xi-Ning Chen, Zhao-Yun Xue, Cheng Xu, Xiao-Fan Wang, Chao Liu, Huan-Yu Sun, Tai-Ping Wang, Yun-Jie Wu, Yu-Chun Guo, Guo-Ping |
| author_facet | Zhuang, Xi-Ning Chen, Zhao-Yun Xue, Cheng Xu, Xiao-Fan Wang, Chao Liu, Huan-Yu Sun, Tai-Ping Wang, Yun-Jie Wu, Yu-Chun Guo, Guo-Ping |
| contents | Quantum machine learning has demonstrated significant potential in solving practical problems, particularly in statistics-focused areas such as data science and finance. However, challenges remain in preparing and learning statistical models on a quantum processor due to issues with trainability and interpretability. In this letter, we utilize the maximum entropy principle to design a statistics-informed parameterized quantum circuit (SI-PQC) for efficiently preparing and training of quantum computational statistical models, including arbitrary distributions and their weighted mixtures. The SI-PQC features a static structure with trainable parameters, enabling in-depth optimized circuit compilation, exponential reductions in resource and time consumption, and improved trainability and interpretability for learning quantum states and classical model parameters simultaneously. As an efficient subroutine for preparing and learning in various quantum algorithms, the SI-PQC addresses the input bottleneck and facilitates the injection of prior knowledge. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_01335 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Statistics-Informed Parameterized Quantum Circuit via Maximum Entropy Principle for Data Science and Finance Zhuang, Xi-Ning Chen, Zhao-Yun Xue, Cheng Xu, Xiao-Fan Wang, Chao Liu, Huan-Yu Sun, Tai-Ping Wang, Yun-Jie Wu, Yu-Chun Guo, Guo-Ping Quantum Physics Statistical Finance Machine Learning Quantum machine learning has demonstrated significant potential in solving practical problems, particularly in statistics-focused areas such as data science and finance. However, challenges remain in preparing and learning statistical models on a quantum processor due to issues with trainability and interpretability. In this letter, we utilize the maximum entropy principle to design a statistics-informed parameterized quantum circuit (SI-PQC) for efficiently preparing and training of quantum computational statistical models, including arbitrary distributions and their weighted mixtures. The SI-PQC features a static structure with trainable parameters, enabling in-depth optimized circuit compilation, exponential reductions in resource and time consumption, and improved trainability and interpretability for learning quantum states and classical model parameters simultaneously. As an efficient subroutine for preparing and learning in various quantum algorithms, the SI-PQC addresses the input bottleneck and facilitates the injection of prior knowledge. |
| title | Statistics-Informed Parameterized Quantum Circuit via Maximum Entropy Principle for Data Science and Finance |
| topic | Quantum Physics Statistical Finance Machine Learning |
| url | https://arxiv.org/abs/2406.01335 |