Statistics-Informed Parameterized Quantum Circuit via Maximum Entropy Principle for Data Science and Finance

• URL: http://arxiv.org/abs/2406.01335v2
• Date: Tue, 18 Jun 2024 12:40:44 GMT
• Title: Statistics-Informed Parameterized Quantum Circuit via Maximum Entropy Principle for Data Science and Finance
• Authors: Xi-Ning Zhuang, Zhao-Yun Chen, Cheng Xue, Xiao-Fan Xu, Chao Wang, Huan-Yu Liu, Tai-Ping Sun, Yun-Jie Wang, Yu-Chun Wu, Guo-Ping Guo,
• Abstract summary: We utilize the maximum entropy principle to design a statistics-informed parameterized quantum circuit (SI-PQC) The SI-PQC features a static structure with trainable parameters, enabling in-depth optimized circuit compilation. As an efficient subroutine for preparing and learning in various quantum algorithms, the SI-PQC addresses the input bottleneck.
• Score: 5.3518921884603
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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