Data-driven Quantum Dynamical Embedding Method for Long-term Prediction on Near-term Quantum Computers
| Autor: | Sun, Tai-Ping, Chen, Zhao-Yun, Xue, Cheng, Liu, Huan-Yu, Zhuang, Xi-Ning, Wang, Yun-Jie, Ma, Shi-Xin, Zhang, Hai-Feng, Wu, Yu-Chun, Guo, Guo-Ping |
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| Rok vydání: | 2023 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | The increasing focus on long-term time series prediction across various fields has been significantly strengthened by advancements in quantum computation. In this paper, we introduce a data-driven method designed for long-term time series prediction with quantum dynamical embedding (QDE). This approach enables a trainable embedding of the data space into an extended state space, allowing for the recursive retrieval of time series information. Based on its independence of time series length, this method achieves depth-efficient quantum circuits that are crucial for near-term quantum computers. Numerical simulations demonstrate the model's improved performance in prediction accuracy and resource efficiency over existing methods, as well as its effective denoising capabilities. We implement this model on the Origin ''Wukong'' superconducting quantum processor with a learnable error-cancellation layer (LECL) for error mitigation, further validating the practical applicability of our approach on near-term quantum devices. Furthermore, the theoretical analysis of the QDE's dynamical properties and its universality enhances its potential for time series prediction. This study establishes a significant step towards the processing of long-term time series on near-term quantum computers, integrating data-driven learning with discrete dynamical embedding for enhanced forecasting capabilities. Comment: 20 pages, 12 figures |
| Databáze: | arXiv |
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