在IBM设备上用于量子时间序列预测的浅交错电路
Mostafizur Rahaman Laskar1, Richa Goel2
1IBM Quantum, IBM Research Lab, Bangalore, India. m.rahaman93@gmail.com.
Scientific reports
|December 12, 2025
概括
量子纠为时间序列分析提供了一种新的方法. 这个量子时间序列框架使用浅量子电路从有限的数据中有效地学习时间模式,优于经典方法.
科学领域:
- 量子计算是一种量子计算.
- 机器学习 机器学习
- 时间序列分析时间序列分析
背景情况:
- 预测时间动态在科学和工程领域至关重要.
- 像神经网络这样的经典方法在计算上昂贵,需要大量的数据集.
- 现有的量子方法通常涉及深度电路和高参数计数.
研究的目的:
- 研究量子纠作为时间模式学习的资源.
- 使用浅量子电路开发一个量子时间序列 (QTS) 框架.
- 探索用于时间序列分析的硬件高效编码和纠方案.
主要方法:
- 提出了一个量子时间序列 (QTS) 框架,将序列数据编码为单量子位旋转.
- 利用前方和交叉纠层来捕捉时间相关性.
- 实现基于相位编码的稀疏纠,以提高硬件效率.
- 使用IBM量子处理器对合成和地球物理数据集进行实验.
主要成果:
- 浅层QTS电路成功地从有限的数据中重现了复杂的时间模式.
- 基于相位编码的稀疏纠证明了线性电路深度和高效的两量子比特复杂性.
- 在QTS框架显示了强度和可扩展性,高达100量子比特.
- 在参数和深度方面表现优于深度变量量子电路.
结论:
- 结构化量子纠可以作为时间模式学习的资源.
- QTS框架为时间序列分析中的近期量子应用提供了一个可扩展的路线.
- 量子纠可以为高效的时间序列预测提供短期记忆效应.
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