优化量子噪声诱导的储库计算,用于非线性和混乱的时间序列预测
Daniel Fry1, Amol Deshmukh2, Samuel Yen-Chi Chen3
1IBM Quantum, Thomas J. Watson Research Center, Yorktown Heights, NY, USA. daniel.fry@ibm.com.
Scientific reports
|November 7, 2023
概括
我们通过使用噪声作为更好的时间序列预测的资源来推进量子储库计算. 这种新的方法增强了量子机器学习模型,改善了参数化和降低了复杂性.
科学领域:
- 量子机器学习就是量子机器学习.
- 量子计算是一种量子计算.
- 非线性动力学是一种非线性动力学.
背景情况:
- 量子储库计算是时间序列预测的一个不断增长的领域.
- 现有的方法需要复杂的调整和高资源利用.
- 水库噪音往往被视为一种损害,而不是一种资源.
研究的目的:
- 开发一种使用噪声作为资源的新型量子储库计算方法.
- 为了提高量子储库的表达力和可学习性.
- 引入一种可控制的量子储库参数化和优化方法.
主要方法:
- 使用噪声诱导的量子储架构.
- 实现可调节的噪声模型用于控制的量子电路参数化.
- 减少了库存电路中的量子位数和纠复杂性.
- 将该方法应用于非线性基准,包括Mackey-Glass系统.
主要成果:
- 使用水库噪声实现了表达性,非线性信号生成.
- 通过单一线性输出层证明了有效的学习.
- 在非线性基准上获得了优异的模拟结果,即使在混乱的状态下也是如此.
- 以最小的资源向Mackey-Glass系统展示了成功的预测100步前进.
结论:
- 噪声诱导的量子储库为时间序列预测提供了一种强大而高效的方法.
- 通过可调节噪声模型进行可控制的参数化,显著提高了储水池的性能.
- 这种方法为推进量子机器学习应用提供了可扩展和有效的途径.
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