论文标题
连续变量量子系统的高斯州提供通用和多功能的储层计算
Gaussian states of continuous-variable quantum systems provide universal and versatile reservoir computing
论文作者
论文摘要
我们确定了用于机器学习任务的线性动力系统连续变量高斯状态的潜力。具体来说，我们考虑储层计算，这是一个用于在线时间序列处理的有效框架。作为储层，我们考虑了量子谐波网络建模，例如线性量子光学系统。我们证明，与通用量子计算不同，无需非高斯资源就可以实现通用储层计算。我们发现，将输入时间序列编码到高斯状态既是调整整体输入输出映射的非线性的一种来源，也是一种手段。我们进一步表明，可以通过编码量子波动（例如挤压真空）而不是经典的强度磁场或热波动来实现所提出模型的全部电位。我们的结果介绍了一个新的研究范式，用于储层计算，利用量子系统的动力学和高斯量子状态的工程，将两个领域都推向新方向。
We establish the potential of continuous-variable Gaussian states of linear dynamical systems for machine learning tasks. Specifically, we consider reservoir computing, an efficient framework for online time series processing. As a reservoir we consider a quantum harmonic network modeling e.g. linear quantum optical systems. We prove that unlike universal quantum computing, universal reservoir computing can be achieved without non-Gaussian resources. We find that encoding the input time series into Gaussian states is both a source and a means to tune the nonlinearity of the overall input-output map. We further show that the full potential of the proposed model can be reached by encoding to quantum fluctuations, such as squeezed vacuum, instead of classical intense fields or thermal fluctuations. Our results introduce a new research paradigm for reservoir computing harnessing the dynamics of a quantum system and the engineering of Gaussian quantum states, pushing both fields into a new direction.
