Quantum Reservoir Computing Using Arrays of Rydberg Atoms
Quantum Reservoir Computing Using Arrays of Rydberg Atoms
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DOI：
10.1103/prxquantum.3.030325
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发表时间：
2022-08-22
期刊：
影响因子：
9.7
通讯作者：
Yelin, Susanne F.
中科院分区：
文献类型：
--
作者：
Bravo, Rodrigo Araiza;Najafi, Khadijeh;Yelin, Susanne F.
量子计算有望加速机器学习算法。然而，有噪声的中间尺度量子（NISQ）设备对实现量子机器学习（QML）优势提出了工程挑战。最近，一系列受大脑噪声容忍动力学启发的QML计算模型已经成为规避NISQ设备硬件限制的一种手段。在本文中，我们介绍了循环神经网络（RNN）的量子版本，这是一种众所周知的大脑神经回路模型。我们的量子RNN（qRNN）利用相互作用的自旋1/2粒子系综的自然哈密顿动力学作为计算手段。在哈密顿量是对角的极限中，qRNN恢复了经典版本的动力学。超过这个限制，我们观察到qRNN的量子动力学为它提供了量子计算特性，可以帮助它进行计算。为此，我们研究了一个固定几何结构的qRNN，即，量子水库计算机，基于里德堡原子阵列，并表明里德堡水库确实能够复制几个认知任务的学习，如多任务，决策和长期记忆，利用这个平台的几个关键特征，如原子间物种相互作用和量子多体疤痕。
Quantum computing promises to speed up machine-learning algorithms. However, noisy intermediate-scale quantum (NISQ) devices pose engineering challenges to realizing quantum machine-learning (QML) advantages. Recently, a series of QML computational models inspired by the noise-tolerant dynamics of the brain has emerged as a means to circumvent the hardware limitations of NISQ devices. In this paper, we introduce a quantum version of a recurrent neural network (RNN), a well-known model for neural circuits in the brain. Our quantum RNN (qRNN) makes use of the natural Hamiltonian dynamics of an ensemble of interacting spin-1/2 particles as a means for computation. In the limit where the Hamiltonian is diagonal, the qRNN recovers the dynamics of the classical version. Beyond this limit, we observe that the quantum dynamics of the qRNN provide it with quantum computational features that can aid it in computation. To this end, we study a fixed-geometry qRNN, i.e., a quantum reservoir computer, based on arrays of Rydberg atoms and show that the Rydberg reservoir is indeed capable of replicating the learning of several cognitive tasks such as multitasking, decision making, and long-term memory by taking advantage of several key features of this platform such as interatomic species interactions and quantum many-body scars.