Quantum reservoir processing
Quantum reservoir processing
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DOI：
10.1038/s41534-019-0149-8
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发表时间：
2019-04-29
影响因子：
7.6
通讯作者：
Liew, Timothy C. H.
中科院分区：
文献类型：
--
作者：
Ghosh, Sanjib;Opala, Andrzej;Liew, Timothy C. H.
人工智能和量子信息的同时兴起为创建量子神经网络等跨学科技术提供了机会。这里介绍的量子储层处理是一个基于储层计算原理开发的量子信息处理平台，储层计算是人工神经网络的一种形式。量子库处理器可以执行定性任务，如识别纠缠的量子态，以及定量任务，如估计输入量子态的非线性函数（例如，熵、纯度或对数负性）。通过这种方式，需要测量多个可观测量的实验方案可以简化为在训练的量子库处理器上测量一个可观测量。
The concurrent rise of artificial intelligence and quantum information poses an opportunity for creating interdisciplinary technologies like quantum neural networks. Quantum reservoir processing, introduced here, is a platform for quantum information processing developed on the principle of reservoir computing that is a form of an artificial neural network. A quantum reservoir processor can perform qualitative tasks like recognizing quantum states that are entangled as well as quantitative tasks like estimating a nonlinear function of an input quantum state (e.g., entropy, purity, or logarithmic negativity). In this way, experimental schemes that require measurements of multiple observables can be simplified to measurement of one observable on a trained quantum reservoir processor.