论文标题
尖峰神经形态芯片学习纠缠量子状态
Spiking neuromorphic chip learns entangled quantum states
论文作者
论文摘要
在过去的几年中，用人工神经网络的量子状态近似引起了很多关注。同时，受生物大脑的结构和动力学特性的启发，模拟神经形态芯片在运行人工神经网络体系结构方面具有高能量效率，以获得生成应用的利润。这鼓励使用此类硬件系统作为量子系统模拟的平台。在这里，我们使用最新的基于Spike的Brainscales硬件报告了原型的实现，从而使我们能够代表具有高忠诚度的最大纠结量子状态。模拟硬件很好地捕获了纯和混合双Quit状态的铃铛相关性，这表明了使用尖峰神经形态芯片模拟量子系统的重要构建块。
The approximation of quantum states with artificial neural networks has gained a lot of attention during the last years. Meanwhile, analog neuromorphic chips, inspired by structural and dynamical properties of the biological brain, show a high energy efficiency in running artificial neural-network architectures for the profit of generative applications. This encourages employing such hardware systems as platforms for simulations of quantum systems. Here we report on the realization of a prototype using the latest spike-based BrainScaleS hardware allowing us to represent few-qubit maximally entangled quantum states with high fidelities. Bell correlations of pure and mixed two-qubit states are well captured by the analog hardware, demonstrating an important building block for simulating quantum systems with spiking neuromorphic chips.
