论文标题
神经形态量子计算
Neuromorphic quantum computing
论文作者
论文摘要
我们建议神经形态计算可以执行量子操作。活跃状态或沉默状态中的尖刺神经元与伊辛旋转的两个状态有关。量子密度矩阵是由伊辛自旋的期望值和相关性构建的。作为迈向量子计算的一步，我们为两个量子系统显示，可以学习量子门作为神经网络动力学的参数的变化。我们对概率计算的建议超出了基于过渡概率的马尔可夫链。对经典概率分布的约束将系统一部分的变化与其他部分相关，类似于纠缠量子系统。
We propose that neuromorphic computing can perform quantum operations. Spiking neurons in the active or silent states are connected to the two states of Ising spins. A quantum density matrix is constructed from the expectation values and correlations of the Ising spins. As a step towards quantum computation we show for a two qubit system that quantum gates can be learned as a change of parameters for neural network dynamics. Our proposal for probabilistic computing goes beyond Markov chains, which are based on transition probabilities. Constraints on classical probability distributions relate changes made in one part of the system to other parts, similar to entangled quantum systems.
