论文标题
在量子计算机上模拟循环突触和神经形态计算
Simulation of memristive synapses and neuromorphic computing on a quantum computer
论文作者
论文摘要
神经形态计算的主要方法之一是将回忆录用作模拟突触。我们提出了一种统一的量子门，表现出复杂行为，包括欧姆定律，滞留滞后环和突触可塑性。观察到编码量子状态的量子相和长期可塑性，滞后。我们还提出了一个具有通用量子计算能力的三层神经网络。证明了在熟悉的神经网络上的量子状态分类。我们的结果铺平了通向脑启发的量子计算的道路。我们通过在超导量子计算机IBMQ_VIGO上进行数值模拟和实验获得这些结果。
One of the major approaches to neuromorphic computing is using memristors as analogue synapses. We propose unitary quantum gates that exhibit memristive behaviours, including Ohm's law, pinched hysteresis loop and synaptic plasticity. Hysteresis depending on the quantum phase and long-term plasticity that encodes the quantum state are observed. We also propose a three-layer neural network with the capability of universal quantum computing. Quantum state classification on the memristive neural network is demonstrated. Our results pave the way towards brain-inspired quantum computing. We obtain these results in numerical simulations and experiments on the superconducting quantum computer ibmq_vigo.
