论文标题
量子神经形态计算
Quantum neuromorphic computing
论文作者
论文摘要
量子神经形态计算在脑启发的量子硬件中物理实现神经网络，以加快其计算。在这篇观点文章中，我们表明，这种新兴范式可以充分利用现有和将来的中间大小量子计算机。某些方法基于参数化的量子电路，并使用神经网络启发的算法对其进行训练。其他方法更接近经典的神经形态计算，利用了量子振荡器组件的物理特性来模仿神经元和计算。我们讨论了具有数字和模拟电路的量子神经形态网络的不同实现，突出了它们各自的优势，并回顾了令人兴奋的最新实验结果。
Quantum neuromorphic computing physically implements neural networks in brain-inspired quantum hardware to speed up their computation. In this perspective article, we show that this emerging paradigm could make the best use of the existing and near future intermediate size quantum computers. Some approaches are based on parametrized quantum circuits, and use neural network-inspired algorithms to train them. Other approaches, closer to classical neuromorphic computing, take advantage of the physical properties of quantum oscillator assemblies to mimic neurons and compute. We discuss the different implementations of quantum neuromorphic networks with digital and analog circuits, highlight their respective advantages, and review exciting recent experimental results.
