Neural networks have achieved impressive breakthroughs in both industry and
academia. How to effectively develop neural networks on quantum computing
devices is a challenging open problem. Here, we propose a new quantum neural
network model for quantum neural computing using (classically-controlled)
single-qubit operations and measurements on real-world quantum systems with
naturally occurring environment-induced decoherence, which greatly reduces the
difficulties of physical implementations. Our model circumvents the problem
that the state-space size grows exponentially with the number of neurons,
thereby greatly reducing memory requirements and allowing for fast optimization
with traditional optimization algorithms. We benchmark our model for
handwritten digit recognition and other nonlinear classification tasks. The
results show that our model has an amazing nonlinear classification ability and
robustness to noise. Furthermore, our model allows quantum computing to be
applied in a wider context and inspires the earlier development of a quantum
neural computer than standard quantum computers.

本研究提出了一种新的量子神经网络模型，使用实际存在的环境诱导的退相干的量子系统上的 (经典控制的) 单量子比特操作和测量，从而极大地降低了物理实现的难度。该模型在手写数字识别和其他非线性分类任务中具有优异的非线性分类能力和对噪声的鲁棒性。