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.

翻译：神经网络在工业界和学术界均取得了突破性进展。如何在量子计算设备上有效开发神经网络，仍是一个具有挑战性的开放问题。本文提出了一种面向量子神经计算的新型量子神经网络模型，该模型利用（经典控制的）单量子比特操作和测量，在具有天然环境诱导退相干的真实量子系统上实现，极大降低了物理实现的难度。该模型避免了状态空间规模随神经元数量呈指数增长的问题，从而大幅降低内存需求，并可借助传统优化算法实现快速优化。我们对手写数字识别及其他非线性分类任务进行了基准测试，结果表明该模型具有卓越的非线性分类能力和噪声鲁棒性。此外，该模型使量子计算能够在更广泛的场景中得到应用，并有望比标准量子计算机更早推动量子神经计算机的发展。