Artificial intelligence algorithms largely build on multi-layered neural networks. Coping with their increasing complexity and memory requirements calls for a paradigmatic change in the way these powerful algorithms are run. Quantum computing promises to solve certain tasks much more efficiently than any classical computing machine, and actual quantum processors are now becoming available through cloud access to perform experiments and testing also outside of research labs. Here we show in practice an experimental realization of an artificial feed-forward neural network implemented on a state-of-art superconducting quantum processor using up to 7 active qubits. The network is made of quantum artificial neurons, which individually display a potential advantage in storage capacity with respect to their classical counterpart, and it is able to carry out an elementary classification task which would be impossible to achieve with a single node. We demonstrate that this network can be equivalently operated either via classical control or in a completely coherent fashion, thus opening the way to hybrid as well as fully quantum solutions for artificial intelligence to be run on near-term intermediate-scale quantum hardware.

人工智能算法主要建立在多层神经网络上。为了应对日益增长的复杂性和内存需求，需要对这些强大算法的运行方式进行一次范式更改。量子计算有望比任何传统计算机更有效地解决某些任务，并且现在可以通过云访问使用实际的量子处理器来在研究实验室之外进行实验和测试。在这里，我们在实践中展示了人工前馈神经网络的实验实现，该网络在最新的超导量子处理器上使用多达7个活动量子位实现。该网络由量子人工神经元组成，与传统的神经元相比，它们分别在存储容量上显示出潜在的优势，并且能够执行基本的分类任务，而这是单个节点无法实现的。我们证明了该网络可以通过经典控制或完全一致的方式等效运行，从而为在短期中规模量子硬件上运行的人工智能的混合以及全量子解决方案开辟了道路。