Abstract
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.
Highlights
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
The field of artificial intelligence was revolutionized by moving from the simple, single layer perceptron design [1] to that of a complete feed-forward neural network, constituted by several neurons organized in multiple successive layers [2, 3]
In this work we have presented an original architecture to build feed-forward neural networks on universal quantum computing hardware and demonstrated the use of them in Noisy Intermediate-Scale Quantum (NISQ) devices
Summary
The field of artificial intelligence was revolutionized by moving from the simple, single layer perceptron design [1] to that of a complete feed-forward neural network (ffNN), constituted by several neurons organized in multiple successive layers [2, 3]. We start from a hybrid approach combining quantum nodes with classical information feed-forward, obtained via classical control of unitary transformations on qubits This design realizes a fully general implementation of a ffNN on a quantum processor assisted by classical registers. The proposed quantum implementation of ffNN offers interesting perspectives on scalability already in the Noisy Intermediate-Scale Quantum (NISQ) [24] regime: the single quantum nodes potentially feature exponential advantage in memory usage, allowing to manipulate high-dimensional data structures with intermediate-size quantum registers, in principle. The hybrid nature of the ffNN itself suggests a seamless integration with existing classical structures and algorithms for neural network computation and machine learning [25]
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