Quantum neural networks with multi-qubit potentials

Abstract

We propose quantum neural networks that include multi-qubit interactions in the neural potential leading to a reduction of the network depth without losing approximative power. We show that the presence of multiqubit potentials in the quantum perceptrons enables more efficient information processing tasks such as XOR gate implementation and prime numbers search, while it also provides a depth reduction to construct distinct entangling quantum gates like CNOT, Toffoli, and Fredkin. This simplification in the network architecture paves the way to address the connectivity challenge to scale up a quantum neural network while facilitates its training.

Realization of a quantum perceptron gate with trapped ions

“…In this letter, we implement a fully tunable quantum perceptron gate, the fundamental unit for the design of artificial quantum neural networks, in which quantum interactions between qubits give rise to a sigmoidal behaviour. There are various theoretical designs of quantum perceptrons [16,23,24,31,32], of which we follow Ref. [24].…”