Quantum neural networks with multi-qubit potentials

TL;DR

This paper introduces quantum neural networks with multi-qubit interactions that reduce network depth and enhance efficiency in complex information processing tasks, aiding scalability and training.

Contribution

It presents a novel quantum neural network architecture incorporating multi-qubit potentials, improving efficiency and scalability over existing models.

Findings

Enables efficient XOR gate implementation

Facilitates prime number search

Reduces network depth for entangling gates

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 multi-qubit 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.