Quantum neural networks with multi-qubit potentials

Yue Ban; E. Torrontegui; J. Casanova

doi:10.1038/s41598-023-35867-1

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arXiv · 2105.02756

Quantum neural networks with multi-qubit potentials

Yue Ban·E. Torrontegui·J. Casanova

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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.

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BibTeX RIS

Yue Ban, E. Torrontegui, J. Casanova. 2021-05-06. Quantum neural networks with multi-qubit potentials. https://doi.org/10.1038/s41598-023-35867-1

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