An Invitation to Distributed Quantum Neural Networks
- URL: http://arxiv.org/abs/2211.07056v1
- Date: Mon, 14 Nov 2022 00:27:01 GMT
- Title: An Invitation to Distributed Quantum Neural Networks
- Authors: Lirand\"e Pira, Chris Ferrie
- Abstract summary: We review the current state of the art in distributed quantum neural networks.
We find that the distribution of quantum datasets shares more similarities with its classical counterpart than does the distribution of quantum models.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Deep neural networks have established themselves as one of the most promising
machine learning techniques. Training such models at large scales is often
parallelized, giving rise to the concept of distributed deep learning.
Distributed techniques are often employed in training large models or large
datasets either out of necessity or simply for speed. Quantum machine learning,
on the other hand, is the interplay between machine learning and quantum
computing. It seeks to understand the advantages of employing quantum devices
in developing new learning algorithms as well as improving the existing ones. A
set of architectures that are heavily explored in quantum machine learning are
quantum neural networks. In this review, we consider ideas from distributed
deep learning as they apply to quantum neural networks. We find that the
distribution of quantum datasets shares more similarities with its classical
counterpart than does the distribution of quantum models, though the unique
aspects of quantum data introduces new vulnerabilities to both approaches. We
review the current state of the art in distributed quantum neural networks,
including recent numerical experiments and the concept of circuit cutting.
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