An Invitation to Distributed Quantum Neural Networks

By Lirande Pira et al

Published on Nov. 15, 2022

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Table of Contents

I. Introduction
II. Distributed Deep learning
A. A Brief History of Deep Neural Networks
B. Distributed Deep Learning
III. Essential Quantum Computing
A. Fundamental concepts
B. Distributed Quantum Computing
IV. Quantum Machine Learning
A. A brief history of QML
B. Quantum Neural Networks
V. Data Parallelism: Splitting the Dataset
A. Basis encoding with data distribution
B. Amplitude encoding with data distribution
C. Data parallelism discussion
VI. Model Parallelism: Splitting the Model
A. Incoherent versus coherent splitting
B. Model parallelism discussion
VII. Discussion
A. Related techniques
B. NISQ and beyond
C. Software tools
D. Closing remarks
References

Summary

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. Quantum machine learning seeks to understand the advantages of employing quantum devices in developing new learning algorithms. In this review, the authors consider ideas from distributed deep learning as they apply to quantum neural networks. They find that the distribution of quantum datasets shares more similarities with its classical counterpart than does the distribution of quantum models. The paper reviews the current state of the art in distributed quantum neural networks, including recent numerical experiments and the concept of circuit cutting.