Training Optimization for Gate-Model Quantum Neural Networks

Gate-based quantum computations represent an essential to realize near-term quantum computer architectures. A gate-model quantum neural network (QNN) is a QNN implemented on a gate-model quantum computer, realized via a set of unitaries with associated gate parameters. Here, we define a training opt...

Full description

Saved in:
Bibliographic Details
Published in:Scientific reports 2019-09, Vol.9 (1), p.12679-19, Article 12679
Main Authors: Gyongyosi, Laszlo, Imre, Sandor
Format: Article
Language:English
Subjects:
639/705
639/705/1042
639/766/483/481
Humanities and Social Sciences
multidisciplinary
Neural networks
Quantum computing
Science
Science (multidisciplinary)
Training
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Gate-based quantum computations represent an essential to realize near-term quantum computer architectures. A gate-model quantum neural network (QNN) is a QNN implemented on a gate-model quantum computer, realized via a set of unitaries with associated gate parameters. Here, we define a training optimization procedure for gate-model QNNs. By deriving the environmental attributes of the gate-model quantum network, we prove the constraint-based learning models. We show that the optimal learning procedures are different if side information is available in different directions, and if side information is accessible about the previous running sequences of the gate-model QNN. The results are particularly convenient for gate-model quantum computer implementations.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-019-48892-w