Cost function embedding and dataset encoding for machine learning with parameterized quantum circuits

Machine learning is seen as a promising application of quantum computation. For near-term noisy intermediate-scale quantum (NISQ) devices, parametrized quantum circuits (PQCs) have been proposed as machine learning models due to their robustness and ease of implementation. However, the cost function...

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Bibliographic Details
Published in:arXiv.org 2019-10
Main Authors: Cao, Shuxiang, Wossnig, Leonard, Vlastakis, Brian, Leek, Peter, Grant, Edward
Format: Article
Language:English
Subjects:
Artificial intelligence
Circuits
Cost function
Datasets
Machine learning
Parameterization
Quantum computers
Quantum computing
Online Access:Get full text
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Summary:Machine learning is seen as a promising application of quantum computation. For near-term noisy intermediate-scale quantum (NISQ) devices, parametrized quantum circuits (PQCs) have been proposed as machine learning models due to their robustness and ease of implementation. However, the cost function is normally calculated classically from repeated measurement outcomes, such that it is no longer encoded in a quantum state. This prevents the value from being directly manipulated by a quantum computer. To solve this problem, we give a routine to embed the cost function for machine learning into a quantum circuit, which accepts a training dataset encoded in superposition or an easily preparable mixed state. We also demonstrate the ability to evaluate the gradient of the encoded cost function in a quantum state.
ISSN:2331-8422
DOI:10.48550/arxiv.1910.03902