Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Leveraging Data Locality in Quantum Convolutional Classifiers

Mingyoung Jeng
,
Alvir Nobel
ORCID logo ,
Vinayak Jha
,
David Levy
ORCID logo ,
Dylan Kneidel
,
Manu Chaudhary
,
Ishraq Islam
,
Audrey Facer
,
Manish Singh
,
Evan Baumgartner
,
Eade Vanderhoof
,
Abina Arshad
,
Esam El-Araby
* ORCID logo
Version 1 : Received: 18 March 2024 / Approved: 18 March 2024 / Online: 18 March 2024 (15:28:57 CET)

How to cite: Jeng, M.; Nobel, A.; Jha, V.; Levy, D.; Kneidel, D.; Chaudhary, M.; Islam, I.; Facer, A.; Singh, M.; Baumgartner, E.; Vanderhoof, E.; Arshad, A.; El-Araby, E. Leveraging Data Locality in Quantum Convolutional Classifiers. Preprints 2024, 2024031060. https://doi.org/10.20944/preprints202403.1060.v1 Jeng, M.; Nobel, A.; Jha, V.; Levy, D.; Kneidel, D.; Chaudhary, M.; Islam, I.; Facer, A.; Singh, M.; Baumgartner, E.; Vanderhoof, E.; Arshad, A.; El-Araby, E. Leveraging Data Locality in Quantum Convolutional Classifiers. Preprints 2024, 2024031060. https://doi.org/10.20944/preprints202403.1060.v1

Abstract

Quantum computing (QC) has opened the door to advancements in machine learning (ML) tasks that are currently implemented in the classical domain. Convolutional neural networks (CNNs) are classical ML architectures that exploit data locality and possess a simpler structure than fully-connected multi-layer perceptrons (MLPs) without compromising the accuracy of classification. However, the concept of preserving data locality is usually overlooked in the existing quantum counterparts of CNNs, particularly for extracting multi-features in multidimensional data. In this paper, we present a multidimensional quantum convolutional classifier (MQCC) that performs multidimensional and multi-feature quantum convolution with average and Euclidean pooling and thus adapting the CNN structure to a variational quantum algorithm (VQA). Average pooling is based on the quantum Haar transform (QHT) and Euclidean pooling is based on partial quantum measurement. The experimental work was conducted using multidimensional data to validate the correctness and demonstrate the scalability of the proposed method utilizing both noisy and noise-free quantum simulations. We evaluated the MQCC model with reference to reported work on state-of-the-art quantum simulators from IBM Quantum and Xanadu using a variety of standard ML datasets. The experimental results showed favorable characteristics of our proposed techniques compared to existing work with respect to a number of quantitative metrics such as the number of training parameters, cross-entropy loss, classification accuracy, circuit depth, and quantum gate count.

Keywords

quantum computing; convolutional neural networks; quantum machine learning; variational quantum algorithms

Subject

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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