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Published VersionCC BY V4.0, Open Access
Journal article
Accurate Image Multi-Class Classification Neural Network Model with Quantum Entanglement Approach
Sensors, Vol.23(5), pp.1-11
2023
PMID: 36904951
Abstract
Quantum machine learning (QML) has attracted significant research attention over the last decade. Multiple models have been developed to demonstrate the practical applications of the quantum properties. In this study, we first demonstrate that the previously proposed quanvolutional neural network (QuanvNN) using a randomly generated quantum circuit improves the image classification accuracy of a fully connected neural network against the Modified National Institute of Standards and Technology (MNIST) dataset and the Canadian Institute for Advanced Research 10 class (CIFAR-10) dataset from 92.0% to 93.0% and from 30.5% to 34.9%, respectively. We then propose a new model referred to as a Neural Network with Quantum Entanglement (NNQE) using a strongly entangled quantum circuit combined with Hadamard gates. The new model further improves the image classification accuracy of MNIST and CIFAR-10 to 93.8% and 36.0%, respectively. Unlike other QML methods, the proposed method does not require optimization of the parameters inside the quantum circuits; hence, it requires only limited use of the quantum circuit. Given the small number of qubits and relatively shallow depth of the proposed quantum circuit, the proposed method is well suited for implementation in noisy intermediate-scale quantum computers. While promising results were obtained by the proposed method when applied to the MNIST and CIFAR-10 datasets, a test against a more complicated German Traffic Sign Recognition Benchmark (GTSRB) dataset degraded the image classification accuracy from 82.2% to 73.4%. The exact causes of the performance improvement and degradation are currently an open question, prompting further research on the understanding and design of suitable quantum circuits for image classification neural networks for colored and complex data.
Details
- Title
- Accurate Image Multi-Class Classification Neural Network Model with Quantum Entanglement Approach
- Authors
- Farina Riaz (Corresponding Author) - University of Southern QueenslandShahab Abdulla - University of Southern QueenslandHajime Suzuki - Commonwealth Scientific and Industrial Research OrganisationSrinjoy Ganguly - University of Southern QueenslandRavinesh C Deo - University of Southern QueenslandSusan Hopkins - University of Southern Queensland
- Publication details
- Sensors, Vol.23(5), pp.1-11
- Publisher
- MDPI AG
- Date published
- 2023
- DOI
- 10.3390/s23052753
- ISSN
- 1424-8220
- PMID
- 36904951
- Copyright note
- © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).
- Data Availability
- The data presented in this study are available on request from the corresponding author.
- Grant note
- This research was funded by Australian Government Research Training Program and Commonwealth Scientific Industrial and Research Organisation.
- Organisation Unit
- School of Education and Tertiary Access
- Language
- English
- Record Identifier
- 991135205502621
- Output Type
- Journal article
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- Collaboration types
- Domestic collaboration
- Web Of Science research areas
- Chemistry, Analytical
- Engineering, Electrical & Electronic
- Instruments & Instrumentation