Quantum Machine Learning: Recent Advances and Outlook

O'Quinn, Wesley; Mao, Shiwen

doi:10.1109/mwc.001.1900341

Cited by 14 publications

(1 citation statement)

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“…The application of quantum computing to the tasks of machine learning, herein referred as quantum machine learning, has attracted much research attention in recent years. Literature surveys on quantum machine learning can be found in [1,10,4]. Among many proposals to combine classical machine learning methods with quantum computing, quanvolutional neural network (QNN) proposed by Henderson et.…”

Section: Introductionmentioning

confidence: 99%

Accurate Image Multi-Class Classification Neural Network Model with Quantum Entanglement Approach

Riaz

Abdulla

Suzuki³

et al. 2023

Sensors

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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.

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Section: Introductionmentioning

confidence: 99%