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A Quantum Convolutional Neural Network for Image Classification

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  • Milind Upadhyay Mountain View High School

DOI:

https://doi.org/10.58445/rars.30

Keywords:

Quantum Computing, Quantum Machine Learning, Image Classification

Abstract

From self-driving cars to medical diagnoses, machine learning (ML) has revolutionized our world in the last few decades. Additionally, Quantum Computing has considerable promise for the future, with superposition and entanglement making quantum algorithms much more efficient and effective than their classical counterparts. Quantum ML aims to apply these quantum principles to the groundbreaking field of ML, such as for image classification. Convolutional Neural Networks (CNNs) are very effective for classification, so we study a Quantum Convolutional Neural Network (QCNN) that is trained to distinguish between handwritten numbers in the MNIST dataset and compared to a similar-sized classical network. After tuning the QCNN and quantum encoding, the QCNN achieved comparable accuracy to the classical network.

References

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Farhi, E. & Neven, H. Classification with Quantum Neural Networks on Near Term Processors (2018). https://arxiv.org/pdf/1802.06002.pdf

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LeCun, Y. & Cortes, C. MNIST handwritten digit database (2010). http://yann.lecun.com/exdb/mnist/

Mathur, N. et al. Medical image classification via quantum neural networks (2021). https://arxiv.org/pdf/2109.01831.pdf

Nielsen, M. CLUSTER-STATE QUANTUM COMPUTATION (2005). https://arxiv.org/pdf/quant-ph/0504097.pdf

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Posted

2022-10-11 — Updated on 2022-12-23

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