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arXiv:2206.11509 · IEEE/ACM Symposium on Edge Computing (SEC)

On Classifying Images using Quantum Image Representation

Ankit Khandelwal, M Girish Chandra, Sayantan Pramanik

Published Dec 1, 2022• 6 citations
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Abstract

Quantum Image Representation is researched from last few years, and more active in the recent past. Set to examine how these representations would be useful for Image Processing in a quantum way, we considered the Quantum Machine Learning problem of image classification in this paper. Encouraging results have been provided on classifying benchmark datasets of grayscale and colour images using two different classifiers and their combination. Multiclass classification performance has also been tested.

DOI
10.1109/sec54971.2022.00067

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Quantum Image Representation is researched from last few years, and more active in the recent past. Set to examine how these representations would be useful for Image Processing in a quantum way, we considered the Quantum Machine Learning problem of image classification in this paper. Encouraging results have been provided on classifying benchmark datasets of grayscale and colour images using two different classifiers and their combination. Multiclass classification performance has also been tested.
Interest in quantum machine learning is growing due to its potential to offer more efficient solutions for problems that are difficult to tackle with classical methods. In this context, the research work presented here focuses on the use of quantum machine-learning techniques for image-classification tasks. We exploit a quantum extreme learning machine by taking advantage of its rich feature map provided by the quantum reservoir substrate. We systematically analyze different phases of the quantum extreme learning machine process, from dataset preparation to final image classification. In particular, we test different encodings, together with principal component analysis and the use of autoencoders, and we examine the dynamics of the model through the use of different Hamiltonians for the quantum reservoir. Our results show that the introduction of a quantum reservoir systematically improves the accuracy of the classifier. In addition, our findings indicate that variations in encoding methods can significantly influence performance and that Hamiltonians with distinct structures exhibit the same discrimination rate, depending on how their eigenstates are related to the encoding and measurement basis.
Quantum Computing and especially Quantum Machine Learning, in a short period of time, has gained a lot of interest through research groups around the world. This can be seen in the increasing number of proposed models for pattern classification applying quantum principles to a certain degree. Despise the increasing volume of models, there is a void in testing these models on real datasets and not only on synthetic ones. The objective of this work is to classify patterns with binary attributes using a quantum classifier. Specially, we show results of a complete quantum classifier applied to image datasets. The experiments show favorable output while dealing with balanced classification problems as well as with imbalanced classes where the minority class is the most relevant. This is promising in medical areas, where usually the important class is also the minority class.
Quantum machine learning, an important element of quantum computing, recently has gained research attention around the world. In this paper, we have proposed a quantum machine learning model to classify images using a quantum classifier. We exhibit the results of a comprehensive quantum classifier with transfer learning applied to image datasets in particular. The work uses hybrid transfer learning technique along with the classical pre-trained network and variational quantum circuits as their final layers on a small scale of dataset. The implementation is carried out in a quantum processor of a chosen set of highly informative functions using PennyLane a cross-platform software package for using quantum computers to evaluate the high-resolution image classifier. The performance of the model proved to be more accurate than its counterpart and outperforms all other existing classical models in terms of time and competence.
Multiclass image classification is considered a challenging task in computer vision that requires correctly classifying an image into one of the multiple distinct groups.In recent years, quantum machine learning has emerged as a topic of significant interest among researchers.Using quantum concepts such as superposition and entanglement, quantum machine learning algorithms provide a more efficient method of processing and classifying high-dimensional image data.This paper proposes a new image classification model using quantum-inspired convolutional neural network architecture or, shortly, QCNN.The proposed model consists of two main phases; pre-processing and classification based on the QCNN phase.Seven benchmark datasets with different characteristics are adopted to evaluate the performance of the proposed model.The experimental results revealed that the proposed QCNN outperformed its classical version.Additionally, the results demonstrated the effectiveness of the proposed model compared with the state-of-the-art models.
In recent years, there has been a growing interest in leveraging the unique properties of quantum computing to develop novel machine learning algorithms and architectures. This research paper presents an investigation of quantum convolutional neural networks (QCNNs), which leverage the unique properties of quantum computing to potentially improve the accuracy and efficiency of image classification tasks. Specifically, the paper explores three different QCNN architectures, including a pure quantum-based QCNN, a hybrid QCNN with a single quantum convolution layer, and a hybrid convolutional architecture with multiple quantum filters. We tested the models on MNIST dataset and the results of the study demonstrate that hybrid architectures that combine quantum and classical processing are more effective than pure quantum-based architectures in image classification tasks. In particular, the third model, the Hybrid Convolution with Multiple Quantum Filters, achieved the highest test set accuracy of 92.7%. The use of multiple quantum filters in conjunction with a classical neural network resulted in enhanced accuracy and efficiency in image classification tasks, highlighting the potential of hybrid architectures for future applications in machine learning tasks.
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