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Известия Южного федерального университета. Технические науки

QUANTUM DEEP LEARNING OF CONVOLUTIONAL NEURAL NETWORK USING VARIATIONAL QUANTUM CIRCUIT

S.M. Gushanskiy, V.E. Buglov

Published Mar 1, 2022• 1 citation
Publisher

Abstract

Quantum computing in general and quantum deep learning represent a promising field relatedto the research of modern methods and algorithms of quantum computing used for the purposeof teaching and developing new architectures of artificial neural networks. Recently, therehas been a trend that research conducted in the field of quantum deep learning is becoming increasinglywidespread among specialists. This can be explained by the fact that it has been establishedthat quantum circuits are capable of functioning like artificial neural networks, whiledemonstrating the best results in solving several tasks, including, for example, the actual task ofclassifying objects in an image or in a video stream. Thanks to the rapid development of quantumcomputing in the field of deep learning, optimal solutions have been found for such urgent problemsas the vanishing gradient problem, finding a local minimum, improving the efficiency oflarge-scale parametric machine learning algorithms, eliminating decoherence and quantum errors,etc. Within the framework of this work, the process of functioning of a quantum variationalscheme is described, its main characteristics are established, and disadvantages are identified.The key features of quantum computing, on which the process of implementing quantum deeplearning with the reinforcement of a convolutional neural network is based, are also analyzed. Inaddition, quantum deep learning of a convolutional neural network has been carried out using avariational quantum scheme, which leads to an increase in the performance of a convolutionalneural network in solving the problem of image processing, namely its classification, using aquantum computing environment. The relevance of this article consists in the implementation of aquantum deep learning algorithm with the reinforcement of a convolutional neural network forimage processing, as well as the great importance of the subject of this study for the future developmentof quantum computing devices that can be used in artificial intelligence systems, etc.,which corresponds to the priority direction of the development of domestic science.

DOI
10.18522/2311-3103-2021-7-167-177

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Quantum computing in general and quantum deep learning represent a promising field relatedto the research of modern methods and algorithms of quantum computing used for the purposeof teaching and developing new architectures of artificial neural networks. Recently, therehas been a trend that research conducted in the field of quantum deep learning is becoming increasinglywidespread among specialists. This can be explained by the fact that it has been establishedthat quantum circuits are capable of functioning like artificial neural networks, whiledemonstrating the best results in solving several tasks, including, for example, the actual task ofclassifying objects in an image or in a video stream. Thanks to the rapid development of quantumcomputing in the field of deep learning, optimal solutions have been found for such urgent problemsas the vanishing gradient problem, finding a local minimum, improving the efficiency oflarge-scale parametric machine learning algorithms, eliminating decoherence and quantum errors,etc. Within the framework of this work, the process of functioning of a quantum variationalscheme is described, its main characteristics are established, and disadvantages are identified.The key features of quantum computing, on which the process of implementing quantum deeplearning with the reinforcement of a convolutional neural network is based, are also analyzed. Inaddition, quantum deep learning of a convolutional neural network has been carried out using avariational quantum scheme, which leads to an increase in the performance of a convolutionalneural network in solving the problem of image processing, namely its classification, using aquantum computing environment. The relevance of this article consists in the implementation of aquantum deep learning algorithm with the reinforcement of a convolutional neural network forimage processing, as well as the great importance of the subject of this study for the future developmentof quantum computing devices that can be used in artificial intelligence systems, etc.,which corresponds to the priority direction of the development of domestic science.
Deep learning algorithms and models have made an impact in the area of AI and machine learning, one among them is CNN. CNN is extensively used in the area of image recognition and object detection for classification purposes. CNN is composed of several layers of filters to get feature maps of input data, yet foremost and crucial one is convolutional layer, hence the name Convolutional neural networks. However, the growth of quantum computing and quantum neural network in deep learning is limited. Three main obstacles that limit the growth of these are, first is due to the lack of real-time quantum computers to experiment with. Second is the improper training algorithms and at last, non-linearity nature of the neural networks. This paper introduces a novel approach to begin one's journey in quantum computing, along with solutions and developments. This work provides a detailed description of architectures, frameworks and algorithms used for implementing a QCNN model. The research was made regarding image recognition and object detection using QCNN and found that QCNN can increase the computational speeds with better performance metrics compared to classical computational methods. This paper also debates about applications of QCNN in computer vision, signal and image processing, Pharmaceuticals, Cryptography and various other fields. This study also explains Key players and future work in developing quantum computers, quantum computing algorithms, software and hardware support to implement QCNN in various applications.
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The unprecedented progress in the domain of quantum computing in the last few years has influenced researchers around the globe to solve multitudes of problems in this promising computing technology. This power of the quantum computer has allowed multitudes of computationally hard problems to be sped up exponentially over their classical counterparts. Along with such power, another promising application of quantum computing has been found in image processing and machine learning. Researches in both quantum image processing and quantum machine learning are still in their infancy but promise exceptional power over its classical counterparts. In this thesis, neural networks will be trained to determine parameters for various parametric quantum circuits to perform important classification tasks, such as image classification. But for image classification, features from the images must also be extracted and epresented in terms of qubits, requiring convolutional layers tailored for quantum techniques. This thesis aims to find good quantum convolutional neural network architectures for image classification with higher accuracy. This is still challenging due to increased cost and error with a higher number of qubits within a system. This thesis is expected to be important in the future direction of the research of quantum CNN.
In the field of image processing, a well-known model is the Convolutional Neural Network, or CNN. The unique benefit that sets this model apart is its exceptional ability to use the correlation information included in the data. Even with their amazing accomplishment, conventional CNNs could have trouble improving further in terms of generalization, accuracy, and computing economy. However, it could be challenging to train CNN correctly and process information quickly if the model or data dimensions are too large. This is since it will cause the data processing to lag. The Quantum Convolutional Neural Network, or QCNN for short, is a novel proposed quantum solution that might either enhance the functionality of an existing learning model or solve a problem requiring the combination of quantum computing with CNN. To highlight the flexibility and versatility of quantum circuits in improving feature extraction capabilities, this paper compares deep quantum circuit architecture designed for image-based tasks with classical Convolutional Neural Networks (CNNs) and a novel quantum circuit architecture. The covidx-cxr4 dataset was used to train quantum-CNN models, and their results were compared against those of other models. The results show that when paired with innovative feature extraction methods, the suggested deep Quantum Convolutional Neural Network (QCNN) outperformed the conventional CNN in terms of processing speed and recognition accuracy. Even though it required more processing time, QCNN outperformed CNN in terms of recognition accuracy. When training on the covidx-cxr4 dataset, this dominance becomes much more apparent, demonstrating how deeper quantum computing has the potential to completely transform image classification problems.
Artificial neural networks have achieved great success in many fields ranging from image recognition to video understanding. However, its high requirements for computing and memory resources have limited further development on processing big data with high dimensions. In recent years, advances in quantum computing show that building neural networks on quantum processors is a potential solution to this problem. In this paper, we propose a novel neural network model named Quantum Convolutional Neural Network (QCNN), aiming at utilizing the computing power of quantum systems to accelerate classical machine learning tasks. The designed QCNN is based on implementable quantum circuits and has a similar structure as classical convolutional neural networks. Numerical simulation results on the MNIST dataset demonstrate the effectiveness of our model.
Quantum computing is a new computational paradigm that promises applications in several fields, including machine learning. In the last decade, deep learning, and in particular Convolutional neural networks (CNN), have become essential for applications in signal processing and image recognition. Quantum deep learning, however remains a challenging problem, as it is difficult to implement non linearities with quantum unitaries. In this paper we propose a quantum algorithm for applying and training deep convolutional neural networks with a potential speedup. The quantum CNN (QCNN) is a shallow circuit, reproducing completely the classical CNN, by allowing non linearities and pooling operations. The QCNN is particularly interesting for deep networks and could allow new frontiers in image recognition, by using more or larger convolution kernels, larger or deeper inputs. We introduce a new quantum tomography algorithm with ℓ∞\ell_{\infty}ℓ∞​ norm guarantees, and new applications of probabilistic sampling in the context of information processing. We also present numerical simulations for the classification of the MNIST dataset to provide practical evidence for the efficiency of the QCNN.