Academic Symposium on Optoelectronics and Microelectronics Technology and Chinese-Russian Symposium on Laser Physics and Laser TechnologyOptoelectronics Technology
Quantum Circuit Construction for Cat and Dog Image Classification Based on Quantum Neural Network
A quantum computer is a type of computer that uses the principles of quantum mechanics to perform calculations. Quantum computers are still in their early stages of development, but TensorFlow provides a library called TensorFlow Quantum (TFQ) for hybrid quantum classical machine learning. One of the models in quantum computing is the Quantum Neural Network (QNN). QNNs are derived from classical neural networks and are designed to handle quantum bits (qubits) and facilitate the transmission of quantum circuits. Quantum Neural Networks (QNNs) are machine learning models designed to use the principles of quantum mechanics for tasks such as image classification. They can process classical image data, but classical data cannot directly achieve superposition states, which is a key feature of quantum systems. Thus, the data must be readable and able to be appropriately input into quantum devices. QNN uses supervised learning methods to predict image data. A quantum neural network (QNN) using supervised learning was developed to classify images of cats and dogs. This implementation utilized quantum circuit simulation within the Python 3.7 programming environment.
A quantum computer is a type of computer that uses the principles of quantum mechanics to perform calculations. Quantum computers are still in their early stages of development, but TensorFlow provides a library called TensorFlow Quantum (TFQ) for hybrid quantum classical machine learning. One of the models in quantum computing is the Quantum Neural Network (QNN). QNNs are derived from classical neural networks and are designed to handle quantum bits (qubits) and facilitate the transmission of quantum circuits. Quantum Neural Networks (QNNs) are machine learning models designed to use the principles of quantum mechanics for tasks such as image classification. They can process classical image data, but classical data cannot directly achieve superposition states, which is a key feature of quantum systems. Thus, the data must be readable and able to be appropriately input into quantum devices. QNN uses supervised learning methods to predict image data. A quantum neural network (QNN) using supervised learning was developed to classify images of cats and dogs. This implementation utilized quantum circuit simulation within the Python 3.7 programming environment.
Quantum Computer is a computer machine using principles of quantum mechanics in doing its computation. The Quantum Computer Machine itself is still in the development stage and has not been deployed yet, however TensorFlow provides a library for hybrid quantum-classical machine learning called TensorFlow Quantum (TFQ). One of the quantum computing models is the Quantum Neural Network (QNN). QNN is adapted from classical neural networks capable of processing qubit data and passing quantum circuits. QNN is a machine learning model that allows quantum computers to classify image data. The image data used is classical data, but classical data cannot reach a superposition state. So in order to carry out this protocol, the data must be readable into a quantum device that provides superposition. QNN uses a supervised learning method to predict image data. Quantum Neural Network (QNN) with a supervised learning method for classifying handwritten numeric image data is implemented using a quantum computer circuit simulation using the Python 3.6 programming language. The quantum computer circuit simulation is designed using library Cirq and TFQ. The classification process is carried out on Google Colab. The results of training on the QNN model obtained a value of 0.337 for the loss value and 0.3427 for the validation loss value. Meanwhile, the hinge accuracy value from the training results is 0.8603 for the hinge accuracy value with training data and 0.8669 for the hinge accuracy validation value. Model testing is done by providing 100 handwritten number images that are tested, with 53 image data of number three and 47 image data of number six. The results obtained for the percentage of testing accuracy are 100% for the number three image and 100% for the number six image. Thus, the total percentage of testing is 100%.
We introduce TensorFlow Quantum (TFQ), an open source library for the rapid prototyping of hybrid quantum-classical models for classical or quantum data. This framework offers high-level abstractions for the design and training of both discriminative and generative quantum models under TensorFlow and supports high-performance quantum circuit simulators. We provide an overview of the software architecture and building blocks through several examples and review the theory of hybrid quantum-classical neural networks. We illustrate TFQ functionalities via several basic applications including supervised learning for quantum classification, quantum control, simulating noisy quantum circuits, and quantum approximate optimization. Moreover, we demonstrate how one can apply TFQ to tackle advanced quantum learning tasks including meta-learning, layerwise learning, Hamiltonian learning, sampling thermal states, variational quantum eigensolvers, classification of quantum phase transitions, generative adversarial networks, and reinforcement learning. We hope this framework provides the necessary tools for the quantum computing and machine learning research communities to explore models of both natural and artificial quantum systems, and ultimately discover new quantum algorithms which could potentially yield a quantum advantage.
Image classification is a fundamental task in deep learning, and recent advances in quantum computing have generated significant interest in quantum neural networks. Traditionally, Convolutional Neural Networks (CNNs) are employed to extract image features, while Multilayer Perceptrons (MLPs) handle decision making. However, parameterized quantum circuits offer the potential to capture complex image features and define sophisticated decision boundaries. In this paper, we present a novel Hybrid Quantum–Classical Neural Network (H-QNN) for image classification, and demonstrate its effectiveness using the MNIST dataset. Our model combines quantum computing with classical supervised learning to enhance classification accuracy and computational efficiency. In this study, we detail the architecture of the H-QNN, emphasizing its capability in feature learning and image classification. Experimental results demonstrate that the proposed H-QNN model outperforms conventional deep learning methods in various training scenarios, showcasing its effectiveness in high-dimensional image classification tasks. Additionally, we explore the broader applicability of hybrid quantum–classical approaches in other domains. Our findings contribute to the growing body of work in quantum machine learning, and underscore the potential of quantum-enhanced models for image recognition and classification.
Quantum machine learning has established as an interdisciplinary field to overcome limitations of classical machine learning and neural networks. This is a field of research which can prove that quantum computers are able to solve problems with complex correlations between inputs that can be hard for classical computers. This suggests that learning models made on quantum computers may be more powerful for applications, potentially faster computation and better generalization on less data. The objective of this paper is to investigate how training of quantum neural network (QNNs) can be done using quantum optimization algorithms for improving the performance and time complexity of QNNs. A classical neural network can be partially quantized to create a hybrid quantum-classical neural network which is used mainly in classification and image recognition. In this paper, a QNN structure is made where a variational parameterized circuit is incorporated as an input layer named as Variational Quantum Neural Network (VQNNs). We encode the cost function of QNNs onto relative phases of a superposition state in the Hilbert space of the network parameters. The parameters are tuned with an iterative quantum approximate optimisation (QAOA) mixer and problem hamiltonians. VQNNs is experimented with MNIST digit recognition (less complex) and crack image classification datasets (more complex) which converges the computation in lesser time than QNN with decent training accuracy.
Quantum computing is expected to fundamentally change computer systems in the future. Recently, a new research topic of quantum computing is the hybrid quantum–classical approach for machine learning, in which a parameterized quantum circuit, also called quantum neural network (QNN), is optimized by a classical computer. This hybrid approach can have the benefits of both quantum computing and classical machine learning methods. In this early stage, it is of crucial importance to understand the new characteristics of quantum neural networks for different machine learning tasks. In this paper, we will study quantum neural networks for the task of classifying images, which are high-dimensional spatial data. In contrast to previous evaluations of low-dimensional or scalar data, we will investigate the impacts of practical encoding types, circuit depth, bias term, and readout on classification performance on the popular MNIST image dataset. Various interesting findings on learning behaviors of different QNNs are obtained through experimental results. To the best of our knowledge, this is the first work that considers various QNN aspects for image data.
Quantum Computing (QC) was born as a purely theoretical subject. Thus, in the beginning, the main category of researchers in the area was physicists and mathematicians. Since IBM introduced the first quantum computer, global interest in this emerging technology has been growing more and more, involving computer science and engineering researchers. Machine learning is another rapidly growing technology, used in a wide range of applications where traditional computing is not sufficient. The two technologies are merged in the thesis work. The different ways in which a combination of them can occur are discussed, as well as the explanation why there is not a single definition of Quantum Machine Learning (QML). Firstly, quantum computing is examined in depth. The basic elements of quantum computation theory, based on the fundamental principles of quantum mechanics, are introduced. The attention is focused on the qubit description on the Bloch sphere, the fundamental quantum gates, and examples of quantum algorithms. Therefore, the differences between quantum simulators and quantum processors are analysed, including the frameworks that provide tools to interact with them. Focusing on Quantum Machine Learning, the different definitions of the technology are described. There are four distinct approaches that are differentiated by the type of data used (quantum or classical) and by the device processing them (quantum or classical). In this thesis, QML is defined as the approach that uses classical data to feed a quantum device. The two possible methods for developing QML algorithms are: 1) to translate existing classical models into the terms of quantum computing; 2) to leave the existing models of classical machine learning completely, and explore new possible ways of doing QML to take full advantage of the physical characteristics of a quantum computer. The strategy adopted is the first one, thus it is used a state-of-the-art hybrid model of Quanvolutional Neural Network (QNN), composed of both quanvolutional and classical convolutional layers. The quanvolutional layers reproduce the same concept of the classical ones: they are made of quantum filters that generate feature maps through locally transforming input data. The transformation can be performed by variational quantum circuits or random quantum circuits. The specific field where Quantum Machine Learning is applied is image classification. The considered case study is a labeled dataset with X-ray images of lungs (labels: pneumonia, normal). The images are pre-processed and cropped for being used by quantum circuits, thus some information is lost. The main aim of the thesis is to understand whether and in which ways Quantum Machine Learning could bring advantages in the mentioned classification in terms of timing, accuracy, or dataset reduction, despite the large decrease in images' resolution. Different configurations of the hybrid model (with a different number of quantum filters and different settings of convolutional layers) are analysed and tested. The current quantum devices have many limitations, thus the time is still immature to observe a significant change in perspective. Nevertheless, the obtained results are promising for the future of QML in terms of dataset and hyperparameters reduction.