The problem of personal accidents in a work environment in many cases is due to the lack of use of helmets, among other safety elements.The objective of this work was to classify images of people who use or not use safety helmets, in the classes identified as correct use of the helmet, incorrect use and without a safety helmet, with a Quantum Transfer Learning model.For this, the method used has allowed the collection of a data set of 750 images of people in different work activities.The classical learning transfer model chosen was ResNet-18; the variational layers of the proposed model were built with the Basic Entangler Layers template for four qubits with the Pennylane quantum simulator.The results were 95.47% accurate in classifying the correct Safety Helmets.The conclusion reached is that the proposed model is a good option to detect and classify people with the use of safety helmets in a work environment.
The problem of personal accidents in a work environment in many cases is due to the lack of use of helmets, among other safety elements.The objective of this work was to classify images of people who use or not use safety helmets, in the classes identified as correct use of the helmet, incorrect use and without a safety helmet, with a Quantum Transfer Learning model.For this, the method used has allowed the collection of a data set of 750 images of people in different work activities.The classical learning transfer model chosen was ResNet-18; the variational layers of the proposed model were built with the Basic Entangler Layers template for four qubits with the Pennylane quantum simulator.The results were 95.47% accurate in classifying the correct Safety Helmets.The conclusion reached is that the proposed model is a good option to detect and classify people with the use of safety helmets in a work environment.
The problem of the COVID-19 disease has deter-mined that about 219 million people have contracted it, of which 4.55 million died. This importance has led to the implementation of security protocols to prevent the spread of this disease. One of the main protocols is to use protective masks that properly cover the nose and mouth. The objective of this paper was to classify images of faces using protective masks of COVID-19, in the classes identified as correct mask, incorrect mask, and no mask, with a Hybrid model of Quantum Transfer Learning. To do this, the method used has made it possible to gather a data set of 660 people of both sexes (man and woman), with ages ranging from 18 to 86 years old. The classic transfer learning model chosen was ResNet-18; the variational layers of the proposed model were built with the Basic Entangler Layers template for four qubits, and the optimization of the training was carried out with the Stochastic Gradient Descent with Nesterov Momentum. The main finding was the 99.05% accuracy in classifying the correct Protective Masks using the Pennylane quantum simulator in the tests performed. The conclusion reached is that the proposed hybrid model is an excellent option to detect the correct position of the protective mask for COVID-19.
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.
With millions of medical data being produced daily, it is important to leverage current technology to improve the healthcare system. As traditional deep learning models are unable to effectively handle high-dimensional data, a hybrid classical-quantum approach is used to enhance traditional deep learning algorithms. The hybrid classical-quantum models are built upon the classical deep learning framework with quantum techniques. The classical layers are utilized for feature extraction while the quantum layer is used for computational tasks. This addresses computational limitations and data imbalance challenges in medical image analysis. Transfer learning models, such as ResNet18 and InceptionV3, are used in conjunction with a quantum variational circuit to perform the task. The model is trained on a chest X-ray dataset, which consists of two sets of data: binary and multiclass. The binary dataset consists of Normal and COVID-19 images, while the multiclass dataset has COVID-19, Normal, and Tuberculosis images for classification. PennyLane, as a simulator, has been used to perform quantum computing. The dataset has also been used to train the traditional transfer learning models, pre-trained ResNet18, and their results have been compared. Out of all the models tested, the hybrid classical-quantum model, namely Hybrid ResNet18, performed the best. It achieved an accuracy of 93.22% for the binary classification task and 93.91% for the multiclass classification. The results show that there is a statistically significant improvement in classification accuracy and F1-score of the hybrid approach over the classical transfer learning baselines under a simulation-based environment. This sets the stage for further research on real-world physical quantum hardware. Class imbalance and overlapping predictions have been addressed using methods such as data augmentation and confusion matrix analysis. Thus, quantum machine learning could be used as a vital tool to analyze medical data for better diagnostic results.
In this paper, we present an implementation of quantum transfer learning to blind and passive detection of image splicing forgeries. Though deep learning models are becoming increasingly popular for various computer vision use cases, they depend on powerful classical machines and GPUs for dealing with complex problem solving and also to reduce the time taken for computation. The quantum computing research community has demonstrated elegant solutions to complex use cases in deep learning and computer vision for reducing storage space and increasing the accuracy of results compared to those obtained on a classical computer. We extend the quantum transfer learning approach formerly applied to image classification, for solving the growing problem of image manipulation, specifically, image splicing detection. A hybrid model is built using the ResNet50 pre-trained classical deep learning network and a quantum variational circuit to classify spliced versus authentic images. We present a comparative empirical study of classical versus quantum transfer learning approach using Xanadu’s pennylane quantum simulator and the pytorch deep learning framework. The model was also evaluated on the actual quantum processor ibmqx2 provided by IBM. Results obtained by execution on the quantum processor ([Formula: see text]%, [Formula: see text]%) and simulator ([Formula: see text]%, [Formula: see text]%) showed improvements in comparison to those obtained from classical computers ([Formula: see text]%, [Formula: see text]%).
Abstract The coronavirus has been affecting people's everyday life since 2019. To stop the further spread of the virus, early detection of carriers of the virus and isolation are needed. Due to the limitations of nuclear accounting amplification assay (NAAT), CT imaging, which has the high sensitivity to the new corona virus, is used as a diagnostic method. In this study, a classical-quantum hybrid model using a variational quantum circuit as a classifier was designed. A pre-trained VGG16 was used for feature extraction, and the extracted features were passed to a variational quantum classifier for classification. Experiments show that using variational quantum circuits to linearly vary the classical data can improve the accuracy of the model prediction. Hybrid Model achieves good results with values of 98.78%, 97.65%, 100.00%, and 98.81% for Accuracy, Precision, Recall, and F1-score metrics, respectively. The comparison between Hybrid Model and the classical model shows that the overall performance of Hybrid Model is better than that of the classical model. Hybrid Model provides a reliable, flexible and scalable classical-quantum deep learning approach for the prediction task of COVID-19.
Many security applications like face recognition and iris recognition developed to ensure the safety of the critical or personal data. Reliability of these applications are highly depending over the reliability of machine vision algorithms. Along with the development of the generative models standard machine vision dependent data security measurement systems became vulnerable. Currently, generating fake data to bypass machine vision dependent security systems is possible with a personal computer. In order to ensure the reliability of security measurements depending on the machine vision techniques it is critical to recognize fake images. In this research, possible use case of a quantum computer to ensure the reliability of machine vision dependent security systems is investigated. A hybrid quantum-classical hybrid model with the transfer learning approach is proposed to recognize whether if a face is fake or not. Effects of the quantum model’s depth over the accuracy is explored. ResNet-18 architecture is used as the classical part and a custom quantum neural network architecture built with the dressed quantum circuits is used as quantum part. This research is aimed to extend the use cases of quantum neural networks to security applications. Accuracies of quantum neural networks with different depths are reported. A simulated quantum computer is used to train the models. Along with the proposed approach it is concluded that it is possible to apply classical-quantum neural networks to improve the reliability of machine vision dependent security systems after the quantum co-processors become available in daily life.