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]%).
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]%).
Quantum transfer learning combines pretrained classical deep learning models with quantum circuits to reuse expressive feature representations while limiting the number of trainable parameters. In this work, we introduce a family of compact quantum transfer learning architectures that attach variational quantum classifiers to frozen convolutional backbones for image classification. We instantiate and evaluate several classical-quantum hybrid models implemented in PennyLane and Qiskit, and systematically compare them with a classical transfer-learning baseline across heterogeneous image datasets. To ensure a realistic assessment, we evaluate all approaches under both ideal simulation and noisy emulation using noise models calibrated from IBM quantum hardware specifications, as well as on real IBM quantum hardware. Experimental results show that the proposed quantum transfer learning architectures achieve competitive and, in several cases, superior accuracy while consistently reducing training time and energy consumption relative to the classical baseline. Among the evaluated approaches, PennyLane-based implementations provide the most favorable trade-off between accuracy and computational efficiency, suggesting that hybrid quantum transfer learning can offer practical benefits in realistic NISQ era settings when feature extraction remains classical.
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.
This study presents an effective analysis of a hybrid transfer learning algorithm in medical image processing, specifically focusing on histopathological cancer detection. The research involves a two-part approach. First, a hybrid model was created, combining classical and quantum units for classifying images into cancerous and non-cancerous cells. Second, the performance of this hybrid model was compared with traditional models, quantum simulators like Pennylane, and IBM's real quantum computers (Lima, Quito, Belem, and Jakarta), considering factors such as the number of qubits, Variational Quantum Classifier (VQC), and number of images. The objective was to aid users in selecting the optimal combination of classical and quantum units for accurate classification. The study found that the hybrid transfer learning model, when used with the Pennylane quantum simulator, yielded slightly higher accuracy than hybrid models implemented with IBM's quantum computers.
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.
The need to have rapid technological solutions is becoming more evident as the information world digitizes and the amount of data around us grows.It is argued that the future of computational systems will be quantum computing, which will be faster and more capable of solving several issues that cannot be solved with current computers.The aim of the current research is to examine the contributions made by quantum transfer learning models in enhancing the detection of breast cancer.Although both classical and deep learning approaches have proved to be effective, they continue to face serious challenges in handling high-dimensional and complex medical data.Quantum computing can be a feasible solution to such complexity.This study presents a hybrid model, which combines a classical pre-trained deep learning model ( ResNet50) with a range of variational quantum circuits-simple, entangled, and more complex.The main goal is to find the best model set up with regard to predictive power and computation time.It has been found that a hybrid model with an entangled variational circuit has an accuracy of 98.46, a precision of 100, and an F1-score of 97.3, which means a better result compared to the standard transfer learning model, which had an accuracy of 94.6, a precision of 97, and an F1-score of 90.4.These results improve upon recent quantum transfer learning studies, such as Azevedo et al. (2022) with 84% accuracy, and align closely with state-of-the-art quantumoptimized models reporting up to 99.3% accuracy, highlighting the effectiveness of our entangled variational approach on ultrasound images.Meanwhile, the traditional transfer learning model was the best in computational resource utilization.
We extend the concept of transfer learning, widely applied in modern machine learning algorithms, to the emerging context of hybrid neural networks composed of classical and quantum elements. We propose different implementations of hybrid transfer learning, but we focus mainly on the paradigm in which a pre-trained classical network is modified and augmented by a final variational quantum circuit. This approach is particularly attractive in the current era of intermediate-scale quantum technology since it allows to optimally pre-process high dimensional data (e.g., images) with any state-of-the-art classical network and to embed a select set of highly informative features into a quantum processor. We present several proof-of-concept examples of the convenient application of quantum transfer learning for image recognition and quantum state classification. We use the cross-platform software library PennyLane to experimentally test a high-resolution image classifier with two different quantum computers, respectively provided by IBM and Rigetti.