Compliance with Personal Protective Equipment (PPE) in mechanical training workshops is essential but complex to monitor consistently. This work presents a Quantum Transfer Learning (QTL) pipeline that couples a pre-trained classical feature extractor with a variational quantum classifier to recognize helmet-use conditions in industrial training footage. We target a three-class setting—correct helmet use, incorrect helmet use, and no helmet—using transfer features from a lightweight convolutional backbone and a Basic-Entangler-type parameterized quantum circuit optimized with the Adam optimizer. The dataset comprises images collected in a controlled mechanical-training environment, with standard splits and class imbalance typical of educational settings. A proof-of-concept evaluation on the QTL-Helmet subset reports a test accuracy of 71.45% after 50 epochs. Furthermore, a significant reduction in training time was observed when using GPUs compared to CPUs, demonstrating the efficiency of accelerated computing. The model shows the viability of the quantum-classical approach for classification tasks with moderate computational resources. While preliminary and below operational thresholds, the study surfaces concrete levers for improvement within the QTL paradigm: stronger data curation and augmentation tailored to PPE attributes, better class rebalancing, deeper classical backbones for richer embeddings, circuit-depth/ansatz search with regularization, and hybrid ensembling of multiple shallow quantum heads. We also outline deployment considerations for near-term quantum hardware via simulator-to-hardware transfer and batching strategies. These results establish a reproducible baseline for quantum-enhanced PPE recognition in industrial and educational contexts and delineate a roadmap to close the performance gap through data, architecture, and training refinements, bringing QTL closer to practical safety monitoring in Industry 4.0.
Compliance with Personal Protective Equipment (PPE) in mechanical training workshops is essential but complex to monitor consistently. This work presents a Quantum Transfer Learning (QTL) pipeline that couples a pre-trained classical feature extractor with a variational quantum classifier to recognize helmet-use conditions in industrial training footage. We target a three-class setting—correct helmet use, incorrect helmet use, and no helmet—using transfer features from a lightweight convolutional backbone and a Basic-Entangler-type parameterized quantum circuit optimized with the Adam optimizer. The dataset comprises images collected in a controlled mechanical-training environment, with standard splits and class imbalance typical of educational settings. A proof-of-concept evaluation on the QTL-Helmet subset reports a test accuracy of 71.45% after 50 epochs. Furthermore, a significant reduction in training time was observed when using GPUs compared to CPUs, demonstrating the efficiency of accelerated computing. The model shows the viability of the quantum-classical approach for classification tasks with moderate computational resources. While preliminary and below operational thresholds, the study surfaces concrete levers for improvement within the QTL paradigm: stronger data curation and augmentation tailored to PPE attributes, better class rebalancing, deeper classical backbones for richer embeddings, circuit-depth/ansatz search with regularization, and hybrid ensembling of multiple shallow quantum heads. We also outline deployment considerations for near-term quantum hardware via simulator-to-hardware transfer and batching strategies. These results establish a reproducible baseline for quantum-enhanced PPE recognition in industrial and educational contexts and delineate a roadmap to close the performance gap through data, architecture, and training refinements, bringing QTL closer to practical safety monitoring in Industry 4.0.
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.
Quantum Transfer Learning (QTL) offers a promising approach for visual quantum machine learning under near-term constraints, where limited qubit counts, shallow circuit depths, and costly hybrid optimization restrict end-to-end quantum training. In this setting, pretrained classical backbones can extract high-level visual features, while compact quantum modules operate as trainable classification heads. However, existing QTL results are difficult to compare because they often differ in datasets, preprocessing, backbone settings, qubit budgets, circuit designs, optimization choices, and reporting protocols. This work presents a controlled benchmarking methodology for evaluating representative QTL methods under a unified transfer-learning pipeline. The benchmark compares DQN-QTL, QPIE-QTL, AE-CQTL, PVCQTL, and ED-QTL under shared preprocessing rules, frozen-backbone settings, training conditions, and reporting metrics. The evaluation focuses on Fashion-MNIST and Hymenoptera Ants vs. Bees as the two main datasets, while CIFAR-10 is used to provide additional configuration-level evidence on a harder natural-image task. Beyond predictive performance, the benchmark analyzes circuit size, trainable parameters, quantum parameters, training time, and architectural sensitivity to qubit count and circuit depth. The results show that no single QTL family dominates across all settings: performance depends on the dataset, encoding strategy, circuit design, and computational cost. These findings highlight the need for resource-aware QTL evaluation and provide guidance for selecting hybrid quantum-classical transfer models under near-term resource constraints.
Quantum Transfer Learning (QTL) recently gained popularity as a hybrid quantum-classical approach for image classification tasks by efficiently combining the feature extraction capabilities of large Convolutional Neural Networks with the potential benefits of Quantum Machine Learning (QML). Existing approaches, however, only utilize gate-based Variational Quantum Circuits for the quantum part of these procedures. In this work we present an approach to employ Quantum Annealing (QA) in QTL-based image classification. Specifically, we propose using annealing-based Quantum Boltzmann Machines as part of a hybrid quantum-classical pipeline to learn the classification of real-world, large-scale data such as medical images through supervised training. We demonstrate our approach by applying it to the three-class COVID-CT-MD dataset, a collection of lung Computed Tomography (CT) scan slices. Using Simulated Annealing as a stand-in for actual QA, we compare our method to classical transfer learning, using a neural network of the same order of magnitude, to display its improved classification performance. We find that our approach consistently outperforms its classical baseline in terms of test accuracy and AUC-ROC-Score and needs less training epochs to do this.
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.
We address the limitations of variational quantum circuits (VQCs) in hybrid classical-quantum transfer learning by introducing post-variational strategies, which reduce training overhead and mitigate optimization issues. Our approach Post Variational Classical Quantum Transfer Learning (PVCQTL) includes three designs: (1) modified observable construction, (2) a hybrid approach, and (3) a variational-post-variational combination. We evaluate these on pre-trained models (VGG19, ResNet50, ResNet18, MobileNet) for 4 and 8 qubits, with ResNet50 performing best in deepfake detection. Compared to classical models (MLP, ResNet50) and quantum baselines hybrid quantum classical neural network (HQCNN), classical-quantum transfer learning (CQTL). PVCQTL consistently achieves better accuracy. The modified observable variant reaches 85% accuracy for Deepfake dataset with lower computational cost. To evaluate generalizability, we tested PVCQTL on three additional binary classification datasets, observing improved accuracy on each. We conducted ablation studies to assess the effects of architectural choices on quantum component variations, including the choice of quantum gates, use of fixed ansatz circuits, and observable measurements. Robustness to input noise and sensitivity of the PVCQTL models were examined through ablation studies on learning rate, batch size, and number of qubits. These results demonstrate that PVCQTL offers a measurable improvement over traditional hybrid classical-quantum approaches.
This paper proposes a quantum transfer learning (QTL) algorithm that combines a transfer learning (TL) algorithm with parameterized quantum circuits (PQC) to investigate the relationship between PQC and model performance in hybrid quantum classical neural networks (HQCNN). The QTL algorithm effectively improves training speed compared to current quantum algorithms, facilitating a more effective exploration of the relationship between PQC and HQCNN. Experimental results show that the expressibility of PQCs is closely related to circuit structure, overlap depth, and the number of qubits. The all-circuits and annular-circuits (APQC) demonstrate better expressibility. Additionally, the expressibility of annular-circuits strongly correlates with the accuracy of the QTL model. The paper also explores the impact of pre-trained models on QTL performance. Ultimately, the pre-trained model ResNet18 and the quantum classifier based on APQC are chosen to form the QTL model. In terms of training time, QTL completes 20 epochs in 4 hours, saving 90% of time compared to general QCHNN.