Quantum machine learning (QML) is a research area that combines algorithms from quantum computing and machine learning into one functional system. This poster presents the results of an example of its successful use to recognize the gender of people by their faces. This recognition was performed using a learned hybrid quantum neural network that contains both neural layers implemented by a classical computer and neural layers implemented by the simulator of a quantum computer in which a variational quantum circuit (VQC) is programmed. The most computationally demanding part was teaching this network a training set containing several hundred female and male faces. The learning was done relatively quickly thanks to the powerful NVIDIA CUDA Quantum simulator. This platform allows running simulations on supercomputing nodes with many CPU and GPU cores, which significantly speeds them up compared to other quantum frameworks. The achieved accuracy was approximately 90% for faces on which the network was not trained. This poster also shows examples of various VQCs along with displayed learning curves depending on the simulated depolarization error probability and learning rate.
Quantum machine learning (QML) is a research area that combines algorithms from quantum computing and machine learning into one functional system. This poster presents the results of an example of its successful use to recognize the gender of people by their faces. This recognition was performed using a learned hybrid quantum neural network that contains both neural layers implemented by a classical computer and neural layers implemented by the simulator of a quantum computer in which a variational quantum circuit (VQC) is programmed. The most computationally demanding part was teaching this network a training set containing several hundred female and male faces. The learning was done relatively quickly thanks to the powerful NVIDIA CUDA Quantum simulator. This platform allows running simulations on supercomputing nodes with many CPU and GPU cores, which significantly speeds them up compared to other quantum frameworks. The achieved accuracy was approximately 90% for faces on which the network was not trained. This poster also shows examples of various VQCs along with displayed learning curves depending on the simulated depolarization error probability and learning rate.
When it comes to software systems that analyze face photos, gender detection is a crucial feature. This research provides a quantum machine learning-based approach for automated gender categorization from face photos utilizing a hybrid classical-quantum neural network, which is based on quantum machine learning. Using the knowledge of a pre-trained off-the-shelf Deep Neural Network (DNN)in conjunction with the transfer learning of a quantum variational circuit, an accurate binary classifier has been produced in this paper. An example of a hybrid network is the binary classifier, which is composed of a convolutional base of the Deep Neural Network (DNN) that has been compressed with a dressed quantum circuit and a dressed quantum circuit. In the classification of a publicly accessible collection of face photographs, better results than that have been previously reported have been obtained.
Face recognition has become an essential biometric technology for applications such as security monitoring, user authentication, and intelligent surveillance systems. Conventional approaches based on deep neural networks, particularly convolution-based architectures, provide high recognition performance but often demand considerable computational resources and extended training durations as network complexity increases. To overcome these challenges, Quantum Machine Learning (QML) utilizes quantum computing concepts, including quantum superposition and entangled states, to achieve efficient feature representation and processing. This paper presents a Hybrid Quantum-Classical Face Recognition System built on a Multi-Gate Quantum Convolutional Neural Network (MG-QCNN). The proposed framework employs a 4-qubit variational quantum circuit that incorporates RY state encoding together with RX, RZ, and CNOT quantum operations to extract discriminative facial representations. In addition, a structural ablation analysis is conducted to investigate the influence of individual quantum components on the overall recognition capability of the model. To enhance spatial feature learning and classification performance, several classical neural network layers, namely convolution, batch normalization, max-pooling, dropout, and fully connected layers, are integrated after the quantum feature extraction stage. Within the hybrid framework, the quantum circuit is responsible for generating meaningful feature representations, whereas the classical neural network performs feature refinement and final identity classification. The framework is further combined with the DeepFace verification approach to perform facial similarity analysis and identity verification. Moreover, an Open-Set Face Recognition mechanism is incorporated to determine if an input facial image corresponds to a registered identity or represents an unseen individual. This capability increases system reliability and supports accurate unknown-person identification
Gender recognition is an important function in numerous software applications that analyze facial images. This paper presents a quantum machine learning based solution towards automatic classification of gender from facial images using the hybrid classical-quantum neural network. Here we obtain a binary classifier using the knowledge of a pre-trained off-the-shelf deep neural network (DNN) combined with the transfer learning of a quantum variational circuit. The binary classifier is a hybrid network consisting of convolutional base of the DNN truncated with a dressed quantum circuit. We obtain performance better than previously reported in classifying publicly available dataset of facial images.
Despite its undeniable success, classical machine learning remains a resource-intensive process. Practical computational efforts for training state-of-the-art models can now only be handled by high speed computer hardware. As this trend is expected to continue, it should come as no surprise that an increasing number of machine learning researchers are investigating the possible advantages of quantum computing. The scientific literature on Quantum Machine Learning is now enormous, and a review of its current state that can be comprehended without a physics background is necessary. The objective of this study is to present a review of Quantum Machine Learning from the perspective of conventional techniques. Departing from giving a research path from fundamental quantum theory through Quantum Machine Learning algorithms from a computer scientist's perspective, we discuss a set of basic algorithms for Quantum Machine Learning, which are the fundamental components for Quantum Machine Learning algorithms. We implement the Quanvolutional Neural Networks (QNNs) on a quantum computer to recognize handwritten digits, and compare its performance to that of its classical counterpart, the Convolutional Neural Networks (CNNs). Additionally, we implement the QSVM on the breast cancer dataset and compare it to the classical SVM. Finally, we implement the Variational Quantum Classifier (VQC) and many classical classifiers on the Iris dataset to compare their accuracies.
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.
Hybrid quantum methods have received limited study for resource-constrained facial biometrics. We present a hybrid quantum-classical facial recognition pipeline designed to run on standard computing hardware. Images undergo gamma correction, contrast enhancement, and principal component analysis before their features are encoded into an eight-qubit variational quantum classifier. Classical image matching then performs recognition. In experiments with 50,000 images, comprising 25,000 faces from CelebA and 25,000 non-face images from CIFAR-10, the method outperformed the reported CPU-trained FaceNet baseline in accuracy and training efficiency. The pipeline was also evaluated on GPU and quantum hardware. In an attendance monitoring deployment at Mahindra University in collaboration with Lloyds Technology Centre, CPU inference took 0.2 to 0.5 seconds per person, and the system remained robust to the use of spectacles. These findings support the feasibility of deploying hybrid quantum methods for facial recognition on existing CPU hardware.