In this article, we introduce a novel approach for enhancing the performance of Quantum Neural Networks (QNNs) through variations in entanglement schemes within variational forms. Our main objective in this research is to improve the performance of QNNs, specifically focusing on their application in classification tasks. We tested three models, each with a different type of entanglement scheme: Linear Entanglement, Circular Entanglement, and our proposed Full Entanglement. Each model consisted of three layers of Ry parameterized gates. We used the Breast Cancer Wisconsin (Diagnostic) dataset and encoded it using the ZZ-feature Map. Then we used this encoded data for training our models and evaluated them based on metrics such as training and validation loss. Our experimental results demonstrate that increasing entanglement can mitigate overfitting, with the proposed Full Entanglement scheme showing the most promising results.
In this article, we introduce a novel approach for enhancing the performance of Quantum Neural Networks (QNNs) through variations in entanglement schemes within variational forms. Our main objective in this research is to improve the performance of QNNs, specifically focusing on their application in classification tasks. We tested three models, each with a different type of entanglement scheme: Linear Entanglement, Circular Entanglement, and our proposed Full Entanglement. Each model consisted of three layers of Ry parameterized gates. We used the Breast Cancer Wisconsin (Diagnostic) dataset and encoded it using the ZZ-feature Map. Then we used this encoded data for training our models and evaluated them based on metrics such as training and validation loss. Our experimental results demonstrate that increasing entanglement can mitigate overfitting, with the proposed Full Entanglement scheme showing the most promising results.
Abstract Quantum machine learning, focusing on quantum neural networks (QNNs), remains a vastly uncharted field of study. Current QNN models primarily employ variational circuits on an ansatz or a quantum feature map, often requiring multiple entanglement layers. This methodology not only increases the computational cost of the circuit beyond what is practical on near-term quantum devices but also misleadingly labels these models as neural networks, given their divergence from the structure of a typical feed-forward neural network (FFNN). Moreover, the circuit depth and qubit needs of these models scale poorly with the number of data features, resulting in an efficiency challenge for real-world machine learning tasks. We introduce a bona fide QNN model, which seamlessly aligns with the versatility of a traditional FFNN in terms of its adaptable intermediate layers and nodes, absent from intermediate measurements such that our entire model is coherent. This model stands out with its reduced circuit depth and number of requisite CNOT gates, achieving a more than 50% reduction in both compared to prevailing QNN models. Furthermore, the qubit count in our model remains unaffected by the data’s feature quantity. We test our proposed model on various benchmarking datasets such as the breast cancer diagnostic (Wisconsin) and credit card fraud detection datasets. Our model achieved an accuracy of 91% on the breast cancer dataset and 85% on the credit card fraud detection dataset, outperforming existing QNN methods by 5–10% while requiring approximately 50% fewer quantum resources. These results showcase the advantageous efficacy of our approach, paving the way for the application of quantum neural networks to relevant real-world machine learning problems.
Breast cancer is the second most common type of cancer among women worldwide. About 12.9% of women born in the United States today are expected to develop breast cancer during their lifetimes, equating to a chance of 1 in 8. Early and accurate classification of breast cancer is a vital task in medical imaging, as it significantly affects treatment outcomes and patient survival rates. In this work, a hybrid classical-quantum deep learning model that combines quantum transfer learning using ResNet152 with a dressed quantum network employing Hadamard, CNOT, and Ry gates is proposed. This integration takes advantage of the advanced feature extraction capabilities of ResNet152 along with the computational efficiencies and potential advantages offered by quantum networks. Our model achieves a training accuracy of 90.42% and a validation accuracy of 89.90%, demonstrating the effectiveness of incorporating quantum computing into deep learning workflows. In comparison, the classical ResNet152 achieves a training accuracy of 87.23% and validation accuracy of 85.78%, while ResNet50 reaches 86.89% training and 86.76% validation accuracy. These results underscore the potential of hybrid classical-quantum approaches in the advancement of medical diagnostics and highlight a promising direction to improve performance in complex classification tasks.
Quantum-assisted models can surpass classical counterparts in specific domains by leveraging quantum superposition entanglement for more efficient data representation. This study investigates a Quantum neural network framework executed on various Quantum simulators, including ionq, Cirq, Pennylane and Quantinnum backends. The iris, wine, Indian liver patient and rain datasets are employed as benchmarking datasets to assess the efficacy of quantum-based classification models. The QNN utilises parameterized quantum circuits and quantum feature maps to learn complex patterns in data, optimizing classification decision boundaries through quantum optimization techniques. The study evaluates QNN performance across different quantum hardware platforms, assessing key metrics such as accuracy, recall, F1 score, and precision. Preliminary results indicate that QNN-based classification effectively captures feature dependencies through quantum correlations, offering competitive performance in data classification tasks. These findings contribute to the broader exploration of quantum-enhanced classification and the potential of quantum devices in real-world data analysis.
Abstract Quantum machine learning is gaining attention for its ability to uncover complex patterns that classical models may overlook. In this research, we explore the use of a quantum neural network (QNN) for classifying prostate cancer based on clinical data. We started with a real-world dataset and applied essential preprocessing steps to prepare it for training. The QNN was built using a ZZFeatureMap for encoding and a variational circuit combining rotation gates and entanglement. Training was performed with the COBYLA optimizer using an 80–20 train-test split. The model achieved strong results, with 87.5% accuracy, 89% precision, and over 90% recall. These outcomes suggest that QNNs offer meaningful potential for improving cancer detection tasks, particularly in medical domains where capturing subtle feature interactions is crucial.
In this work, an analysis of the performance of different Variational Quantum Circuits is presented, investigating how it changes with respect to entanglement topology, adopted gates, and Quantum Machine Learning tasks to be performed. The objective of the analysis is to identify the optimal way to construct circuits for Quantum Neural Networks. In the presented experiments, two types of circuits are used: one with alternating layers of rotations and entanglement, and the other, similar to the first one, but with an additional final layer of rotations. As rotation layers, all combinations of one and two rotation sequences are considered. Four different entanglement topologies are compared: linear, circular, pairwise, and full. Different tasks are considered, namely the generation of probability distributions and images, and image classification. Achieved results are correlated with the expressibility and entanglement capability of the different circuits to understand how these features affect performance.
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.