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Found 9 papers, 1 papers with code
QFNN-FFD: Quantum Federated Neural Network for Financial Fraud Detection
This study introduces the Quantum Federated Neural Network for Financial Fraud Detection (QFNN-FFD), a cutting-edge framework merging Quantum Machine Learning (QML) and quantum computing with Federated Learning (FL) for financial fraud detection.
FedQNN: Federated Learning using Quantum Neural Networks
In this study, we explore the innovative domain of Quantum Federated Learning (QFL) as a framework for training Quantum Machine Learning (QML) models via distributed networks.
Brain Tumor Diagnosis Using Quantum Convolutional Neural Networks
Integrating Quantum Convolutional Neural Networks (QCNNs) into medical diagnostics represents a transformative advancement in the classification of brain tumors.
Financial Fraud Detection using Quantum Graph Neural Networks
QGNNs are a type of neural network that can process graph-structured data and leverage the power of Quantum Computing (QC) to perform computations more efficiently than classical neural networks.
Quantum State Tomography using Quantum Machine Learning
Quantum State Tomography (QST) is a fundamental technique in Quantum Information Processing (QIP) for reconstructing unknown quantum states.
Financial Fraud Detection: A Comparative Study of Quantum Machine Learning Models
In this research, a comparative study of four Quantum Machine Learning (QML) models was conducted for fraud detection in finance.
Simulation of a Variational Quantum Perceptron using Grover's Algorithm
The quantum perceptron, the variational circuit, and the Grover algorithm have been proposed as promising components for quantum machine learning.
Enhancing Quantum Support Vector Machines through Variational Kernel Training
Our proposed model, quantum variational kernel SVM (QVK-SVM), leverages the quantum kernel and quantum variational algorithm.
Classical-to-Quantum Sequence Encoding in Genomics
Our research contributes to developing classical-to-quantum data encoding methods in the science of Bioinformatics by introducing innovative algorithms that utilise diverse fields and advanced techniques.
