Quantum neural networks (QNNs) succeed in object recognition, natural language
processing, and financial analysis. To maximize the accuracy of a QNN on a Noisy …

Quantum computing (QC) demonstrates substantial theoretical promise in addressing
classically intractable problems. Recent investments and advancements across QC system …

State-of-the-art quantum machine learning (QML) algorithms fail to offer practical
advantages over their notoriously powerful classical counterparts, due to the limited learning …

We propose a circuit-level backdoor attack, QTrojan, against Quantum Neural Networks
(QNNs) in this paper. QTrojan is implemented by a few quantum gates inserted into the …

Quantum Error Correction (QEC) is one of the fundamental problems in quantum computer
systems, which aims to detect and correct errors in the data qubits within quantum …

Despite the success of Quantum Neural Networks (QNNs) in decision-making systems, their
fairness remains unexplored, as the focus primarily lies on accuracy. This work conducts a …

Variational quantum circuits (VQCs) have become a powerful tool for implementing
Quantum Neural Networks (QNNs), addressing a wide range of complex problems. Well …

Digital Twin (DT) technologies create digital models of physical entities frequently in
multimedia forms, which are crucial for concurrent simulation and analysis of real-world …

For a large number of tasks, quantum computing demonstrates the potential for exponential
acceleration over classical computing. In the NISQ era, variable-component subcircuits …

Current quantum generative adversarial networks (QGANs) still struggle with practical-sized
data. First, many QGANs use principal component analysis (PCA) for dimension reduction …