The paper introduces QADR, a novel hybrid quantum-classical framework that efficiently trains variational quantum circuits by localizing entanglement reduction, thereby overcoming the exponential memo…

This survey provides a detailed overview of quantum adversarial machine learning, examining existing attacks, novel quantum-enhanced defense strategies, and the theoretical challenges in securing quan…

QML-PipeGuard introduces a contract-based framework that monitors the behavioral fingerprint of quantum machine learning pipelines to detect both hardware drift and malicious channel substitution.

The paper proposes QShield, a hybrid quantum-classical neural network architecture, which significantly enhances the adversarial robustness of deep learning models against various attacks.

This study empirically benchmarks classical and quantum machine learning models for image recognition, finding that while quantum models offer superior accuracy and resource efficiency at high dimensi…

The paper argues that current lattice-based post-quantum cryptography, which relies on injecting noise, is not unconditionally secure because advanced quantum error correction and learning techniques…

This paper surveys the security vulnerabilities of Variational Quantum Circuits (VQCs) to backdoor attacks, detailing various attack mechanisms and analyzing current detection and defense strategies.

This paper presents a GPU-accelerated implementation of a Learning with Errors (LWE)-based Key Encapsulation Mechanism (KEM), demonstrating significant speedups and energy efficiency gains on modern G…

The paper evaluates quantum machine learning for detecting anomalies in UAVs using a rigorous, leakage-free methodology, showing that a hybrid XGBoost + Data Reuploading classifier performs well, part…

The paper reviews adversarial machine learning vulnerabilities and proposes conceptual frameworks for enhancing AI robustness by integrating quantum computing techniques.

The paper reviews the vulnerability of AI to adversarial attacks and proposes conceptual frameworks for enhancing AI robustness by integrating quantum computing techniques.

This paper introduces Electric Flow Sampling (elfs) as a zero-error quantum walk primitive and uses it to derive improved quantum algorithms for various graph problems, including semi-supervised learn…

The paper proposes a layered, modular network architecture combining Quantum Key Distribution (QKD) and Post-Quantum Cryptography (PQC) to achieve scalable, end-to-end post-quantum security in multi-h…

The paper proposes QT-PUF, a novel quantum tunneling leakage-based Physical Unclonable Function (PUF) designed for ultralow-power, implantable IoMT devices, achieving high reliability and minimal powe…

The paper proposes a novel Meta-Quantum Ensemble (MQE) framework, which fuses outputs from Quantum Support Vector Machines (QSVMs) and Quantum Neural Networks (QNNs) using a Random Forest meta-learner…

The paper introduces 'quantum-safe,' a Python library that addresses the remaining 'production gap' in post-quantum cryptography (PQC) by providing robust, easy-to-use hybrid implementations and compr…

The paper proposes Q-AGNN, a Quantum-Enhanced Attentive Graph Neural Network, to improve intrusion detection by modeling network flows as graphs and leveraging quantum circuits to capture complex rela…

The paper estimates the quantum resources required to break 256-bit ECC cryptography and warns that fast-clock quantum computers could enable on-spend attacks on modern cryptocurrencies, necessitating…

The paper presents Broken Quantum, a comprehensive formal security audit that identifies 547 security vulnerabilities across 45 open-source quantum computing simulators, revealing critical flaws in me…

The paper proposes a stochastic risk-aware optimization framework for covert quantum communication, significantly improving throughput and expanding feasible operating regions under realistic channel…