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 provides a comprehensive, system-level taxonomy for designing quantum-resistant network architectures, moving beyond simple protocol substitutions to address key distribution and management…

This paper introduces the first explicit data obfuscation technique to protect classical sensitive values during the execution phase of quantum computation.

This survey provides a comprehensive review of the security challenges, threats, and mitigation strategies associated with the rapid advancement of quantum computing.

The paper proposes a quantum-resistant quantum teleportation (QRQT) framework using post-quantum cryptography to secure the classical channel, establishing maximum secure communication distances and a…

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…

The paper develops a robust framework for covert quantum communication by analyzing performance over quantum channels with bounded uncertainty in transmissivity and noise, showing that worst-case secu…

The paper constructs strong cryptographic primitives, including key agreement and oblivious transfer, directly from classical tests of anti-commutation (ToNC) on quantum devices, while also developing…

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

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 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.

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 paper introduces the Manipulate-and-Observe attack, demonstrating that by combining partial qubit interception with probing parity-leakage during reconciliation, an eavesdropper can significantly…

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…

The paper proposes a formal framework to analyze how the combined cryptographic transformations across all layers of a network stack determine the overall post-quantum security posture of a message.

The paper develops a structurally justified framework for measuring Quantum Cryptographic Exposure (HNDL) by showing that the compromise probability factorizes into distinct, interacting components ba…

The paper introduces a framework, PD-FHC, that allows users to outsource Boolean computations to an untrusted cloud while guaranteeing both computational privacy and plausible deniability against coer…

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 demonstrates that soft fusion in multi-warden covert communication has structural limits, showing that the Fusion Center gains no significant detection advantage from randomizing the number…