16 citations · 31 across the 15 of their papers we have counts for
5 papers · 1 filter
When cheap gradients fail: the measurement cost of attacking quantum classifiers
Bacui Li, Chandra Thapa, Tansu Alpcan +1
Adversarial perturbations threaten machine learning classifiers, including variational quantum classifiers. We show that finite quantum measurement statistics (shot noise) act as a…
A Qubit-Efficient Hybrid Quantum Encoding Mechanism for Quantum Machine Learning
Hevish Cowlessur, Tansu Alpcan, Chandra Thapa +2
Efficiently embedding high-dimensional datasets onto noisy and low-qubit quantum systems is a significant barrier to practical Quantum Machine Learning (QML). Approaches such as qu…
A Hybrid Quantum Neural Network for Split Learning
Hevish Cowlessur, Chandra Thapa, Tansu Alpcan +1
Quantum Machine Learning (QML) is an emerging field of research with potential applications to distributed collaborative learning, such as Split Learning (SL). SL allows resource-c…
Mitigation of Channel Tampering Attacks in Continuous-Variable Quantum Key Distribution
Sebastian P. Kish, Chandra Thapa, Mikhael Sayat +3
Despite significant advancements in continuous-variable quantum key distribution (CV-QKD), practical CV-QKD systems can be compromised by various attacks. Consequently, identifying…
Radio Signal Classification by Adversarially Robust Quantum Machine Learning
Yanqiu Wu, Eromanga Adermann, Chandra Thapa +3
Radio signal classification plays a pivotal role in identifying the modulation scheme used in received radio signals, which is essential for demodulation and proper interpretation…