19 citations · 22 across the 5 of their papers we have counts for
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Adversarial Threats in Quantum Machine Learning: A Survey of Attacks and Defenses
Archisman Ghosh, Satwik Kundu, Swaroop Ghosh
Quantum Machine Learning (QML) integrates quantum computing with classical machine learning, primarily to solve classification, regression and generative tasks. However, its rapid…
Inverse-Transpilation: Reverse-Engineering Quantum Compiler Optimization Passes from Circuit Snapshots
Satwik Kundu, Swaroop Ghosh
Circuit compilation, a crucial process for adapting quantum algorithms to hardware constraints, often operates as a ``black box,'' with limited visibility into the optimization tec…
Adversarial Data Poisoning Attacks on Quantum Machine Learning in the NISQ Era
Satwik Kundu, Swaroop Ghosh
With the growing interest in Quantum Machine Learning (QML) and the increasing availability of quantum computers through cloud providers, addressing the potential security risks as…
Security Concerns in Quantum Machine Learning as a Service
Satwik Kundu, Swaroop Ghosh
Quantum machine learning (QML) is a category of algorithms that employ variational quantum circuits (VQCs) to tackle machine learning tasks. Recent discoveries have shown that QML…
STIQ: Safeguarding Training and Inferencing of Quantum Neural Networks from Untrusted Cloud
Satwik Kundu, Swaroop Ghosh
The high expenses imposed by current quantum cloud providers, coupled with the escalating need for quantum resources, may incentivize the emergence of cheaper cloud-based quantum s…
Evaluating Efficacy of Model Stealing Attacks and Defenses on Quantum Neural Networks
Satwik Kundu, Debarshi Kundu, Swaroop Ghosh
Cloud hosting of quantum machine learning (QML) models exposes them to a range of vulnerabilities, the most significant of which is the model stealing attack. In this study, we ass…