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View article: Adversarial Threats in Quantum Machine Learning: A Survey of Attacks and Defenses
Adversarial Threats in Quantum Machine Learning: A Survey of Attacks and Defenses Open
Quantum Machine Learning (QML) integrates quantum computing with classical machine learning, primarily to solve classification, regression and generative tasks. However, its rapid development raises critical security challenges in the Nois…
View article: Inverse-Transpilation: Reverse-Engineering Quantum Compiler Optimization Passes from Circuit Snapshots
Inverse-Transpilation: Reverse-Engineering Quantum Compiler Optimization Passes from Circuit Snapshots Open
Circuit compilation, a crucial process for adapting quantum algorithms to hardware constraints, often operates as a ``black box,'' with limited visibility into the optimization techniques used by proprietary systems or advanced open-source…
View article: Adversarial Data Poisoning Attacks on Quantum Machine Learning in the NISQ Era
Adversarial Data Poisoning Attacks on Quantum Machine Learning in the NISQ Era Open
With the growing interest in Quantum Machine Learning (QML) and the increasing availability of quantum computers through cloud providers, addressing the potential security risks associated with QML has become an urgent priority. One key co…
View article: Security Concerns in Quantum Machine Learning as a Service
Security Concerns in Quantum Machine Learning as a Service Open
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 models can effectively generalize from limited training dat…
View article: STIQ: Safeguarding Training and Inferencing of Quantum Neural Networks from Untrusted Cloud
STIQ: Safeguarding Training and Inferencing of Quantum Neural Networks from Untrusted Cloud Open
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 services from potentially untrusted providers. Deploying or …
View article: Evaluating Efficacy of Model Stealing Attacks and Defenses on Quantum Neural Networks
Evaluating Efficacy of Model Stealing Attacks and Defenses on Quantum Neural Networks Open
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 assess the efficacy of such attacks in the realm of quantum co…
View article: DyPP: Dynamic Parameter Prediction to Accelerate Convergence of Variational Quantum Algorithms
DyPP: Dynamic Parameter Prediction to Accelerate Convergence of Variational Quantum Algorithms Open
The exponential run time of quantum simulators on classical machines and long queue times and high costs of real quantum devices present significant challenges in the efficient optimization of Variational Quantum Algorithms (VQAs) like Var…
View article: Exploring Topological Semi-Metals for Interconnects
Exploring Topological Semi-Metals for Interconnects Open
The size of transistors has drastically reduced over the years. Interconnects have likewise also been scaled down. Today, conventional copper (Cu)-based interconnects face a significant impediment to further scaling since their electrical …
View article: Quantum Machine Learning for Material Synthesis and Hardware Security
Quantum Machine Learning for Material Synthesis and Hardware Security Open
Using quantum computing, this paper addresses two scientifically pressing and day-to-day relevant problems, namely, chemical retrosynthesis which is an important step in drug/material discovery and security of the semiconductor supply chai…
View article: Knowledge Distillation in Quantum Neural Network using Approximate Synthesis
Knowledge Distillation in Quantum Neural Network using Approximate Synthesis Open
Recent assertions of a potential advantage of Quantum Neural Network (QNN) for specific Machine Learning (ML) tasks have sparked the curiosity of a sizable number of application researchers. The parameterized quantum circuit (PQC), a major…
View article: Security Aspects of Quantum Machine Learning: Opportunities, Threats and Defenses
Security Aspects of Quantum Machine Learning: Opportunities, Threats and Defenses Open
In the last few years, quantum computing has experienced a growth spurt. One\nexciting avenue of quantum computing is quantum machine learning (QML) which\ncan exploit the high dimensional Hilbert space to learn richer representations\nfro…
View article: ICCAD Special Session Paper: Quantum-Classical Hybrid Machine Learning for Image Classification
ICCAD Special Session Paper: Quantum-Classical Hybrid Machine Learning for Image Classification Open
Image classification is a major application domain for conventional deep learning (DL). Quantum machine learning (QML) has the potential to revolutionize image classification. In any typical DL-based image classification, we use convolutio…
View article: Quantum-Classical Hybrid Machine Learning for Image Classification (ICCAD Special Session Paper)
Quantum-Classical Hybrid Machine Learning for Image Classification (ICCAD Special Session Paper) Open
Image classification is a major application domain for conventional deep learning (DL). Quantum machine learning (QML) has the potential to revolutionize image classification. In any typical DL-based image classification, we use convolutio…