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Evaluating Efficacy of Model Stealing Attacks and Defenses on Quantum Neural Networks
In this study, we assess the efficacy of such attacks in the realm of quantum computing.
DyPP: Dynamic Parameter Prediction to Accelerate Convergence of Variational Quantum Algorithms
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 Variational Quantum Eigensolver (VQE), Quantum Approximate Optimization Algorithm (QAOA) and Quantum Neural Networks (QNNs).
Quantum Machine Learning for Material Synthesis and Hardware Security
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 chain.
Security Aspects of Quantum Machine Learning: Opportunities, Threats and Defenses
In the last few years, quantum computing has experienced a growth spurt.
Quantum-Classical Hybrid Machine Learning for Image Classification (ICCAD Special Session Paper)
We review two proposals on quantum-classical hybrid ML models for image classification namely, Quanvolutional Neural Network and dimension reduction using a classical algorithm followed by QNN.
