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Found 6 papers, 1 papers with code
QUACK: Quantum Aligned Centroid Kernel
As a small step in improving the potential applications of QKMs, we introduce QUACK, a quantum kernel algorithm whose time complexity scales linear with the number of samples during training, and independent of the number of training samples in the inference stage.
A Comparative Analysis of Adversarial Robustness for Quantum and Classical Machine Learning Models
While adversarial attacks successfully transfer across this boundary in both directions, we also show that regularization helps quantum networks to be more robust, which has direct impact on Lipschitz bounds and transfer attacks.
Towards Efficient Quantum Anomaly Detection: One-Class SVMs using Variable Subsampling and Randomized Measurements
Quantum computing, with its potential to enhance various machine learning tasks, allows significant advancements in kernel calculation and model precision.
Protecting Publicly Available Data With Machine Learning Shortcuts
Machine-learning (ML) shortcuts or spurious correlations are artifacts in datasets that lead to very good training and test performance but severely limit the model's generalization capability.
Semisupervised Anomaly Detection using Support Vector Regression with Quantum Kernel
Kernel methods based on quantum kernel estimation have emerged as a promising approach to QML on NISQ devices, offering theoretical guarantees, versatility, and compatibility with NISQ constraints.
Deep Reinforcement Learning for Backup Strategies against Adversaries
In this setting, the usual round-robin scheme, which always replaces the oldest backup, is no longer optimal with respect to avoidable exposure.
