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Found 7 papers, 2 papers with code
Quantum Patch-Based Autoencoder for Anomaly Segmentation
Quantum Machine Learning investigates the possibility of quantum computers enhancing Machine Learning algorithms.
Constructing Optimal Noise Channels for Enhanced Robustness in Quantum Machine Learning
With the rapid advancement of Quantum Machine Learning (QML), the critical need to enhance security measures against adversarial attacks and protect QML models becomes increasingly evident.
Quantum Neural Networks under Depolarization Noise: Exploring White-Box Attacks and Defenses
Leveraging the unique properties of quantum mechanics, Quantum Machine Learning (QML) promises computational breakthroughs and enriched perspectives where traditional systems reach their boundaries.
A Hyperparameter Study for Quantum Kernel Methods
Quantum kernel methods are a promising method in quantum machine learning thanks to the guarantees connected to them.
Efficient MILP Decomposition in Quantum Computing for ReLU Network Robustness
We conduct a detailed analysis for the decomposition of MILP with Benders and Dantzig-Wolfe methods.
Diffusion Denoised Smoothing for Certified and Adversarial Robust Out-Of-Distribution Detection
As the use of machine learning continues to expand, the importance of ensuring its safety cannot be overstated.
Quantum-classical convolutional neural networks in radiological image classification
Quantum machine learning is receiving significant attention currently, but its usefulness in comparison to classical machine learning techniques for practical applications remains unclear.
