Pascal Debus
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View article: Old Rules in a New Game: Mapping Uncertainty Quantification to Quantum Machine Learning
Old Rules in a New Game: Mapping Uncertainty Quantification to Quantum Machine Learning Open
One of the key obstacles in traditional deep learning is the reduction in model transparency caused by increasingly intricate model functions, which can lead to problems such as overfitting and excessive confidence in predictions. With the…
View article: Quantum Support Vector Regression for Robust Anomaly Detection
Quantum Support Vector Regression for Robust Anomaly Detection Open
Anomaly Detection (AD) is critical in data analysis, particularly within the domain of IT security. In this study, we explore the potential of Quantum Machine Learning for application to AD with special focus on the robustness to noise and…
View article: Quantum Autoencoder for Multivariate Time Series Anomaly Detection
Quantum Autoencoder for Multivariate Time Series Anomaly Detection Open
Anomaly Detection (AD) defines the task of identifying observations or events that deviate from typical - or normal - patterns, a critical capability in IT security for recognizing incidents such as system misconfigurations, malware infect…
View article: Quantum Machine Learning Playground
Quantum Machine Learning Playground Open
This article introduces an innovative interactive visualization tool designed to demystify quantum machine learning (QML) algorithms. Our work is inspired by the success of classical machine learning visualization tools, such as TensorFlow…
View article: Efficient Quantum One-Class Support Vector Machines for Anomaly Detection Using Randomized Measurements and Variable Subsampling
Efficient Quantum One-Class Support Vector Machines for Anomaly Detection Using Randomized Measurements and Variable Subsampling Open
Quantum one-class support vector machines leverage the advantage of quantum kernel methods for semi-supervised anomaly detection. However, their quadratic time complexity with respect to data size poses challenges when dealing with large d…
View article: QUACK: Quantum Aligned Centroid Kernel
QUACK: Quantum Aligned Centroid Kernel Open
Quantum computing (QC) seems to show potential for application in machine learning (ML). In particular quantum kernel methods (QKM) exhibit promising properties for use in supervised ML tasks. However, a major disadvantage of kernel method…
View article: Towards Classical Software Verification using Quantum Computers
Towards Classical Software Verification using Quantum Computers Open
We explore the possibility of accelerating the formal verification of classical programs with a quantum computer. A common source of security flaws stems from the existence of common programming errors like use after free, null-pointer der…
View article: A Comparative Analysis of Adversarial Robustness for Quantum and Classical Machine Learning Models
A Comparative Analysis of Adversarial Robustness for Quantum and Classical Machine Learning Models Open
Quantum machine learning (QML) continues to be an area of tremendous interest from research and industry. While QML models have been shown to be vulnerable to adversarial attacks much in the same manner as classical machine learning models…
View article: Towards Efficient Quantum Anomaly Detection: One-Class SVMs Using Variable Subsampling and Randomized Measurements
Towards Efficient Quantum Anomaly Detection: One-Class SVMs Using Variable Subsampling and Randomized Measurements Open
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View article: Towards Efficient Quantum Anomaly Detection: One-Class SVMs using Variable Subsampling and Randomized Measurements
Towards Efficient Quantum Anomaly Detection: One-Class SVMs using Variable Subsampling and Randomized Measurements Open
Quantum computing, with its potential to enhance various machine learning tasks, allows significant advancements in kernel calculation and model precision. Utilizing the one-class Support Vector Machine alongside a quantum kernel, known fo…
View article: Protecting Publicly Available Data With Machine Learning Shortcuts
Protecting Publicly Available Data With Machine Learning Shortcuts Open
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. Such shortcuts are insidious because they go …
View article: Semisupervised Anomaly Detection using Support Vector Regression with Quantum Kernel
Semisupervised Anomaly Detection using Support Vector Regression with Quantum Kernel Open
Anomaly detection (AD) involves identifying observations or events that deviate in some way from the rest of the data. Machine learning techniques have shown success in automating this process by detecting hidden patterns and deviations in…
View article: Distributed Anomaly Detection of Single Mote Attacks in RPL Networks
Distributed Anomaly Detection of Single Mote Attacks in RPL Networks Open
RPL, a protocol for IP packet routing in wireless sensor networks, is known to be susceptible to a wide range of attacks. Especially effective are ’single mote attacks’, where the attacker only needs to control a single sensor node. These …