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pro vyhledávání: '"Lorenz , Jeanette"'
Publikováno v:
International Conference on Quantum Computing and Engineering (QCE), 2024
Quantum machine learning uses principles from quantum mechanics to process data, offering potential advances in speed and performance. However, previous work has shown that these models are susceptible to attacks that manipulate input data or exploit
Externí odkaz:
http://arxiv.org/abs/2408.01200
Publikováno v:
International Conference on Quantum Computing and Engineering, 2024
Breakthroughs in machine learning (ML) and advances in quantum computing (QC) drive the interdisciplinary field of quantum machine learning to new levels. However, due to the susceptibility of ML models to adversarial attacks, practical use raises sa
Externí odkaz:
http://arxiv.org/abs/2408.00895
As quantum machine learning continues to develop at a rapid pace, the importance of ensuring the robustness and efficiency of quantum algorithms cannot be overstated. Our research presents an analysis of quantum randomized smoothing, how data encodin
Externí odkaz:
http://arxiv.org/abs/2407.18021
In this work, we are introducing a Quantum-Classical Bayesian Neural Network (QCBNN) that is capable to perform uncertainty-aware classification of classical medical dataset. This model is a symbiosis of a classical Convolutional NN that performs ult
Externí odkaz:
http://arxiv.org/abs/2406.06307
Quantum computing promises enabling solving large problem instances, e.g. large linear equation systems with HHL algorithm, once the hardware stack matures. For the foreseeable future quantum computing will remain in the so-called NISQ era, in which
Externí odkaz:
http://arxiv.org/abs/2406.06288
Autor:
Eichhorn, Domenik, Schweikart, Maximilian, Poser, Nick, Fiand, Frederik, Poggel, Benedikt, Lorenz, Jeanette Miriam
The advent of quantum algorithms has initiated a discourse on the potential for quantum speedups for optimization problems. However, several factors still hinder a practical realization of the potential benefits. These include the lack of advanced, e
Externí odkaz:
http://arxiv.org/abs/2405.09115
Advancements in Quantum Computing (QC) and Neural Combinatorial Optimization (NCO) represent promising steps in tackling complex computational challenges. On the one hand, Variational Quantum Algorithms such as QAOA can be used to solve a wide range
Externí odkaz:
http://arxiv.org/abs/2405.07790
Autor:
Monnet, Maureen, Chaabani, Nermine, Dragan, Theodora-Augustina, Schachtner, Balthasar, Lorenz, Jeanette Miriam
Quantum machine learning was recently applied to various applications and leads to results that are comparable or, in certain instances, superior to classical methods, in particular when few training data is available. These results warrant a more in
Externí odkaz:
http://arxiv.org/abs/2405.03027
Quantum Machine Learning investigates the possibility of quantum computers enhancing Machine Learning algorithms. Anomaly segmentation is a fundamental task in various domains to identify irregularities at sample level and can be addressed with both
Externí odkaz:
http://arxiv.org/abs/2404.17613
Autor:
Drăgan, Theodora-Augustina, Tandon, Akshat, Strobel, Carsten, Krauser, Jasper Simon, Lorenz, Jeanette Miriam
Quantum machine learning (QML) as combination of quantum computing with machine learning (ML) is a promising direction to explore, in particular due to the advances in realizing quantum computers and the hoped-for quantum advantage. A field within QM
Externí odkaz:
http://arxiv.org/abs/2404.17499