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pro vyhledávání: '"Perrier, Elija"'
Autor:
Perrier, Elija
The use of geometric and symmetry techniques in quantum and classical information processing has a long tradition across the physical sciences as a means of theoretical discovery and applied problem solving. In the modern era, the emergent combinatio
Externí odkaz:
http://arxiv.org/abs/2409.04955
Geometric methods have useful application for solving problems in a range of quantum information disciplines, including the synthesis of time-optimal unitaries in quantum control. In particular, the use of Cartan decompositions to solve problems in o
Externí odkaz:
http://arxiv.org/abs/2404.02358
The availability of large-scale datasets on which to train, benchmark and test algorithms has been central to the rapid development of machine learning as a discipline and its maturity as a research discipline. Despite considerable advancements in re
Externí odkaz:
http://arxiv.org/abs/2108.06661
Autor:
Perrier, Elija
Quantum information technologies, covering quantum computing, quantum communication and quantum sensing, are among the most significant technologies to emerge in recent decades, offering the promise of paradigm-shifting computational capacity with si
Externí odkaz:
http://arxiv.org/abs/2102.00759
Autor:
Perrier, Elija
In this paper, we inaugurate the field of quantum fair machine learning. We undertake a comparative analysis of differences and similarities between classical and quantum fair machine learning algorithms, specifying how the unique features of quantum
Externí odkaz:
http://arxiv.org/abs/2102.00753
Autor:
Perrier, Elija
The ethical consequences of, constraints upon and regulation of algorithms arguably represent the defining challenges of our age, asking us to reckon with the rise of computational technologies whose potential to radically transforming social and ind
Externí odkaz:
http://arxiv.org/abs/2102.04234
The application of machine learning techniques to solve problems in quantum control together with established geometric methods for solving optimisation problems leads naturally to an exploration of how machine learning approaches can be used to enha
Externí odkaz:
http://arxiv.org/abs/2006.11332
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