Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Umeano, Chukwudubem"'
We consider the problem of distinguishing two vectors (visualized as images or barcodes) and learning if they are related to one another. For this, we develop a geometric quantum machine learning (GQML) approach with embedded symmetries that allows f
Externí odkaz:
http://arxiv.org/abs/2409.01496
Autor:
Umeano, Chukwudubem, Jamet, François, Lindoy, Lachlan P., Rungger, Ivan, Kyriienko, Oleksandr
We develop a quantum simulation-based approach for studying properties of strongly correlated magnetic materials at increasing scale. We consider a paradigmatic example of a quantum spin liquid (QSL) state hosted by the honeycomb Kitaev model, and us
Externí odkaz:
http://arxiv.org/abs/2407.04205
We introduce a quantum data embedding protocol based on the preparation of a ground state of a parameterized Hamiltonian. We analyze the corresponding quantum feature map, recasting it as an adiabatic state preparation procedure with Trotterized evol
Externí odkaz:
http://arxiv.org/abs/2404.07174
We provide a quantum protocol to perform topological data analysis (TDA) via the distillation of quantum thermal states. Recent developments of quantum thermal state preparation algorithms reveal their characteristic scaling defined by properties of
Externí odkaz:
http://arxiv.org/abs/2402.15633
Geometric quantum machine learning (GQML) aims to embed problem symmetries for learning efficient solving protocols. However, the question remains if (G)QML can be routinely used for constructing protocols with an exponential separation from classica
Externí odkaz:
http://arxiv.org/abs/2402.03871
We develop a quantum topological data analysis (QTDA) protocol based on the estimation of the density of states (DOS) of the combinatorial Laplacian. Computing topological features of graphs and simplicial complexes is crucial for analyzing datasets
Externí odkaz:
http://arxiv.org/abs/2312.07115
We can learn from analyzing quantum convolutional neural networks (QCNNs) that: 1) working with quantum data can be perceived as embedding physical system parameters through a hidden feature map; 2) their high performance for quantum phase recognitio
Externí odkaz:
http://arxiv.org/abs/2308.16664