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Found 3 papers, 1 papers with code
Topological data analysis on noisy quantum computers
In this study, we present NISQ-TDA, a fully implemented end-to-end quantum machine learning algorithm needing only a short circuit-depth, that is applicable to high-dimensional classical data, and with provable asymptotic speedup for certain classes of problems.
Directed Graph Auto-Encoders
We introduce a new class of auto-encoders for directed graphs, motivated by a direct extension of the Weisfeiler-Leman algorithm to pairs of node labels.
PCENet: High Dimensional Surrogate Modeling for Learning Uncertainty
In this paper, we present a novel surrogate model for representation learning and uncertainty quantification, which aims to deal with data of moderate to high dimensions.
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