154 citations · 448 across the 31 of their papers we have counts for
32 papers
Emergent aggregation from collective foraging
Gorka Muñoz-Gil, Andrea López-Incera, Vide Ramsten +3
Collective behaviour in living systems is usually modelled as the outcome of a \emph{direct} social drive: agents are rewarded, or hard-wired, to align with or approach their neigh…
Discovering quantum phenomena with Interpretable Machine Learning
Paulin de Schoulepnikoff, Hendrik Poulsen Nautrup, Hans J. Briegel +1
Interpretable machine learning techniques are becoming essential tools for extracting physical insights from complex quantum data. We build on recent advances in variational autoen…
Disentanglement by means of action-induced representations
Gorka Muñoz-Gil, Hendrik Poulsen Nautrup, Arunava Majumder +4
Learning interpretable representations with variational autoencoders (VAEs) is a major goal of representation learning. The main challenge lies in obtaining disentangled representa…
Learning Minimal Representations of Many-Body Physics from Snapshots of a Quantum Simulator
Frederik Møller, Gabriel Fernández-Fernández, Thomas Schweigler +3
Analog quantum simulators provide access to many-body dynamics beyond the reach of classical computation. However, extracting physical insights from experimental data is often hind…
Run-and-Tumble Particles Learning Chemotaxis
Nicholas Tovazzi, Gorka Muñoz-Gil, Michele Caraglio
Through evolution, bacteria have developed the ability to perform chemotactic motion in order to find nourishment. By adopting a machine learning approach, we aim to understand how…
Quantum computing and artificial intelligence: status and perspectives
Giovanni Acampora, Andris Ambainis, Natalia Ares +36
This white paper discusses and explores the various points of intersection between quantum computing and artificial intelligence (AI). It describes how quantum computing could supp…