154 citations · 448 across the 31 of their papers we have counts for
6 papers · 1 filter
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…
Interpretable representation learning of quantum data enabled by probabilistic variational autoencoders
Paulin de Schoulepnikoff, Gorka Muñoz-Gil, Hendrik Poulsen Nautrup +1
Interpretable machine learning is rapidly becoming a crucial tool for scientific discovery. Among existing approaches, variational autoencoders (VAEs) have shown promise in extract…
Synthesis of discrete-continuous quantum circuits with multimodal diffusion models
Florian Fürrutter, Zohim Chandani, Ikko Hamamura +2
Efficiently compiling quantum operations remains a major bottleneck in scaling quantum computing. Today's state-of-the-art methods achieve low compilation error by combining search…
Learning to reset in target search problems
Gorka Muñoz-Gil, Hans J. Briegel, Michele Caraglio
Target search problems are central to a wide range of fields, from biological foraging to the optimization algorithms. Recently, the ability to reset the search has been shown to s…