8 citations · 9 across the 5 of their papers we have counts for
4 papers · 1 filter
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…
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…
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…
Hybrid Ground-State Quantum Algorithms based on Neural Schrödinger Forging
Paulin de Schoulepnikoff, Oriel Kiss, Sofia Vallecorsa +2
Entanglement forging based variational algorithms leverage the bi-partition of quantum systems for addressing ground state problems. The primary limitation of these approaches lies…