12 papers
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…
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…
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 stochastic processes with variational autoencoders
Gabriel Fernández-Fernández, Carlo Manzo, Maciej Lewenstein +2
Stochastic processes have found numerous applications in science, as they are broadly used to model a variety of natural phenomena. Due to their intrinsic randomness and uncertaint…
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…
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…