366 citations · 3k across the 53 of their papers we have counts for
13 papers · 1 filter
Characterizing out-of-distribution generalization of neural networks: application to the disordered Su-Schrieffer-Heeger model
Kacper Cybiński, Marcin Płodzień, Michał Tomza +3
Machine learning (ML) is a promising tool for the detection of phases of matter. However, ML models are also known for their black-box construction, which hinders understanding of…
A hybrid quantum algorithm to detect conical intersections
Emiel Koridon, Joana Fraxanet, Alexandre Dauphin +3
Conical intersections are topologically protected crossings between the potential energy surfaces of a molecular Hamiltonian, known to play an important role in chemical processes…
Retrieving space-dependent polarization transformations via near-optimal quantum process tomography
Francesco Di Colandrea, Lorenzo Amato, Roberto Schiattarella +2
An optical waveplate rotating light polarization can be modeled as a single-qubit unitary operator, whose action can be experimentally determined via quantum process tomography. St…
Modern applications of machine learning in quantum sciences
Anna Dawid, Julian Arnold, Borja Requena +26
In this book, we provide a comprehensive introduction to the most recent advances in the application of machine learning methods in quantum sciences. We cover the use of deep learn…
Ultra-long quantum walks via spin-orbit photonics
Francesco Di Colandrea, Amin Babazadeh, Alexandre Dauphin +3
The possibility of fine-tuning the couplings between optical modes is a key requirement in photonic circuits for quantum simulations. In these architectures, emulating the long-tim…
Hessian-based toolbox for reliable and interpretable machine learning in physics
Anna Dawid, Patrick Huembeli, Michał Tomza +2
Machine learning (ML) techniques applied to quantum many-body physics have emerged as a new research field. While the numerical power of this approach is undeniable, the most expre…