no code implementations • 22 Oct 2023 • Przemysław R. Grzybowski, Antoni Jankiewicz, Eloy Piñol, David Cirauqui, Dorota H. Grzybowska, Paweł M. Petrykowski, Miguel Ángel García-March, Maciej Lewenstein, Gorka Muñoz-Gil, Alejandro Pozas-Kerstjens
It is widely known that Boltzmann machines are capable of representing arbitrary probability distributions over the values of their visible neurons, given enough hidden ones.
1 code implementation • 21 Jul 2023 • Gabriel Fernández-Fernández, Carlo Manzo, Maciej Lewenstein, Alexandre Dauphin, Gorka Muñoz-Gil
Stochastic processes have found numerous applications in science, as they are broadly used to model a variety of natural phenomena.
no code implementations • 2 Jan 2023 • Carlo Manzo, Gorka Muñoz-Gil, Giovanni Volpe, Miguel Angel Garcia-March, Maciej Lewenstein, Ralf Metzler
Preface to the special issue "Characterisation of Physical Processes from Anomalous Diffusion Data" associated with the Anomalous Diffusion Challenge ( https://andi-challenge. org ) and published in Journal of Physics A: Mathematical and Theoretical.
1 code implementation • 7 Aug 2021 • Gorka Muñoz-Gil, Guillem Guigó i Corominas, Maciej Lewenstein
In this work, we explore the use of unsupervised methods in anomalous diffusion data.
1 code implementation • 5 Mar 2021 • Borja Requena, Gorka Muñoz-Gil, Maciej Lewenstein, Vedran Dunjko, Jordi Tura
A number of standard methods are used to tackle such problems: variational approaches focus on parameterizing a subclass of solutions within the feasible set; in contrast, relaxation techniques have been proposed to approximate it from outside, thus complementing the variational approach by providing ultimate bounds to the global optimal solution.
Transfer Learning Quantum Physics
1 code implementation • 14 Jan 2021 • Niklas Käming, Anna Dawid, Korbinian Kottmann, Maciej Lewenstein, Klaus Sengstock, Alexandre Dauphin, Christof Weitenberg
Recently, machine learning methods have been shown to be an alternative way of localising phase boundaries also from noisy and imperfect data and without the knowledge of the order parameter.
Anomaly Detection Quantum Gases Disordered Systems and Neural Networks Mesoscale and Nanoscale Physics Quantum Physics
1 code implementation • 15 Dec 2020 • Guillem Müller-Rigat, Albert Aloy, Maciej Lewenstein, Irénée Frérot
This very flexible method, whose complexity does not scale with the system size, allows us to systematically improve over all previously-known Bell's inequalities robustly violated by ensembles of quantum spin-$1/2$; and to discover novel families of Bell's inequalities, tailored to spin-squeezed states and many-body spin singlets of arbitrary spin-$j$ ensembles.
Quantum Physics Other Condensed Matter Quantum Gases Strongly Correlated Electrons
1 code implementation • 9 Apr 2020 • Anna Dawid, Patrick Huembeli, Michał Tomza, Maciej Lewenstein, Alexandre Dauphin
Neural networks (NNs) normally do not allow any insight into the reasoning behind their predictions.
Quantum Physics Disordered Systems and Neural Networks
1 code implementation • 29 Feb 2020 • Emilio Pisanty, Marcelo F. Ciappina, Maciej Lewenstein
High-harmonic generation - the emission of high-frequency radiation by the ionization and subsequent recombination of an atomic electron driven by a strong laser field - is widely understood using a quasiclassical trajectory formalism, derived from a saddle-point approximation, where each saddle corresponds to a complex-valued trajectory whose recombination contributes to the harmonic emission.
Quantum Physics Atomic Physics Optics
1 code implementation • 3 Oct 2019 • Alejandro Pozas-Kerstjens, Gorka Muñoz-Gil, Eloy Piñol, Miguel Ángel García-March, Antonio Acín, Maciej Lewenstein, Przemysław R. Grzybowski
We introduce a new family of energy-based probabilistic graphical models for efficient unsupervised learning.
no code implementations • 24 Jul 2019 • Shi-Ju Ran, Zheng-Zhi Sun, Shao-Ming Fei, Gang Su, Maciej Lewenstein
To transfer a specific piece of information with $|\Psi \rangle$, our proposal is to encode such information in the separable state with the minimal distance to the measured state $|\Phi \rangle$ that is obtained by partially measuring on $|\Psi \rangle$ in a designed way.
1 code implementation • 7 Mar 2019 • Gorka Muñoz-Gil, Miguel Angel Garcia-March, Carlo Manzo, José D. Martín-Guerrero, Maciej Lewenstein
In this paper, we propose a machine learning method based on a random forest architecture, which is able to associate even very short trajectories to the underlying diffusion mechanism with a high accuracy.
1 code implementation • 3 Oct 2018 • Shi-Ju Ran, Bin Xi, Cheng Peng, Gang Su, Maciej Lewenstein
In this work we propose to simulate many-body thermodynamics of infinite-size quantum lattice models in one, two, and three dimensions, in terms of few-body models of only O(10) sites, which we coin as quantum entanglement simulators (QES's).
Strongly Correlated Electrons Computational Physics Quantum Physics
2 code implementations • 15 Aug 2018 • Emilio Pisanty, Gerard Jiménez, Verónica Vicuña-Hernández, Antonio Picón, Alessio Celi, Juan P. Torres, Maciej Lewenstein
The fundamental polarization singularities of monochromatic light are normally associated with invariance under coordinated rotations: symmetry operations that rotate the spatial dependence of an electromagnetic field by an angle $\theta$ and its polarization by a multiple $\gamma\theta$ of that angle.
Optics
1 code implementation • 4 Jul 2018 • Alexis Chacón, Dasol Kim, Wei Zhu, Shane P. Kelly, Alexandre Dauphin, Emilio Pisanty, Andrew S. Maxwell, Antonio Picón, Marcelo F. Ciappina, Dong Eon Kim, Christopher Ticknor, Avadh Saxena, Maciej Lewenstein
Topological materials are of interest to both fundamental science and advanced technologies, because topological states are robust with respect to perturbations and dissipation.
Mesoscale and Nanoscale Physics Quantum Physics
1 code implementation • 24 Mar 2018 • Yuhan Liu, Xiao Zhang, Maciej Lewenstein, Shi-Ju Ran
In this work, we implement simple numerical experiments, related to pattern/images classification, in which we represent the classifiers by many-qubit quantum states written in the matrix product states (MPS).
no code implementations • ICLR 2018 • Ding Liu, Shi-Ju Ran, Peter Wittek, Cheng Peng, Raul Blázquez García, Gang Su, Maciej Lewenstein
The resemblance between the methods used in studying quantum-many body physics and in machine learning has drawn considerable attention.
3 code implementations • ICLR 2018 • Ding Liu, Shi-Ju Ran, Peter Wittek, Cheng Peng, Raul Blázquez García, Gang Su, Maciej Lewenstein
We study the quantum features of the TN states, including quantum entanglement and fidelity.
1 code implementation • 30 Aug 2017 • Shi-Ju Ran, Emanuele Tirrito, Cheng Peng, Xi Chen, Gang Su, Maciej Lewenstein
One goal is to provide a systematic introduction of TN contraction algorithms (motivations, implementations, relations, implications, etc.
Computational Physics Statistical Mechanics Strongly Correlated Electrons Applied Physics Quantum Physics