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Found 19 papers, 15 papers with code
Universal representation by Boltzmann machines with Regularised Axons
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.
Learning minimal representations of stochastic processes with variational autoencoders
Stochastic processes have found numerous applications in science, as they are broadly used to model a variety of natural phenomena.
Preface: Characterisation of Physical Processes from Anomalous Diffusion Data
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.
Unsupervised learning of anomalous diffusion data
In this work, we explore the use of unsupervised methods in anomalous diffusion data.
Certificates of quantum many-body properties assisted by machine learning
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
Unsupervised machine learning of topological phase transitions from experimental data
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
Inferring Nonlinear Many-Body Bell Inequalities From Average Two-Body Correlations: Systematic Approach for Arbitrary Spin-j Ensembles
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
Phase Detection with Neural Networks: Interpreting the Black Box
Neural networks (NNs) normally do not allow any insight into the reasoning behind their predictions.
Quantum Physics Disordered Systems and Neural Networks
The imaginary part of the high-harmonic cutoff
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
Efficient training of energy-based models via spin-glass control
We introduce a new family of energy-based probabilistic graphical models for efficient unsupervised learning.
Quantum Compressed Sensing with Unsupervised Tensor-Network Machine Learning
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.
Machine learning method for single trajectory characterization
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.
Quantum simulation for thermodynamics of infinite-size many-body systems by O(10) sites
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
Knotting fractional-order knots with the polarization state of light
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
Circular dichroism in high-order harmonic generation: Heralding topological phases and transitions in Chern insulators
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
Entanglement-guided architectures of machine learning by quantum tensor network
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).
Machine Learning by Two-Dimensional Hierarchical Tensor Networks: A Quantum Information Theoretic Perspective on Deep Architectures
The resemblance between the methods used in studying quantum-many body physics and in machine learning has drawn considerable attention.
Machine Learning by Unitary Tensor Network of Hierarchical Tree Structure
We study the quantum features of the TN states, including quantum entanglement and fidelity.
Review of Tensor Network Contraction Approaches
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
