scholarly journals Machine learning outperforms thermodynamics in measuring how well a many-body system learns a drive

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Weishun Zhong ◽  
Jacob M. Gold ◽  
Sarah Marzen ◽  
Jeremy L. England ◽  
Nicole Yunger Halpern
Keyword(s):  
Machine Learning ◽  
Novelty Detection ◽  
Representation Learning ◽  
Many Body ◽  
Discrimination Ability ◽  
Machine Learning Model ◽  
Systems Learning ◽  
Statistical Mechanical ◽  
Far From Equilibrium ◽  
Many Body Systems

AbstractDiverse many-body systems, from soap bubbles to suspensions to polymers, learn and remember patterns in the drives that push them far from equilibrium. This learning may be leveraged for computation, memory, and engineering. Until now, many-body learning has been detected with thermodynamic properties, such as work absorption and strain. We progress beyond these macroscopic properties first defined for equilibrium contexts: We quantify statistical mechanical learning using representation learning, a machine-learning model in which information squeezes through a bottleneck. By calculating properties of the bottleneck, we measure four facets of many-body systems’ learning: classification ability, memory capacity, discrimination ability, and novelty detection. Numerical simulations of a classical spin glass illustrate our technique. This toolkit exposes self-organization that eludes detection by thermodynamic measures: Our toolkit more reliably and more precisely detects and quantifies learning by matter while providing a unifying framework for many-body learning.

Machine learning distributions of quantum ansatz with hierarchical structure

2020 ◽  
Vol 34 (20) ◽  
pp. 2050196
Author(s):  
Haozhen Situ ◽  
Zhimin He
Keyword(s):  
Machine Learning ◽  
Quantum Systems ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Machine Learning Model ◽  
Variational Autoencoder ◽  
Systems Learning ◽  
Near Term ◽  
Learning Measurement

Machine learning techniques can help to represent and solve quantum systems. Learning measurement outcome distribution of quantum ansatz is useful for characterization of near-term quantum computing devices. In this work, we use the popular unsupervised machine learning model, variational autoencoder (VAE), to reconstruct the measurement outcome distribution of quantum ansatz. The number of parameters in the VAE are compared with the number of measurement outcomes. The numerical results show that VAE can efficiently learn the measurement outcome distribution with few parameters. The influence of entanglement on the task is also revealed.

Generalized Kinetic Equation for Far-from-Equilibrium Many-Body Systems

2011 ◽  
Vol 143 (5) ◽  
pp. 1020-1034 ◽  
Author(s):  
C. A. B. Silva ◽  
Aurea R. Vasconcellos ◽  
J. Galvão Ramos ◽  
Roberto Luzzi
Keyword(s):  
Kinetic Equation ◽  
Many Body ◽  
Far From Equilibrium ◽  
Generalized Kinetic Equation ◽  
Many Body Systems

scholarly journals Machine learning active-nematic hydrodynamics

2021 ◽  
Vol 118 (10) ◽  
pp. e2016708118
Author(s):  
Jonathan Colen ◽  
Ming Han ◽  
Rui Zhang ◽  
Steven A. Redford ◽  
Linnea M. Lemma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Initial Conditions ◽  
Molecular Motor ◽  
Machine Learning Algorithms ◽  
Many Body ◽  
Hydrodynamic Parameters ◽  
Many Body Systems ◽  
And Control ◽  
Macroscopic Parameters

Hydrodynamic theories effectively describe many-body systems out of equilibrium in terms of a few macroscopic parameters. However, such parameters are difficult to determine from microscopic information. Seldom is this challenge more apparent than in active matter, where the hydrodynamic parameters are in fact fields that encode the distribution of energy-injecting microscopic components. Here, we use active nematics to demonstrate that neural networks can map out the spatiotemporal variation of multiple hydrodynamic parameters and forecast the chaotic dynamics of these systems. We analyze biofilament/molecular-motor experiments with microtubule/kinesin and actin/myosin complexes as computer vision problems. Our algorithms can determine how activity and elastic moduli change as a function of space and time, as well as adenosine triphosphate (ATP) or motor concentration. The only input needed is the orientation of the biofilaments and not the coupled velocity field which is harder to access in experiments. We can also forecast the evolution of these chaotic many-body systems solely from image sequences of their past using a combination of autoencoders and recurrent neural networks with residual architecture. In realistic experimental setups for which the initial conditions are not perfectly known, our physics-inspired machine-learning algorithms can surpass deterministic simulations. Our study paves the way for artificial-intelligence characterization and control of coupled chaotic fields in diverse physical and biological systems, even in the absence of knowledge of the underlying dynamics.

scholarly journals Machine learning entanglement freedom

2018 ◽  
Vol 16 (08) ◽  
pp. 1840002 ◽  
Author(s):  
Samuel Spillard ◽  
Christopher J. Turner ◽  
Konstantinos Meichanetzidis
Keyword(s):  
Machine Learning ◽  
Optimization Problem ◽  
Alternative Measure ◽  
Free Fermion ◽  
Many Body ◽  
Entanglement Spectrum ◽  
Emergent Phenomena ◽  
Interaction Distance ◽  
Many Body Systems ◽  
The Given

Quantum many-body systems realize many different phases of matter characterized by their exotic emergent phenomena. While some simple versions of these properties can occur in systems of free fermions, their occurrence generally implies that the physics is dictated by an interacting Hamiltonian. The interaction distance has been successfully used to quantify the effect of interactions in a variety of states of matter via the entanglement spectrum [C. J. Turner, K. Meichanetzidis, Z. Papic and J. K. Pachos, Nat. Commun. 8 (2017) 14926, Phys. Rev. B 97 (2018) 125104]. The computation of the interaction distance reduces to a global optimization problem whose goal is to search for the free-fermion entanglement spectrum closest to the given entanglement spectrum. In this work, we employ techniques from machine learning in order to perform this same task. In a supervised learning setting, we use labeled data obtained by computing the interaction distance and predict its value via linear regression. Moving to a semi-supervised setting, we train an autoencoder to estimate an alternative measure to the interaction distance, and we show that it behaves in a similar manner.

Machine Learning Accelerated Genetic Algorithms for Computational Materials Search

2018 ◽  
Author(s):  
Steen Lysgaard ◽  
Paul C. Jennings ◽  
Jens Strabo Hummelshøj ◽  
Thomas Bligaard ◽  
Tejs Vegge
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Au Nanoparticles ◽  
Learning Model ◽  
Energy Calculations ◽  
Atomic Distribution ◽  
Machine Learning Model ◽  
Fold Reduction ◽  
Computational Materials

A machine learning model is used as a surrogate fitness evaluator in a genetic algorithm (GA) optimization of the atomic distribution of Pt-Au nanoparticles. The machine learning accelerated genetic algorithm (MLaGA) yields a 50-fold reduction of required energy calculations compared to a traditional GA.

Sign in / Sign up

Export Citation Format

Share Document