Global minimization via classical tunneling assisted by collective force
field formation
- URL: http://arxiv.org/abs/2102.03385v3
- Date: Thu, 6 Jan 2022 10:52:32 GMT
- Title: Global minimization via classical tunneling assisted by collective force
field formation
- Authors: Francesco Caravelli, Forrest C. Sheldon, Fabio L. Traversa
- Abstract summary: We describe a phenomenon where the increase of dimensions self-consistently generates a force field due to dynamical instabilities.
We dub this collective and nonperturbative effect a "Lyapunov force" which steers the system towards the global minimum of the potential function.
The mechanism is appealing for its physical relevance in nanoscale physics, and to possible applications in optimization, novel Monte Carlo schemes and machine learning.
- Score: 3.0938904602244346
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Simple dynamical models can produce intricate behaviors in large networks.
These behaviors can often be observed in a wide variety of physical systems
captured by the network of interactions. Here we describe a phenomenon where
the increase of dimensions self-consistently generates a force field due to
dynamical instabilities. This can be understood as an unstable ("rumbling")
tunneling mechanism between minima in an effective potential. We dub this
collective and nonperturbative effect a "Lyapunov force" which steers the
system towards the global minimum of the potential function, even if the full
system has a constellation of equilibrium points growing exponentially with the
system size. The system we study has a simple mapping to a flow network,
equivalent to current-driven memristors. The mechanism is appealing for its
physical relevance in nanoscale physics, and to possible applications in
optimization, novel Monte Carlo schemes and machine learning.
Related papers
- Inferring Relational Potentials in Interacting Systems [56.498417950856904]
We propose Neural Interaction Inference with Potentials (NIIP) as an alternative approach to discover such interactions.
NIIP assigns low energy to the subset of trajectories which respect the relational constraints observed.
It allows trajectory manipulation, such as interchanging interaction types across separately trained models, as well as trajectory forecasting.
arXiv Detail & Related papers (2023-10-23T00:44:17Z) - TANGO: Time-Reversal Latent GraphODE for Multi-Agent Dynamical Systems [43.39754726042369]
We propose a simple-yet-effective self-supervised regularization term as a soft constraint that aligns the forward and backward trajectories predicted by a continuous graph neural network-based ordinary differential equation (GraphODE)
It effectively imposes time-reversal symmetry to enable more accurate model predictions across a wider range of dynamical systems under classical mechanics.
Experimental results on a variety of physical systems demonstrate the effectiveness of our proposed method.
arXiv Detail & Related papers (2023-10-10T08:52:16Z) - Uncovering the Origins of Instability in Dynamical Systems: How
Attention Mechanism Can Help? [0.0]
We show that attention should be directed toward the collective behaviour of imbalanced structures and polarity-driven structural instabilities within the network.
Our study provides a proof of concept to understand why perturbing some nodes of a network may cause dramatic changes in the network dynamics.
arXiv Detail & Related papers (2022-12-19T17:16:41Z) - Dynamics with autoregressive neural quantum states: application to
critical quench dynamics [41.94295877935867]
We present an alternative general scheme that enables one to capture long-time dynamics of quantum systems in a stable fashion.
We apply the scheme to time-dependent quench dynamics by investigating the Kibble-Zurek mechanism in the two-dimensional quantum Ising model.
arXiv Detail & Related papers (2022-09-07T15:50:00Z) - Towards Learning Self-Organized Criticality of Rydberg Atoms using Graph
Neural Networks [0.0]
Criticality (SOC) is a ubiquitous dynamical phenomenon believed to be responsible for the emergence of universal scale-invariant behavior.
We show that we can accurately reproduce the Monte-Carlo SOC dynamics as well as generalize along the two important axes of particle number and particle density.
arXiv Detail & Related papers (2022-07-05T15:43:16Z) - Decimation technique for open quantum systems: a case study with
driven-dissipative bosonic chains [62.997667081978825]
Unavoidable coupling of quantum systems to external degrees of freedom leads to dissipative (non-unitary) dynamics.
We introduce a method to deal with these systems based on the calculation of (dissipative) lattice Green's function.
We illustrate the power of this method with several examples of driven-dissipative bosonic chains of increasing complexity.
arXiv Detail & Related papers (2022-02-15T19:00:09Z) - Forced Variational Integrator Networks for Prediction and Control of
Mechanical Systems [7.538482310185133]
We show that forced variational integrator networks (FVIN) architecture allows us to accurately account for energy dissipation and external forcing.
This can result in highly-data efficient model-based control and can predict on real non-conservative systems.
arXiv Detail & Related papers (2021-06-05T21:39:09Z) - Enhancement of quantum correlations and geometric phase for a driven
bipartite quantum system in a structured environment [77.34726150561087]
We study the role of driving in an initial maximally entangled state evolving under a structured environment.
This knowledge can aid the search for physical setups that best retain quantum properties under dissipative dynamics.
arXiv Detail & Related papers (2021-03-18T21:11:37Z) - Learning Stable Deep Dynamics Models [91.90131512825504]
We propose an approach for learning dynamical systems that are guaranteed to be stable over the entire state space.
We show that such learning systems are able to model simple dynamical systems and can be combined with additional deep generative models to learn complex dynamics.
arXiv Detail & Related papers (2020-01-17T00:04:45Z) - The quantum dynamical map of the spin boson model [0.0]
We present a non-peturbative extension of such map, i.e. that is valid for a general spin coupled to a bosonic environment in a thermal state.
The proposed derivation can be extended to other finite-level open quantum systems including many body, initial system-environment correlated states, multiple-time correlation functions or quantum information protocols.
arXiv Detail & Related papers (2020-01-13T13:37:18Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.