Artificial stochastic neural network on the base of double quantum wells
- URL: http://arxiv.org/abs/2208.07584v1
- Date: Tue, 16 Aug 2022 07:54:19 GMT
- Title: Artificial stochastic neural network on the base of double quantum wells
- Authors: O. V. Pavlovsky, V. I. Dorozhinsky, S.D. Mostovoy
- Abstract summary: We consider a model of an artificial neural network based on quantum-mechanical particles in $W$ potential.
A form of the self-potential of a particle as well as two interaction potentials (exciting and inhibiting) are proposed.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We consider a model of an artificial neural network based on
quantum-mechanical particles in $W$ potential. These particles play the role of
neurons in our model. To simulate such a quantum-mechanical system the
Monte-Carlo integration method is used. A form of the self-potential of a
particle as well as two interaction potentials (exciting and inhibiting) are
proposed. Examples of simplest logical elements (such as AND, OR and NOT) are
shown. Further we show an implementation of the simplest convolutional network
in framework of our model.
Related papers
- Non-binary artificial neuron with phase variation implemented on a quantum computer [0.0]
We introduce an algorithm that generalizes the binary model manipulating the phase of complex numbers.
We propose, test, and implement a neuron model that works with continuous values in a quantum computer.
arXiv Detail & Related papers (2024-10-30T18:18:53Z) - Fourier Neural Operators for Learning Dynamics in Quantum Spin Systems [77.88054335119074]
We use FNOs to model the evolution of random quantum spin systems.
We apply FNOs to a compact set of Hamiltonian observables instead of the entire $2n$ quantum wavefunction.
arXiv Detail & Related papers (2024-09-05T07:18:09Z) - Pairing-based graph neural network for simulating quantum materials [0.8192907805418583]
We develop a pairing-based graph neural network for simulating quantum many-body systems.
Variational Monte Carlo with our neural network simultaneously provides an accurate, flexible, and scalable method for simulating many-electron systems.
arXiv Detail & Related papers (2023-11-03T17:12:29Z) - Neural network approach to quasiparticle dispersions in doped
antiferromagnets [0.0]
We study the ability of neural quantum states to represent the bosonic and fermionic $t-J$ model on different 1D and 2D lattices.
We present a method to calculate dispersion relations from the neural network state representation.
arXiv Detail & Related papers (2023-10-12T17:59:33Z) - Capturing dynamical correlations using implicit neural representations [85.66456606776552]
We develop an artificial intelligence framework which combines a neural network trained to mimic simulated data from a model Hamiltonian with automatic differentiation to recover unknown parameters from experimental data.
In doing so, we illustrate the ability to build and train a differentiable model only once, which then can be applied in real-time to multi-dimensional scattering data.
arXiv Detail & Related papers (2023-04-08T07:55:36Z) - A real neural network state for quantum chemistry [1.9363665969803923]
The Boltzmann machine (RBM) has been successfully applied to solve the many-electron Schr$ddottexto$dinger equation.
We propose a single-layer fully connected neural network adapted from RBM and apply it to study ab initio quantum chemistry problems.
arXiv Detail & Related papers (2023-01-10T02:21:40Z) - Towards Neural Variational Monte Carlo That Scales Linearly with System
Size [67.09349921751341]
Quantum many-body problems are central to demystifying some exotic quantum phenomena, e.g., high-temperature superconductors.
The combination of neural networks (NN) for representing quantum states, and the Variational Monte Carlo (VMC) algorithm, has been shown to be a promising method for solving such problems.
We propose a NN architecture called Vector-Quantized Neural Quantum States (VQ-NQS) that utilizes vector-quantization techniques to leverage redundancies in the local-energy calculations of the VMC algorithm.
arXiv Detail & Related papers (2022-12-21T19:00:04Z) - Dilute neutron star matter from neural-network quantum states [58.720142291102135]
Low-density neutron matter is characterized by the formation of Cooper pairs and the onset of superfluidity.
We model this density regime by capitalizing on the expressivity of the hidden-nucleon neural-network quantum states combined with variational Monte Carlo and reconfiguration techniques.
arXiv Detail & Related papers (2022-12-08T17:55:25Z) - The Hintons in your Neural Network: a Quantum Field Theory View of Deep
Learning [84.33745072274942]
We show how to represent linear and non-linear layers as unitary quantum gates, and interpret the fundamental excitations of the quantum model as particles.
On top of opening a new perspective and techniques for studying neural networks, the quantum formulation is well suited for optical quantum computing.
arXiv Detail & Related papers (2021-03-08T17:24:29Z) - Emergent Quantumness in Neural Networks [0.0]
We derive the Schr"odinger equation with "Planck's constant" determined by the chemical potential of hidden variables.
We also discuss implications of the results for machine learning, fundamental physics and, in a more speculative way, evolutionary biology.
arXiv Detail & Related papers (2020-12-09T14:32:33Z) - Variational Monte Carlo calculations of $\mathbf{A\leq 4}$ nuclei with
an artificial neural-network correlator ansatz [62.997667081978825]
We introduce a neural-network quantum state ansatz to model the ground-state wave function of light nuclei.
We compute the binding energies and point-nucleon densities of $Aleq 4$ nuclei as emerging from a leading-order pionless effective field theory Hamiltonian.
arXiv Detail & Related papers (2020-07-28T14:52:28Z)
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.