Combining Machine Learning with Physics: A Framework for Tracking and Sorting Multiple Dark Solitons

• URL: http://arxiv.org/abs/2111.04881v1
• Date: Mon, 8 Nov 2021 23:49:04 GMT
• Title: Combining Machine Learning with Physics: A Framework for Tracking and Sorting Multiple Dark Solitons
• Authors: Shangjie Guo, Sophia M. Koh, Amilson R. Fritsch, I. B. Spielman, and Justyna P. Zwolak
• Abstract summary: In ultracold atom experiments, data often comes in the form of images which suffer information loss. We describe a framework combining machine learning (ML) models with physics-based traditional analyses. Our trained implementation of this framework -- SolDet -- is publicly available as an open-source python package.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In ultracold atom experiments, data often comes in the form of images which suffer information loss inherent in the techniques used to prepare and measure the system. This is particularly problematic when the processes of interest are complicated, such as interactions among excitations in Bose-Einstein condensates (BECs). In this paper, we describe a framework combining machine learning (ML) models with physics-based traditional analyses to identify and track multiple solitonic excitations in images of BECs. We use an ML-based object detector to locate the solitonic excitations and develop a physics-informed classifier to sort solitonic excitations into physically motivated sub-categories. Lastly, we introduce a quality metric quantifying the likelihood that a specific feature is a kink soliton. Our trained implementation of this framework -- SolDet -- is publicly available as an open-source python package. SolDet is broadly applicable to feature identification in cold atom images when trained on a suitable user-provided dataset.

Related papers

• Quantum-informed simulations for mechanics of materials: DFTB+MBD framework [40.83978401377059]
  We study how quantum effects can modify the mechanical properties of systems relevant to materials engineering. We provide an open-source repository containing all codes, datasets, and examples presented in this work.
  arXiv  Detail & Related papers  (2024-04-05T16:59:01Z)
• Nonnegative/Binary Matrix Factorization for Image Classification using Quantum Annealing [0.0]
  We implement a matrix factorization method using quantum annealing for image classification. Our findings show that when the amount of data, features, and epochs is small, the accuracy of models trained by NBMF is superior to classical machine-learning methods.
  arXiv  Detail & Related papers  (2023-11-02T06:41:27Z)
• A didactic approach to quantum machine learning with a single qubit [68.8204255655161]
  We focus on the case of learning with a single qubit, using data re-uploading techniques. We implement the different proposed formulations in toy and real-world datasets using the qiskit quantum computing SDK.
  arXiv  Detail & Related papers  (2022-11-23T18:25:32Z)
• Dark Solitons in Bose-Einstein Condensates: A Dataset for Many-body Physics Research [0.0]
  We establish a dataset of over $1.6times104$ experimental images of Bose-Einstein condensates containing solitonic excitations. About 33 % of this dataset has manually assigned and carefully curated labels. The remainder is automatically labeled using SolDet -- an implementation of a physics-informed ML data analysis framework.
  arXiv  Detail & Related papers  (2022-05-17T09:53:16Z)
• Which priors matter? Benchmarking models for learning latent dynamics [70.88999063639146]
  Several methods have proposed to integrate priors from classical mechanics into machine learning models. We take a sober look at the current capabilities of these models. We find that the use of continuous and time-reversible dynamics benefits models of all classes.
  arXiv  Detail & Related papers  (2021-11-09T23:48:21Z)
• Tracing Information Flow from Open Quantum Systems [52.77024349608834]
  We use photons in a waveguide array to implement a quantum simulation of the coupling of a qubit with a low-dimensional discrete environment. Using the trace distance between quantum states as a measure of information, we analyze different types of information transfer.
  arXiv  Detail & Related papers  (2021-03-22T16:38:31Z)
• Using Data Assimilation to Train a Hybrid Forecast System that Combines Machine-Learning and Knowledge-Based Components [52.77024349608834]
  We consider the problem of data-assisted forecasting of chaotic dynamical systems when the available data is noisy partial measurements. We show that by using partial measurements of the state of the dynamical system, we can train a machine learning model to improve predictions made by an imperfect knowledge-based model.
  arXiv  Detail & Related papers  (2021-02-15T19:56:48Z)
• Machine-learning enhanced dark soliton detection in Bose-Einstein condensates [0.0]
  We describe an automated classification and positioning system for identifying localized excitations in atomic Bose-Einstein condensates (BECs) We openly publish our labeled dataset of dark solitons, the first of its kind, for further machine learning research.
  arXiv  Detail & Related papers  (2021-01-14T00:44:56Z)
• Fed-Sim: Federated Simulation for Medical Imaging [131.56325440976207]
  We introduce a physics-driven generative approach that consists of two learnable neural modules. We show that our data synthesis framework improves the downstream segmentation performance on several datasets.
  arXiv  Detail & Related papers  (2020-09-01T19:17:46Z)
• A Trainable Optimal Transport Embedding for Feature Aggregation and its Relationship to Attention [96.77554122595578]
  We introduce a parametrized representation of fixed size, which embeds and then aggregates elements from a given input set according to the optimal transport plan between the set and a trainable reference. Our approach scales to large datasets and allows end-to-end training of the reference, while also providing a simple unsupervised learning mechanism with small computational cost.
  arXiv  Detail & Related papers  (2020-06-22T08:35:58Z)

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.