anomaly_detection_for_quantum_j1_j2_model

Autoencoder and anomaly detection techniques for discovery of phase diagram boundaries for the quantum J1-J2 model

https://github.com/marcinplodzien/anomaly_detection_for_quantum_j1_j2_model

Science Score: 44.0%

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Repository

Autoencoder and anomaly detection techniques for discovery of phase diagram boundaries for the quantum J1-J2 model

Basic Info
  • Host: GitHub
  • Owner: MarcinPlodzien
  • Language: Jupyter Notebook
  • Default Branch: main
  • Size: 1.87 MB
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  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
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Created about 1 year ago · Last pushed about 1 year ago
Metadata Files
Readme Citation

README.md

AnomalydetectionforquantumJ1J2model

Autoencoder and anomaly detection techniques for discovery of phase diagram boundaries for the quantum J1-J2 model

Owner

  • Name: Marcin Plodzien
  • Login: MarcinPlodzien
  • Kind: user
  • Location: Barcelona
  • Company: ICFO

Marcin Płodzień is a theoretical physicist working in the field of many-body systems with a focus on quantum simulators and deep learning for quantum mechanic.

Citation (CITATION.cff)

# CITATION.cff

cff-version: 1.2.0
message: "If you use this code in your research, please cite it using the following metadata."
title: "Anomaly Detection for Quantum J1-J2 Model"
authors:
  - family-names: "Płodzień"
    given-names: "Marcin"
    orcid: "https://orcid.org/0000-0002-0835-1644"  
date-released: "2025-01-06"  # Adjust the release date as appropriate
version: "1.0.0"  # Update this version based on your release tag
repository-code: "https://github.com/MarcinPlodzien/Anomaly_detection_for_quantum_J1_J2_model"
keywords:
  - anomaly detection
  - quantum mechanics
  - J1-J2 model
  - autoencoder
license: "MIT"  # Replace with your repository's license
abstract: >
  This repository contains code for anomaly detection in the quantum J1-J2 model.
  It includes implementations and methods for identifying and analyzing finite size 
  phase diagram with a focus on leveraging autoencoder techniques.

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