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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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
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
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
- Website: https://sites.google.com/site/marcinplodzienphysics/home
- Repositories: 1
- Profile: https://github.com/MarcinPlodzien
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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