correlators-exact-diagonalization - Correlators-Exact-Diagonalization (beehive)

Release Notes – Version 0.0.0

Overview

This release marks the initial launch of Correlators-Exact-Diagonalization. The project provides a robust framework for performing exact diagonalization on quantum many-body systems with the primary goal of computing various correlators. The code is structured to allow for easy extension and integration with related numerical analysis tools.

New Features

• Exact Diagonalization Engine:
  Implements a core algorithm for computing eigenvalues and eigenvectors of Hamiltonians, ensuring precise solutions for small to moderate system sizes.

• Correlation Function Computation:
  Includes modules to calculate various correlation functions which are essential for analyzing quantum states and system properties.

• Modular and Extensible Design:
  The project is organized into clear, independent modules that facilitate testing, debugging, and further development.

• Documentation & Examples:
  Comprehensive README and inline documentation provide guidance on installation, usage, and potential extensions. Example scripts demonstrate how to run analyses and interpret results.

Improvements

• Optimized numerical routines for enhanced performance during matrix operations.
• Improved code readability and consistent style across modules.
• Added basic test cases to ensure correctness of computations and to support future refactoring.

Bug Fixes

• Resolved an issue related to matrix dimension mismatches during initialization.
• Fixed edge cases in input data handling to prevent runtime errors.

Getting Started

• Installation: Follow the instructions in the README to set up the environment and dependencies.
• Usage: Check the examples directory for scripts demonstrating how to perform exact diagonalization and compute correlators.
• Contribution: Feedback, issues, and pull requests are welcome. Please refer to the contribution guidelines in the repository.

Future Roadmap

• Extend the engine to handle larger system sizes with improved numerical stability.
• Introduce additional observables and more sophisticated correlation metrics.
• Enhance visualization tools to better represent computational results.
• Incorporate community feedback to further streamline usability and functionality.

- Python
Published by petio9671 over 1 year ago