neuraLQX is an open-source Python package designed for high-performance simulations of canonical loop quantum gravity systems. Built on top of NetKet [1, 2], neuraLQX offers a complete and user-friendly environment for applying the machinery of NetKet specifically in the context of canonical loop quantum gravity with ease.
Note: The package is currently under development and will be released at a later date. Once available, it can be installed via pip. Check back here for the release data later.
Release Date: TBA
Modular and Customizable Design:
- Flexible Quantum Models: Easily construct custom quantum models, including various geometric and quantum operators.
- Custom Graphs: Design and implement custom graphs to fit specific needs.
- Quantum Constraints: Ready-to-use quantum constraints such as Gauß and Euclidean Hamilton constraints, with the ability to solve these using neural networks (see NQS [3]).
- Pre-trained Neural Networks: Includes pre-trained novel neural networks capable of solving constraints across different models, saving time and effort.
Advanced State Characterization and Analysis:
- State Characterization Tools: Analyze states using tools like coloring operators, N-point functions and Rényi entropy.
- Multiple Hilbert Spaces: Support for different Hilbert spaces (e.g. gauge-invariant subspaces).
- Gauge Groups and Spacetime Dimensions: Choose from different gauge groups (e.g., U(1)3, SU(2)) and spacetime dimensions, providing flexibility for diverse simulations.
- Standard Models of Interest: Implementations of some commonly used models, such as the torus universe, are included for convenience.
High-Performance Computing (HPC) Friendly and Ease of Use:
- Parallelization Support: Optimized for HPC environments, allowing for efficient parallel computations.
- Seamless Installation and Setup: Modular designed enabling the bypass technical difficulties, allowing you to focus on simulating systems rather than dealing with implementation issues.
The package is still under development. However, its functionality has already been put to test. You can read about some systems explored using neuraLQX in this paper [4] and this paper [5] too.
You can also see some more in depth examples which showcase the intended usage of the package in our tutorials page.
This package will be available under the Apache 2.0 license. You can read about the Apache 2.0 license here.
[1] F. Vicentini and others, “NetKet 3: Machine Learning Toolbox for Many-Body Quantum Systems,” SciPost Phys. Codeb., vol. 2022, p. 7, 2022, doi: 10.21468/SciPostPhysCodeb.7.
[2] G. Carleo et al., “NetKet: A machine learning toolkit for many-body quantum systems,” SoftwareX, vol. 10, p. 100311, Jul. 2019, doi: 10.1016/j.softx.2019.100311.
[3] G. Carleo and M. Troyer, “Solving the quantum many-body problem with artificial neural networks,” Science, vol. 355, no. 6325, pp. 602–606, 2017, doi: 10.1126/science.aag2302.
[4] H. Sahlmann and W. Sherif, “Towards quantum gravity with neural networks: Solving quantum Hamilton constraints of 3d Euclidean gravity in the weak coupling limit,” May 2024 [arXiv: 2405.00661 [gr-qc]].
[5] H. Sahlmann and W. Sherif, “Towards quantum gravity with neural networks: Solving the quantum Hamilton constraint of U(1) BF theory,” Feb. 2024 [arXiv: 2402.10622 [gr-qc]].