SecureFinAI-Lab/Ising_Model_Instances

Overview

This Ising model dataset is an comprehensive open-source collection of typical and prevalent Ising models used within the physics field. The objective is to provide Ising model systems of high interest to physics and machine learning researchers, featuring key charateristics such as scaling complexity, dimensionality, disorder, and magnetism.

To further promote interdisciplinary physics applications with the ML community, our collection of Ising model datasets are curated from various ML papers/textbooks proposing Ising models applications. We organize the datasets based on dimensionality and geometery of Ising models.

Dataset References

Directory Name	Paper	Reference
ML4P	A high-bias, low-variance introduction to Machine Learning for physicists	1
BCQS	Beyond-classical computation in quantum simulation	2
BIQ	Biq Library	3
DWAVE	Computational complexity of three-dimensional Ising spin glass: Lessons from D-Wave annealer	4
RL	Finding the ground state of spin Hamiltonians with reinforcement learning	5
QGS	Quantum Enhanced Greedy Solver for Optimization Problems	6
QMMC	Quantum-Enhanced Markov Monte Carlo	7
DRL	Searching for spin glass ground states through deep reinforcement learning	8
VAN	Solving Statistical Mechanics Using Variational Autoregressive Networks	9
VNA	Variational Neural Annealing	10

License

The dataset is licensed under OpenMDW-1.0. It is a permissive open model license. It allows anyone to freely use, modify, and redistribute the model materials — including model architectures, parameters, data, and documentation — for any purpose, including for commercial purposes, provided that the license text is included with any redistributed version. There are no restrictions on the use or licensing of any outputs, models, or results derived from the data.