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3D Ising Spin Glass Solutions

Description This dataset provides ground states and energies for three-dimensional Ising spin glass instances with system sizesN=263,678,958,1312,2084,5627.   All configurations were obtained using a cyclic quantum annealing protocol imple

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CreatorZhang, Hao
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Published2024-12-30
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DOI10.5281/zenodo.16547191
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Downloads121
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Licensecc-by-4.0
File Size1.7 MB
Data TypeDataset
Published2024
Licensecc-by-4.0
Total Views1,846
Total Downloads121

Description

This dataset provides ground states and energies for three-dimensional Ising spin glass instances with system sizes
N=263,678,958,1312,2084,5627.

 

All configurations were obtained using a cyclic quantum annealing protocol implemented on the D-Wave quantum annealer, followed by a digital cooling method.

The dataset is associated with the results reported in:

 

H. Zhang & A. Kamenev, “Computational complexity of three-dimensional Ising spin glass: Lessons from D-Wave annealer”, Phys. Rev. Research 7, 033098 (2025).
https://journals.aps.org/prresearch/abstract/10.1103/3bkn-v5rd

 

 

Applications

  • Benchmarking quantum and classical optimization algorithms on large-scale optimization problems

  • Studying 3D Ising spin glasses

  • Providing reference ground states for testing quantum annealing and hybrid quantum-classical methods

 

Data Structure

For each system size N:

SpinGlassData/N_N_realization_r/
 ├── J.npz # Coupling matrix J (dictionary format)
 └── solution.npz # Contains:
 # - "solution": ground state spin configuration
 # - "energy": ground state energy
 
 
A consolidated file all_data.npz is also provided for fast loading.

 

Loading Data

Method 1: Load individual instances

import numpy as np

number_of_nodes_list = [263, 678, 958, 1312, 2084, 5627]
realization_number = 1
solution_list, energy_list = [], []

for N in number_of_nodes_list:
 directory = f'SpinGlassData/N_N_realization_realization_number/'
 J = np.load(directory + "J.npz", allow_pickle=True)["J"].item()
 sol = np.load(directory + "solution.npz", allow_pickle=True)["solution"].item()
 E = np.load(directory + "solution.npz", allow_pickle=True)["energy"].item()
 solution_list.append(sol)
 energy_list.append(E)

Method 2: Load all-in-one file

 
import numpy as np

data = np.load("SpinGlassData/all_data.npz", allow_pickle=True)
J_list = data["J_list"]
ground_energy_list = np.array(data["ground_energy_list"])
ground_state_list = data["ground_state_list"]
N_list = np.array(data["N_list"])


Citation

If you use this dataset, please cite the related publication:

 

H. Zhang & A. Kamenev, “Computational complexity of three-dimensional Ising spin glass: Lessons from D-Wave annealer”, Phys. Rev. Research 7, 033098 (2025).
https://journals.aps.org/prresearch/abstract/10.1103/3bkn-v5rd

 

BibTex

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3D Ising Spin Glass Solutions (Full Dataset)1.7 MB
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ReadmeVia DOI record
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Files are hosted on the source repository. Click download to access the full dataset.

Zhang, Hao (2024). 3D Ising Spin Glass Solutions. https://doi.org/10.5281/zenodo.16547191