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
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 energyall_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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Files are hosted on the source repository. Click download to access the full dataset.