3DSpinGlassWithPbits

This repository contains code and associated data used to generate the results of the paper titled:
“Pushing the Boundary of Quantum Advantage in Hard Combinatorial Optimization with Probabilistic Computers.”

⚙️ Requirements

• MATLAB (developed and tested on recent versions)

⚙️ Usage

1. Clone this repository:

   https://github.com/OPUSLab/3DSpinGlassWithPbits.git
   

2. Open MATLAB and navigate to the cloned directory.

3. Run:

   APT.m

   to use the adaptive parallel tempering (APT) or,

   SQA.m

   to use simulated quantum annealing.

4. The codes will save the lowest energy among all the replicas at the end of annealing in a .mat file.

⚙️ Customization

You can modify the following parameters in the scripts (APT.m or SQA.m):

• logical: 1 for logical instances and 0 for embedded instances
• instanceSize: Size of the instances (8, 10, 12, 15 and 16 can be used for logical instances, 15 can be used for embedded instances)
• instanceID: Which instance to run (0 to 299)
• run_per_instance: Number of independent experiments to run per instance (50 is used in this work)
• total_real_num_sweeps: Total number of sweeps (MCS) to be used
• num_sweeps_per_swap: Number of sweeps to use before making a swap attempt (only used in APT.m)
• num_sweeps_read_per_swap: Number of sweeps to use in a swap attempt (1 is used in this work, only used in APT.m)
• num_internal_replicas : Number of ICM replicas to be used per temperature (only used in APT.m)
• base_seed: Seed to the random number generator
• num_replicas: Number of Trotter replicas to be used (only used in SQA.m)
• GammaX: Magnitude of the transverse field (only used in SQA.m)
• betaAll: Inverse temperature to number of replicas ratio (only used in SQA.m)

⚙️ Plotting

The repository also contains processed data and python codes to generate Figures 2, 3 and 4 of the main text.

Contributing

Contributions to improve the code or extend its functionality are welcome. Please feel free to submit issues or pull requests.

Citations

To cite this work, please cite the following paper: Chowdhury, S., Aadit, N.A., Grimaldi, A. et al. Pushing the boundary of quantum advantage in hard combinatorial optimization with probabilistic computers. Nat Commun 16, 9193 (2025). https://doi.org/10.1038/s41467-025-64235-y

Contact

If you have any questions or suggestions, please open an issue in this repository or contact Shuvro Chowdhury (schowdhury@ucsb.edu).