Post-processing

Load a QUBO from the dataset¶

In [ ]:

import os
import re
from pathlib import Path
from qubosolver.saveload import load_qubo_dataset
output_directory = Path(str(os.path.abspath("01-dataset-generation-and-loading")).replace("docs/tutorial", "qubosolver_logs/tutorial"))

def load_datasets_by_size(directory: str):
    """
    Loads datasets from a directory by extracting the size from filenames.

    Args:
        directory (str): Path to the directory containing dataset files.

    Returns:
        dict[int, torch.Tensor]: A dictionary where keys are sizes and values are loaded datasets.
    """
    # Regular expression to match filenames like "raw_qubo_dataset_size_{size}.pt"
    pattern = r"raw_qubo_dataset_size_(\d+)\.pt"
    datasets_by_size = {}
    os.makedirs(directory, exist_ok=True)
    for filename in os.listdir(directory):
        match = re.match(pattern, filename)
        if match:
            size = int(match.group(1))
            file_path = os.path.join(directory, filename)
            dataset = load_qubo_dataset(file_path)
            datasets_by_size[size] = dataset
            print(f"Loaded dataset with size {size} from {file_path}")
    return datasets_by_size
datasets = load_datasets_by_size(output_directory)
size = 20
data_size_5 = datasets[size]

qubo_cofficents, first_qubo_solution = data_size_5[9]
print(f"Coefficients : {qubo_cofficents}")

import os import re from pathlib import Path from qubosolver.saveload import load_qubo_dataset output_directory = Path(str(os.path.abspath("01-dataset-generation-and-loading")).replace("docs/tutorial", "qubosolver_logs/tutorial")) def load_datasets_by_size(directory: str): """ Loads datasets from a directory by extracting the size from filenames. Args: directory (str): Path to the directory containing dataset files. Returns: dict[int, torch.Tensor]: A dictionary where keys are sizes and values are loaded datasets. """ # Regular expression to match filenames like "raw_qubo_dataset_size_{size}.pt" pattern = r"raw_qubo_dataset_size_(\d+)\.pt" datasets_by_size = {} os.makedirs(directory, exist_ok=True) for filename in os.listdir(directory): match = re.match(pattern, filename) if match: size = int(match.group(1)) file_path = os.path.join(directory, filename) dataset = load_qubo_dataset(file_path) datasets_by_size[size] = dataset print(f"Loaded dataset with size {size} from {file_path}") return datasets_by_size datasets = load_datasets_by_size(output_directory) size = 20 data_size_5 = datasets[size] qubo_cofficents, first_qubo_solution = data_size_5[9] print(f"Coefficients : {qubo_cofficents}")

Postprocessing after a tabu search heuristic¶

In [ ]:

from qubosolver import QUBOInstance
from qubosolver.config import SolverConfig
from qubosolver.solver import QuboSolver


instance = QUBOInstance(qubo_cofficents)

# Create a SolverConfig object with classical solver options.
config = SolverConfig.from_kwargs(
    use_quantum=False,
    classical_solver_type="dwave_tabu", 
    do_postprocessing=True
)

# Instantiate the classical solver using the dispatcher.
classical_solver = QuboSolver(instance, config)

# Solve the QUBO problem.
solution = classical_solver.solve()
print("Solution result:", solution)

from qubosolver import QUBOInstance from qubosolver.config import SolverConfig from qubosolver.solver import QuboSolver instance = QUBOInstance(qubo_cofficents) # Create a SolverConfig object with classical solver options. config = SolverConfig.from_kwargs( use_quantum=False, classical_solver_type="dwave_tabu", do_postprocessing=True ) # Instantiate the classical solver using the dispatcher. classical_solver = QuboSolver(instance, config) # Solve the QUBO problem. solution = classical_solver.solve() print("Solution result:", solution)