Example of using the QUBO solver on a pool of small random instances.

# pylint: disable=invalid-name

import numpy as np

from qtealeaves.optimization import QUBOConvergenceParameters, QUBOSolver


def main(qubo_size: int | None = 8, n_instances: int | None = 10) -> None:
    """Example of a QUBO solver routine on random instances."""

    # Create a small pool of small random (integer) QUBO problems
    np.random.seed(11)
    qubo_problems = [
        np.random.randint(-50, 50, (qubo_size, qubo_size)) for _ in range(n_instances)
    ]
    qubo_problems = [qubo + qubo.T for qubo in qubo_problems]

    # Solve the QUBOs and benchmarking with brute-force
    print("\n\n")
    separator = "-" * 60
    for jj, qprob in enumerate(qubo_problems):
        my_solver = QUBOSolver(qprob)
        print(separator)
        print(f"Solving QUBO problem instance n° {jj + 1}......")

        ## Brute-force for benchmarking
        my_solver.solve("brute-force")
        bf_config = my_solver.best_config
        bf_cost = my_solver.best_cost

        print(
            f"  Brute-force solution in {my_solver.runtimes['time-to-solution']:.2f} s"
        )
        print(f"\t Cost --> {bf_cost}")
        print(f"\t Solution --> {bf_config}")

        ## Tensor-network based solver:
        ## define user's parameters for solving the QUBO
        my_conv_params = QUBOConvergenceParameters(
            max_bond_dimension=8,
            tn_ansatz="MPS",  # can be 'TTN'
            data_type="D",
            max_iter=10,
            transverse_field_ratio=1e9,
            tn_io_tag=f"{jj + 1}",
            # the two following parameters automatically define
            # the tensor backend from qtaleaves and / or redtea
            linalg_backend_library="numpy",  # Can be 'torch'
            device="cpu",  # can be 'gpu'
        )

        ## Tensor-network based solver:
        ## call the TN-based solver
        my_solver.solve(tn_conv_params=my_conv_params)

        ## Tensor-network based solver:
        ## process results
        print(
            f"  Tensor-network solution in {my_solver.runtimes['time-to-solution']:.2f} s"
        )
        print(f"\t Cost --> {my_solver.best_cost}")
        print(f"\t Solution --> {my_solver.best_config}")
        print(f"Solving QUBO problem instance n° {jj + 1}......done")
        print(separator, "\n\n")

    # Output
    print(f"\nExample `{__file__}` ran successfully; no asserts implemented.")


if __name__ == "__main__":
    main()