Drive Shaping Methods¶

A quantum program in the Rydberg analog model is defined as a time-dependent drive Hamiltonian that is imposed on the qubits (in addition to the interaction Hamiltonian). The drive shaping configuration part (the drive_shaping field of SolverConfig) defines how the drive parameters are constructed.

In this notebook, we show how to use different drive shaping methods:

• ADIABATIC (Has no parameters to be customized).
• OPTIMIZED (Has seven parameters that can be customized).

We choose the method when defining the SolverConfig configuration, with drive_shaping_method = DriveType.(method)

Default SolverConfig parameters to run a quantum approach:

• use_quantum: bool = False (for using the drive shaping methods, we have to set it to True.)
• backend: LocalEmulator | RemoteEmulator | QPU (by default, use a LocalEmulator based on qutip)
• device: Device = DigitalAnalogDevice() (also available: AnalogDevice())
• embedding_method: str | EmbedderType | None = EmbedderType.GREEDY (also available: BLADE)
• drive_shaping_method: str | DriveType | None = DriveType.ADIABATIC (also available: OPTIMIZED)

In [ ]:

import torch

from qubosolver.qubo_instance import QUBOInstance
from qubosolver.config import SolverConfig, DriveShapingConfig
from qubosolver.qubo_types import DriveType
from qubosolver.solver import QuboSolver

import matplotlib.pyplot as plt
plt.rcParams["animation.html"] = "jshtml"

%matplotlib inline

import torch from qubosolver.qubo_instance import QUBOInstance from qubosolver.config import SolverConfig, DriveShapingConfig from qubosolver.qubo_types import DriveType from qubosolver.solver import QuboSolver import matplotlib.pyplot as plt plt.rcParams["animation.html"] = "jshtml" %matplotlib inline

** Create the QUBOInstance **

Here, we have a 3x3 QUBO matrix with negative diagonal and positive off-diagonal terms.

In [ ]:

coefficients = torch.tensor([[-1.0, 0.5, 0.2], [0.5, -2.0, 0.3], [0.2, 0.3, -3.0]])
instance = QUBOInstance(coefficients)

coefficients = torch.tensor([[-1.0, 0.5, 0.2], [0.5, -2.0, 0.3], [0.2, 0.3, -3.0]]) instance = QUBOInstance(coefficients)

Standard Adiabatic¶

Default method

In [ ]:

default_config = SolverConfig.from_kwargs(
    use_quantum=True, drive_shaping=DriveShapingConfig(drive_shaping_method=DriveType.ADIABATIC),
)
solver = QuboSolver(instance, default_config)

solution = solver.solve()
print(solution)

default_config = SolverConfig.from_kwargs( use_quantum=True, drive_shaping=DriveShapingConfig(drive_shaping_method=DriveType.ADIABATIC), ) solver = QuboSolver(instance, default_config) solution = solver.solve() print(solution)

QUBOSolution(bitstrings=tensor([[0., 0., 1.],
        [0., 1., 0.],
        [1., 0., 0.],
        [0., 0., 0.]]), costs=tensor([-3., -2., -1.,  0.]), counts=tensor([ 4,  7,  8, 81]), probabilities=tensor([0.0400, 0.0700, 0.0800, 0.8100]), solution_status=<SolutionStatusType.UNPROCESSED: 'unprocessed'>)

If needed, one can access the drive used and draw the sequence used by the quantum solver as follows:

In [ ]:

embedding = solver.embedding()
drive = solver.drive(embedding)[0]
solver.draw_sequence(drive, embedding)

embedding = solver.embedding() drive = solver.drive(embedding)[0] solver.draw_sequence(drive, embedding)

Optimized Drive shaping¶

Parameters to customize:¶

For the OPTIMIZED drive shaping, we have the following parameters:

• optimized_n_calls: Number of calls for the optimization process.
• optimized_re_execute_opt_drive: Whether to re-run the optimal drive sequence after optimization.
• optimized_initial_omega_parameters: Default initial omega parameters for the drive. Defaults to (5, 10, 5).
• optimized_initial_detuning_parameters: Default initial detuning parameters for the drive. Defaults to (-10, 0, 10).

Default configuration¶

In [ ]:

default_config = SolverConfig.from_kwargs(
    use_quantum=True, drive_shaping=DriveShapingConfig(drive_shaping_method=DriveType.OPTIMIZED),
)
solver = QuboSolver(instance, default_config)

solution = solver.solve()
print(solution)

default_config = SolverConfig.from_kwargs( use_quantum=True, drive_shaping=DriveShapingConfig(drive_shaping_method=DriveType.OPTIMIZED), ) solver = QuboSolver(instance, default_config) solution = solver.solve() print(solution)

QUBOSolution(bitstrings=tensor([[0., 0., 1.],
        [0., 1., 0.],
        [1., 0., 0.],
        [0., 0., 0.]]), costs=tensor([-3., -2., -1.,  0.]), counts=tensor([28, 27, 41,  4], dtype=torch.int32), probabilities=tensor([0.2800, 0.2700, 0.4100, 0.0400]), solution_status=<SolutionStatusType.UNPROCESSED: 'unprocessed'>)

Changing optimized_n_calls¶

In [ ]:

default_config = SolverConfig.from_kwargs(
    use_quantum=True, drive_shaping=DriveShapingConfig(drive_shaping_method=DriveType.OPTIMIZED, optimized_n_calls=13),
)
solver = QuboSolver(instance, default_config)

solution = solver.solve()
print(solution)

default_config = SolverConfig.from_kwargs( use_quantum=True, drive_shaping=DriveShapingConfig(drive_shaping_method=DriveType.OPTIMIZED, optimized_n_calls=13), ) solver = QuboSolver(instance, default_config) solution = solver.solve() print(solution)

QUBOSolution(bitstrings=tensor([[0., 0., 1.],
        [0., 1., 0.],
        [1., 0., 0.],
        [0., 0., 0.]]), costs=tensor([-3., -2., -1.,  0.]), counts=tensor([20, 29, 35, 16], dtype=torch.int32), probabilities=tensor([0.2000, 0.2900, 0.3500, 0.1600]), solution_status=<SolutionStatusType.UNPROCESSED: 'unprocessed'>)

Changing the initial parameters¶

Initial parameters of the optimization procedure can be changed as optimized_initial_omega_parameters and optimized_initial_detuning_parameters

In [ ]:

default_config = SolverConfig.from_kwargs(
    use_quantum=True, drive_shaping=DriveShapingConfig(drive_shaping_method=DriveType.OPTIMIZED, optimized_initial_omega_parameters=[2.0, 15.0, 5.0,], optimized_initial_detuning_parameters=[-45.0, 0.0, 25.0]),
)
solver = QuboSolver(instance, default_config)

solution = solver.solve()
print(solution)

default_config = SolverConfig.from_kwargs( use_quantum=True, drive_shaping=DriveShapingConfig(drive_shaping_method=DriveType.OPTIMIZED, optimized_initial_omega_parameters=[2.0, 15.0, 5.0,], optimized_initial_detuning_parameters=[-45.0, 0.0, 25.0]), ) solver = QuboSolver(instance, default_config) solution = solver.solve() print(solution)

QUBOSolution(bitstrings=tensor([[0., 0., 1.],
        [0., 1., 0.],
        [1., 0., 0.],
        [0., 0., 0.]]), costs=tensor([-3., -2., -1.,  0.]), counts=tensor([ 7, 17, 11, 65], dtype=torch.int32), probabilities=tensor([0.0700, 0.1700, 0.1100, 0.6500]), solution_status=<SolutionStatusType.UNPROCESSED: 'unprocessed'>)

Changing optimized_re_execute_opt_drive¶

In [ ]:

default_config = SolverConfig.from_kwargs(
    use_quantum=True, drive_shaping=DriveShapingConfig(drive_shaping_method=DriveType.OPTIMIZED, optimized_re_execute_opt_drive=True),
)
solver = QuboSolver(instance, default_config)

solution = solver.solve()
print(solution)

default_config = SolverConfig.from_kwargs( use_quantum=True, drive_shaping=DriveShapingConfig(drive_shaping_method=DriveType.OPTIMIZED, optimized_re_execute_opt_drive=True), ) solver = QuboSolver(instance, default_config) solution = solver.solve() print(solution)

QUBOSolution(bitstrings=tensor([[0., 0., 1.],
        [0., 1., 0.],
        [1., 0., 0.],
        [0., 0., 0.]]), costs=tensor([-3., -2., -1.,  0.]), counts=tensor([33, 30, 28,  9]), probabilities=tensor([0.3300, 0.3000, 0.2800, 0.0900]), solution_status=<SolutionStatusType.UNPROCESSED: 'unprocessed'>)

Adding custom functions¶

One can change the drive shaping method by incorporating custom functions for:

• Evaluating a candidate bitstring and QUBO via optimized_custom_qubo_cost
• Performing optimization with a different objective than the best cost via optimized_custom_objective
• Adding callback functions via optimized_callback_objective.

In [ ]:

from qubosolver.utils.qubo_eval import calculate_qubo_cost

# example of penalization
def penalized_qubo(bitstring: str, QUBO: torch.Tensor) -> float:
    return calculate_qubo_cost(bitstring, QUBO) + 2 * bitstring.count("0")

# example of saving intermediate results
opt_results = list()
def callback(d: dict) -> None:
    opt_results.append(d)

# example of using an average cost
def average_ojective(
    bitstrings: list,
    counts: list,
    probabilities: list,
    costs: list,
    best_cost: float,
    best_bitstring: str,
) -> float:
    return sum([p * c for p, c in zip(probabilities, costs)])

drive_shaping=DriveShapingConfig(drive_shaping_method=DriveType.OPTIMIZED, 
    optimized_re_execute_opt_drive=True,
    optimized_custom_qubo_cost=penalized_qubo, 
    optimized_callback_objective=callback,
    optimized_custom_objective = average_ojective,
)

config = SolverConfig(
    use_quantum=True, 
    drive_shaping=drive_shaping,
)

from qubosolver.utils.qubo_eval import calculate_qubo_cost # example of penalization def penalized_qubo(bitstring: str, QUBO: torch.Tensor) -> float: return calculate_qubo_cost(bitstring, QUBO) + 2 * bitstring.count("0") # example of saving intermediate results opt_results = list() def callback(d: dict) -> None: opt_results.append(d) # example of using an average cost def average_ojective( bitstrings: list, counts: list, probabilities: list, costs: list, best_cost: float, best_bitstring: str, ) -> float: return sum([p * c for p, c in zip(probabilities, costs)]) drive_shaping=DriveShapingConfig(drive_shaping_method=DriveType.OPTIMIZED, optimized_re_execute_opt_drive=True, optimized_custom_qubo_cost=penalized_qubo, optimized_callback_objective=callback, optimized_custom_objective = average_ojective, ) config = SolverConfig( use_quantum=True, drive_shaping=drive_shaping, )

In [ ]:

solver = QuboSolver(instance, config)
solution = solver.solve()
len(opt_results), opt_results[-1]

solver = QuboSolver(instance, config) solution = solver.solve() len(opt_results), opt_results[-1]

Out[ ]:

(20,
 {'x': [5.008864997401849,
   9.962027508268053,
   7.097581038790153,
   -34.612625398428435,
   2.2551049415033475,
   63.66932478277556],
  'cost_eval': 3.3})

In [ ]:

solution

solution

Out[ ]:

QUBOSolution(bitstrings=tensor([[0., 0., 1.],
        [0., 1., 0.],
        [1., 0., 0.],
        [0., 0., 0.]]), costs=tensor([-3., -2., -1.,  0.]), counts=tensor([35, 24, 34,  7]), probabilities=tensor([0.3500, 0.2400, 0.3400, 0.0700]), solution_status=<SolutionStatusType.UNPROCESSED: 'unprocessed'>)