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Pasqal Documentation

Optimized Pulse

Optimized Pulse Shaper

Section titled “Optimized Pulse Shaper”

OptimizedPulseShaper uses bayesian optimization to find pulse parameters (amplitude and detuning) in order to solve a QUBO problem using quantum simulation.

It outputs both the optimized pulse and a solution object containing bitstrings, counts, probabilities, and associated costs.

Features:

Section titled “Features:”
  • Computes normalized weights from the QUBO diagonal to support later application of the Detuning Map Modulator (DMM).
  • Uses Bayesian optimization to tune six parameters: three for the Rabi amplitude (Ω\Omega), and three for the global detuning (δ\delta).
  • Executes quantum simulations at each iteration to evaluate candidate pulse parameters and their performance on the QUBO.
  • Returns the final optimized pulse and best QUBO solution, with full metadata (counts, probabilities, and costs).

Initialization Parameters:

Section titled “Initialization Parameters:”
Field Type Description
instance QUBOInstance Qubo instance.
config SolverConfig Configuration for solving.

Pulse Parameterization

Section titled “Pulse Parameterization”

The optimized pulse is built from an InterpolatedWaveform with:

Amplitude: Ω=[0,Ω1,Ω2,Ω3,0]\Omega = [0, \Omega_1, \Omega_2, \Omega_3, 0]

Detuning: δ=[δ1,δ2,δ3]\delta = [\delta_1, \delta_2, \delta_3]

These waveforms:

Always start and end in zero amplitude; Use 3 intermediate amplitude values (Ω1\Omega_1 to Ω3\Omega_3) and 3 detuning values (δ1\delta_1 to δ3\delta_3), which are the parameters optimized.

The pulse starts with an InterpolatedWaveform with the points:

  • Ω=[0,5,10,5,0]\Omega = [0, 5, 10, 5, 0]
  • δ=[−10,0,10]\delta = [-10, 0, 10]

Methods Overview

Section titled “Methods Overview”

  • generate(self, target: Register, instance: QUBOInstance) -> tuple[Pulse, QUBOSolution]: Runs the Bayesian optimization loop and returns the optimized pulse and corresponding solution. Handles fallback cases if simulation fails.

  • build_pulse(self, params: list) -> Pulse: Creates a Pulse from a 6-element parameter list: the first 3 for amplitude, the last 3 for detuning.

  • _compute_norm_weights(self, QUBO: torch.Tensor) -> list[float]: Normalizes the QUBO diagonal weights (used in DMM shaping).

  • run_simulation(...) -> tuple[...]: Runs a simulation of the current pulse on a quantum backend and returns bitstring results, probabilities, and QUBO costs.

  • compute_qubo_cost(self, bitstring: str, QUBO: torch.Tensor) -> float: Computes the QUBO cost of a specific bitstring.

Output Structure

Section titled “Output Structure”

After the final round of optimization, the following attributes are populated:

  • pulse: Final Pulse object with optimized waveform parameters.
  • best_cost: Minimum cost found during optimization.
  • best_bitstring: Corresponding bitstring with the lowest cost.
  • bitstrings, counts, probabilities, costs: Full result distributions as PyTorch tensors.

Example

Section titled “Example”

import torch


from qubosolver import QUBOInstance
from qubosolver.config import SolverConfig, PulseShapingConfig
from qoolqit._solvers.types import BackendType, DeviceType
from qubosolver.solver import QuboSolver
from qubosolver.qubo_types import PulseType




Q = torch.tensor([[-63.9423,   0.0000], [0.0000, -44.1916]])


instance = QUBOInstance(Q)


default_config = SolverConfig(
    use_quantum = True, pulse_shaping=PulseShapingConfig(pulse_shaping_method=PulseType.OPTIMIZED), n_calls = 25
)
solver = QuboSolver(instance, default_config)


solution = solver.solve()
print(solution)
QUBOSolution(bitstrings=tensor([[1, 1]]), costs=tensor([-108.1339]), counts=None, probabilities=None, solution_status=) This will return a QUBOSolution instance, which comprehends the solution bitstrings, the counts of each bitstring, their probabilities and costs.