qcalibrateremote 0.1.4

QCalibrate remote interface

A client library for runnig a custom optimisation with qcalibrate software

Note

The API is experimental and subject to change without a prior notice

Description

The qcalibrateremote package provides interface to QCalibrate optimisation service

Currently two modes are supported.

• pure parameter optimisation

• Random chopped base PWC function optimization

Examples

Use Web UI to create an experiment and define optimization parameters.

Parameter optimisation

# import dependencies
from typing import Dict

from qcalibrateremote import (
    EvaluateFigureOfMerit,
    FigureOfMerit,
    create_optimizer_client,
)

# setup client connection (copy form web UI: https://www.qcalibrate.staging.optimal-control.net:31603)
experiment_id="0xabcd"
token=("ey...")

optimizer_client = create_optimizer_client(
    host="grpc.qcalibrate.staging.optimal-control.net", port=31603, token=token)

# define infidelity evaluation class
class DistanceFom(EvaluateFigureOfMerit):

    def __init__(self, *args, **kwargs) -> None:
        super().__init__()

    def infidelity(self, param1, param2) -> float:
        return (param1 - 0.55)**2 + (param2 - 0.33)**2

    def evaluate(self, parameters: Dict[str, float], **kwargs) -> FigureOfMerit:
        """Abstract method for figure of merit evaluation"""
        # print(parameters)
        return FigureOfMerit(self.infidelity(**parameters), '')

# run optimisation
optimisation_result = optimizer_client.run(experiment_id=experiment_id, evaluate_fom_class=DistanceFom)

# best fiting parameters
optimisation_result.top[0].parameters

Pulse optimisation

# import dependencies
from typing import Dict

from qcalibrateremote import (
    EvaluateFigureOfMerit,
    FigureOfMerit,
    create_optimizer_client,
    Pulse,
)

# setup client connection (copy form web UI: https://www.qcalibrate.staging.optimal-control.net:31603)
experiment_id="0xabcd"
token=("ey...")

optimizer_client = create_optimizer_client(
    host="grpc.qcalibrate.staging.optimal-control.net", port=31603, token=token)

# define infidelity evaluation class
def expected_pulse(t):
    return np.sin(2*np.pi*t)**4

class SineFom(EvaluateFigureOfMerit):

    def evaluate(self, parameters: Dict[str, float], pulses: Dict[str, Pulse], **kwargs) -> FigureOfMerit:
        pulse1 = pulses["pulse1"]

        inf = np.sum((expected_pulse(pulse1.times) - pulse1.values)**2)

        return FigureOfMerit(inf, '{}')

# run optimisation
optimisation_result = optimizer_client.run(experiment_id=experiment_id, evaluate_fom_class=SineFom)

# plot best fiting pulse
pulse1 = optimisation_result.top[0].pulses["pulse1"]
import matplotlib.pyplot as plt

plt.plot(pulse1.times, expected_pulse(pulse1.times))
plt.plot(pulse1.times, pulse1.values)