Magnetohydrosensor module

This code was taken from https://qml.baidu.com/tutorials/quantum-simulation/variational-quantum-metrology.html#2-variational-quantum-sensors we've modifide it by adding a class called MagnetoHydroSensor that takes as input the number of sensors and added a method to the class that take in a list of magnetic fields that are used to calibrate the sensor to increase it's sensitvity.

magnetohydrosensor.MSE(qnn: Layer, phi: float) → Tensor

Calculate MSE

Parameters:

  • cir -- variational Ramsey interferometer

  • phi -- unknown parameter

  • a -- parameter of the estimator

Returns:

MSE

class magnetohydrosensor.MagnetoHydroSensor(default_field=0, sensors_count=10)

Bases: object

feedback_loop(sensors_fields)
plot()
magnetohydrosensor.RamseyCircuit(theta_EN: Tensor, theta_DE: Tensor, input_phi: float) → Circuit

Construct variational Ramsey interferometer

Parameters:

  • theta_EN -- the parameters of encoding circuit, shape is [depth_En, num_qubits，3]

  • theta_DE -- the parameters of decoding circui, shape is [depth_De, num_qubits，3]

  • input_phi -- unknown parameter

Returns:

Circuit

class magnetohydrosensor.RamseyInterferometer

Bases: Layer

Variational Ramsey interferometer

forward(fields_list) → Tensor

Calculate loss

opt(fields_list) → None

Optimize QNN

magnetohydrosensor.calculate_m(num_qubits: int) → List[int]
magnetohydrosensor.loss_func(qnn: Layer, magnetic_fields_list: list)

Calculate loss

Parameters:

  • qnn -- a QNN

  • sampling_times -- the number of partitions in the selected interval

  • mean -- the mean of a normal distribution

  • variance -- the variance of a normal distribution

magnetohydrosensor.optimization(qnn: Layer, fields_list, num_itr: int, learning_rate: float) → None

Optimize QNN

Parameters:

  • qnn -- a QNN

  • num_itr -- the number of optimization iterations

  • learning_rate -- learning rate

magnetohydrosensor.std_dev(numbers)

This function takes a list of numbers as input and returns their standard deviation.

magnetohydrosensor.std_mean(numbers)

This function takes a list of numbers as input and returns their mean.