c3.model

The model class, containing information on the system and its modelling.

Module Contents

class c3.model.Model(subsystems=None, couplings=None, tasks=None, max_excitations=0)

What the theorist thinks about from the system.

Class to store information about our system/problem/device. Different models can represent the same system.

Parameters

• subsystems (list) – List of individual, non-interacting physical components like qubits or resonators

• couplings (list) – List of interaction operators between subsystems, like couplings or drives.

• tasks (list) – Badly named list of processing steps like line distortions and read out modeling

• max_excitations (int) – Allow only up to max_excitations in the system

set_max_excitations(self, max_excitations) → None

Set the maximum number of excitations in the system used for propagation.

read_config(self, filepath: str) → None

Load a file and parse it to create a Model object.

Parameters

filepath (str) – Location of the configuration file

fromdict(self, cfg: dict) → None

Load a file and parse it to create a Model object.

Parameters

cfg (dict) – configuration file

write_config(self, filepath: str) → None

Write dictionary to a HJSON file.

asdict(self) → dict

Return a dictionary compatible with config files.

__str__(self) → str

Return str(self).

set_dressed(self, dressed)

Go to a dressed frame where static couplings have been eliminated.

Parameters

dressed (boolean) –

set_lindbladian(self, lindbladian)

Set whether to include open system dynamics.

Parameters

lindbladian (boolean) –

set_FR(self, use_FR)

Setter for the frame rotation option for adjusting the individual rotating frames of qubits when using gate sequences

get_Hamiltonian(self, signal=None)

Get a hamiltonian with an optional signal. This will return an hamiltonian over time. Can be used e.g. for tuning the frequency of a transmon, where the control hamiltonian is not easily accessible. If max.excitation is non-zero the resulting Hamiltonian is cut accordingly

update_Hamiltonians(self)

Recompute the matrix representations of the Hamiltonians.

update_Lindbladians(self)

Return Lindbladian operators and their prefactors.

update_drift_eigen(self, ordered=True)

Compute the eigendecomposition of the drift Hamiltonian and store both the Eigenenergies and the transformation matrix.

update_dressed(self, ordered=True)

Compute the Hamiltonians in the dressed basis by diagonalizing the drift and applying the resulting transformation to the control Hamiltonians.

get_Frame_Rotation(self, t_final: numpy.float64, freqs: dict, framechanges: dict)

Compute the frame rotation needed to align Lab frame and rotating Eigenframes of the qubits.

Parameters

• t_final (tf.float64) – Gate length

• freqs (list) – Frequencies of the local oscillators.

• framechanges (list) – List of framechanges. A phase shift applied to the control signal to compensate relative phases of drive oscillator and qubit.

Returns

A (diagonal) propagator that adjust phases

Return type

tf.Tensor

get_dephasing_channel(self, t_final, amps)

Compute the matrix of the dephasing channel to be applied on the operation.

Parameters

• t_final (tf.float64) – Duration of the operation.

• amps (dict of tf.float64) – Dictionary of average amplitude on each drive line.

Returns

Matrix representation of the dephasing channel.

Return type

tf.tensor