Libraries package¶
Libraries contain a collection of functions that all share a signature to be used interchangably. One entry of a library is selected in the corresponding config file.
Algorithms module¶
Collection of (optimization) algorithms. All entries share a common signature with optional arguments.
- c3.libraries.algorithms.adaptive_scan(x_init, fun=None, fun_grad=None, grad_lookup=None, options={})[source]¶
One dimensional scan of the function values around the initial point, using adaptive sampling
- Parameters
x_init (float) – Initial point
fun (callable) – Goal function
fun_grad (callable) – Function that computes the gradient of the goal function
grad_lookup (callable) – Lookup a previously computed gradient
options (dict) –
Options include
- accuracy_goal: float
Targeted accuracy for the sampling algorithm
- probe_listlist
Points to definitely include in the sampling
- init_pointboolean
Include the initial point in the sampling
- c3.libraries.algorithms.cma_pre_lbfgs(x_init, fun=None, fun_grad=None, grad_lookup=None, options={})[source]¶
Performs a CMA-Es optimization and feeds the result into LBFG-S for further refinement.
- c3.libraries.algorithms.cmaes(x_init, fun=None, fun_grad=None, grad_lookup=None, options={})[source]¶
Wrapper for the pycma implementation of CMA-Es. See also:
http://cma.gforge.inria.fr/apidocs-pycma/
- Parameters
x_init (float) – Initial point.
fun (callable) – Goal function.
fun_grad (callable) – Function that computes the gradient of the goal function.
grad_lookup (callable) – Lookup a previously computed gradient.
options (dict) –
Options of pycma and the following custom options.
- noisefloat
Artificial noise added to a function evaluation.
- init_pointboolean
Force the use of the initial point in the first generation.
- spreadfloat
Adjust the parameter spread of the first generation cloud.
- stop_at_convergenceint
Custom stopping condition. Stop if the cloud shrunk for this number of generations.
- stop_at_sigmafloat
Custom stopping condition. Stop if the cloud shrunk to this standard deviation.
- Returns
Parameters of the best point.
- Return type
np.ndarray
- c3.libraries.algorithms.gcmaes(x_init, fun=None, fun_grad=None, grad_lookup=None, options={})[source]¶
EXPERIMENTAL CMA-Es where every point in the cloud is optimized with LBFG-S and the resulting cloud and results are used for the CMA update.
- c3.libraries.algorithms.grid2D(x_init, fun=None, fun_grad=None, grad_lookup=None, options={})[source]¶
Two dimensional scan of the function values around the initial point.
- Parameters
x_init (float) – Initial point
fun (callable) – Goal function
fun_grad (callable) – Function that computes the gradient of the goal function
grad_lookup (callable) – Lookup a previously computed gradient
options (dict) –
Options include points : int
The number of samples
- boundslist
Range of the scan for both dimensions
- c3.libraries.algorithms.lbfgs(x_init, fun=None, fun_grad=None, grad_lookup=None, options={})[source]¶
Wrapper for the scipy.optimize.minimize implementation of LBFG-S. See also:
https://docs.scipy.org/doc/scipy/reference/optimize.minimize-lbfgsb.html
- Parameters
x_init (float) – Initial point
fun (callable) – Goal function
fun_grad (callable) – Function that computes the gradient of the goal function
grad_lookup (callable) – Lookup a previously computed gradient
options (dict) – Options of scipy.optimize.minimize
- Returns
Scipy result object.
- Return type
Result
- c3.libraries.algorithms.lbfgs_grad_free(x_init, fun=None, fun_grad=None, grad_lookup=None, options={})[source]¶
Wrapper for the scipy.optimize.minimize implementation of LBFG-S. We let the algorithm determine the gradient by its own.
See also:
https://docs.scipy.org/doc/scipy/reference/optimize.minimize-lbfgsb.html
- Parameters
x_init (float) – Initial point
fun (callable) – Goal function
fun_grad (callable) – Function that computes the gradient of the goal function
grad_lookup (callable) – Lookup a previously computed gradient
options (dict) – Options of scipy.optimize.minimize
- Returns
Scipy result object.
- Return type
Result
- c3.libraries.algorithms.single_eval(x_init, fun=None, fun_grad=None, grad_lookup=None, options={})[source]¶
Return the function value at given point.
- Parameters
x_init (float) – Initial point
fun (callable) – Goal function
fun_grad (callable) – Function that computes the gradient of the goal function
grad_lookup (callable) – Lookup a previously computed gradient
options (dict) – Algorithm specific options
- c3.libraries.algorithms.sweep(x_init, fun=None, fun_grad=None, grad_lookup=None, options={})[source]¶
One dimensional scan of the function values around the initial point.
- Parameters
x_init (float) – Initial point
fun (callable) – Goal function
fun_grad (callable) – Function that computes the gradient of the goal function
grad_lookup (callable) – Lookup a previously computed gradient
options (dict) –
Options include points : int
The number of samples
- boundslist
Range of the scan
- c3.libraries.algorithms.tf_adadelta(x_init: numpy.ndarray, fun: Optional[Callable] = None, fun_grad: Optional[Callable] = None, grad_lookup: Optional[Callable] = None, options: dict = {}) scipy.optimize.optimize.OptimizeResult [source]¶
Optimize using TensorFlow Adadelta https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/Adadelta
- Parameters
x_init (np.ndarray) – starting value of parameter(s)
fun (Callable, optional) – function to minimize, by default None
fun_grad (Callable, optional) – gradient of function to minimize, by default None
grad_lookup (Callable, optional) – lookup stored gradients, by default None
options (dict, optional) – optional parameters for optimizer, by default {}
- Returns
SciPy OptimizeResult type object with final parameters
- Return type
OptimizeResult
- c3.libraries.algorithms.tf_adam(x_init: numpy.ndarray, fun: Optional[Callable] = None, fun_grad: Optional[Callable] = None, grad_lookup: Optional[Callable] = None, options: dict = {}) scipy.optimize.optimize.OptimizeResult [source]¶
Optimize using TensorFlow ADAM https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/Adam
- Parameters
x_init (np.ndarray) – starting value of parameter(s)
fun (Callable, optional) – function to minimize, by default None
fun_grad (Callable, optional) – gradient of function to minimize, by default None
grad_lookup (Callable, optional) – lookup stored gradients, by default None
options (dict, optional) – optional parameters for optimizer, by default {}
- Returns
SciPy OptimizeResult type object with final parameters
- Return type
OptimizeResult
- c3.libraries.algorithms.tf_rmsprop(x_init: numpy.ndarray, fun: Optional[Callable] = None, fun_grad: Optional[Callable] = None, grad_lookup: Optional[Callable] = None, options: dict = {}) scipy.optimize.optimize.OptimizeResult [source]¶
Optimize using TensorFlow RMSProp https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/RMSprop
- Parameters
x_init (np.ndarray) – starting value of parameter(s)
fun (Callable, optional) – function to minimize, by default None
fun_grad (Callable, optional) – gradient of function to minimize, by default None
grad_lookup (Callable, optional) – lookup stored gradients, by default None
options (dict, optional) – optional parameters for optimizer, by default {}
- Returns
SciPy OptimizeResult type object with final parameters
- Return type
OptimizeResult
- c3.libraries.algorithms.tf_sgd(x_init: numpy.ndarray, fun: Optional[Callable] = None, fun_grad: Optional[Callable] = None, grad_lookup: Optional[Callable] = None, options: dict = {}) scipy.optimize.optimize.OptimizeResult [source]¶
Optimize using TensorFlow Stochastic Gradient Descent with Momentum https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/SGD
- Parameters
x_init (np.ndarray) – starting value of parameter(s)
fun (Callable, optional) – function to minimize, by default None
fun_grad (Callable, optional) – gradient of function to minimize, by default None
grad_lookup (Callable, optional) – lookup stored gradients, by default None
options (dict, optional) – optional parameters for optimizer, by default {}
- Returns
SciPy OptimizeResult type object with final parameters
- Return type
OptimizeResult
Chip module¶
Component class and subclasses for the components making up the quantum device.
- class c3.libraries.chip.CShuntFluxQubit(name: str, desc: Optional[str] = None, comment: Optional[str] = None, hilbert_dim: Optional[int] = None, calc_dim: Optional[int] = None, EC: Optional[c3.c3objs.Quantity] = None, EJ: Optional[c3.c3objs.Quantity] = None, EL: Optional[c3.c3objs.Quantity] = None, phi: Optional[c3.c3objs.Quantity] = None, phi_0: Optional[c3.c3objs.Quantity] = None, gamma: Optional[c3.c3objs.Quantity] = None, d: Optional[c3.c3objs.Quantity] = None, t1: Optional[c3.c3objs.Quantity] = None, t2star: Optional[c3.c3objs.Quantity] = None, temp: Optional[c3.c3objs.Quantity] = None, anhar: Optional[c3.c3objs.Quantity] = None, params={}, resolution=None)[source]¶
Bases:
c3.libraries.chip.Qubit
- get_Hamiltonian(signal: Optional[Union[dict, bool]] = None, transform: Optional[tensorflow.python.framework.ops.Tensor] = None) tensorflow.python.framework.ops.Tensor [source]¶
Calculate the hamiltonian :returns: Hamiltonian :rtype: tf.Tensor
- get_freq(phi_sig=0)¶
- class c3.libraries.chip.CShuntFluxQubitCos(name: str, desc: Optional[str] = None, comment: Optional[str] = None, hilbert_dim: Optional[int] = None, calc_dim: Optional[int] = None, EC: Optional[c3.c3objs.Quantity] = None, EJ: Optional[c3.c3objs.Quantity] = None, EL: Optional[c3.c3objs.Quantity] = None, phi: Optional[c3.c3objs.Quantity] = None, phi_0: Optional[c3.c3objs.Quantity] = None, gamma: Optional[c3.c3objs.Quantity] = None, d: Optional[c3.c3objs.Quantity] = None, t1: Optional[c3.c3objs.Quantity] = None, t2star: Optional[c3.c3objs.Quantity] = None, temp: Optional[c3.c3objs.Quantity] = None, anhar: Optional[c3.c3objs.Quantity] = None, params=None)[source]¶
Bases:
c3.libraries.chip.Qubit
- get_Hamiltonian(signal: Optional[Union[dict, bool]] = None, transform: Optional[tensorflow.python.framework.ops.Tensor] = None)[source]¶
Compute the Hamiltonian. Multiplies the number operator with the frequency and anharmonicity with the Duffing part and returns their sum.
- Returns
Hamiltonian
- Return type
tf.Tensor
- class c3.libraries.chip.Coupling(name, desc=None, comment=None, strength: Optional[c3.c3objs.Quantity] = None, connected: Optional[List[str]] = None, params=None, hamiltonian_func=None)[source]¶
Bases:
c3.libraries.chip.LineComponent
Represents a coupling behaviour between elements.
- Parameters
strength (Quantity) – coupling strength
connected (list) – all physical components coupled via this specific coupling
- class c3.libraries.chip.Drive(**props)[source]¶
Bases:
c3.libraries.chip.LineComponent
Represents a drive line.
- Parameters
connected (list) – all physical components receiving driving signals via this line
- class c3.libraries.chip.Fluxonium(name: str, desc: Optional[str] = None, comment: Optional[str] = None, hilbert_dim: Optional[int] = None, calc_dim: Optional[int] = None, EC: Optional[c3.c3objs.Quantity] = None, EJ: Optional[c3.c3objs.Quantity] = None, EL: Optional[c3.c3objs.Quantity] = None, phi: Optional[c3.c3objs.Quantity] = None, phi_0: Optional[c3.c3objs.Quantity] = None, gamma: Optional[c3.c3objs.Quantity] = None, t1: Optional[c3.c3objs.Quantity] = None, t2star: Optional[c3.c3objs.Quantity] = None, temp: Optional[c3.c3objs.Quantity] = None, params=None)[source]¶
- class c3.libraries.chip.LineComponent(**props)[source]¶
Bases:
c3.c3objs.C3obj
Represents the components connecting chip elements and drives.
- Parameters
connected (list) – specifies the component that are connected with this line
- class c3.libraries.chip.PhysicalComponent(**props)[source]¶
Bases:
c3.c3objs.C3obj
Represents the components making up a chip.
- Parameters
hilbert_dim (int) – Dimension of the Hilbert space of this component
- get_Hamiltonian(signal: Optional[Union[dict, bool]] = None, transform: Optional[tensorflow.python.framework.ops.Tensor] = None) Dict[str, tensorflow.python.framework.ops.Tensor] [source]¶
Compute the Hamiltonian. :param signal: dictionary with signals to be used a time dependend Hamiltonian. By default “values” key will be used.
If true value control hamiltonian will be returned, used for later combination of signal and hamiltonians.
- Parameters
transform – transform the hamiltonian, e.g. for expressing the hamiltonian in the expressed basis. Use this function if transform will be necessary and signal is given, in order to apply the transform only on single hamiltonians instead of all timeslices.
- get_transformed_hamiltonians(transform: Optional[tensorflow.python.framework.ops.Tensor] = None)[source]¶
get transformed hamiltonians with given applied transformation. The Hamiltonians are assumed to be stored in Hs. :param transform: transform to be applied to the hamiltonians. Default: None for returning the hamiltonians without transformation applied.
- class c3.libraries.chip.Qubit(name, hilbert_dim, desc=None, comment=None, freq: Optional[c3.c3objs.Quantity] = None, anhar: Optional[c3.c3objs.Quantity] = None, t1: Optional[c3.c3objs.Quantity] = None, t2star: Optional[c3.c3objs.Quantity] = None, temp: Optional[c3.c3objs.Quantity] = None, params=None)[source]¶
Bases:
c3.libraries.chip.PhysicalComponent
Represents the element in a chip functioning as qubit.
- Parameters
freq (Quantity) – frequency of the qubit
anhar (Quantity) – anharmonicity of the qubit. defined as w01 - w12
t1 (Quantity) – t1, the time decay of the qubit due to dissipation
t2star (Quantity) – t2star, the time decay of the qubit due to pure dephasing
temp (Quantity) – temperature of the qubit, used to determine the Boltzmann distribution of energy level populations
- get_Hamiltonian(signal: Optional[Union[dict, bool]] = None, transform: Optional[tensorflow.python.framework.ops.Tensor] = None)[source]¶
Compute the Hamiltonian. Multiplies the number operator with the frequency and anharmonicity with the Duffing part and returns their sum.
- Returns
Hamiltonian
- Return type
tf.Tensor
- get_Lindbladian(dims)[source]¶
Compute the Lindbladian, based on relaxation, dephasing constants and finite temperature.
- Returns
Hamiltonian
- Return type
tf.Tensor
- class c3.libraries.chip.Resonator(**props)[source]¶
Bases:
c3.libraries.chip.PhysicalComponent
Represents the element in a chip functioning as resonator.
- Parameters
freq (Quantity) – frequency of the resonator
- get_Hamiltonian(signal: Optional[Union[dict, bool]] = None, transform: Optional[tensorflow.python.framework.ops.Tensor] = None)[source]¶
Compute the Hamiltonian.
- class c3.libraries.chip.SNAIL(name: str, desc: str = ' ', comment: str = ' ', hilbert_dim: int = 4, freq: Optional[c3.c3objs.Quantity] = None, anhar: Optional[c3.c3objs.Quantity] = None, beta: Optional[c3.c3objs.Quantity] = None, t1: Optional[c3.c3objs.Quantity] = None, t2star: Optional[c3.c3objs.Quantity] = None, temp: Optional[c3.c3objs.Quantity] = None, params: Optional[dict] = None)[source]¶
Bases:
c3.libraries.chip.Qubit
Represents the element in a chip functioning as a three wave mixing element also knwon as a SNAIL. Reference: https://arxiv.org/pdf/1702.00869.pdf :param freq: frequency of the qubit :type freq: Quantity :param anhar: anharmonicity of the qubit. defined as w01 - w12 :type anhar: Quantity :param beta: third order non_linearity of the qubit. :type beta: Quantity :param t1: t1, the time decay of the qubit due to dissipation :type t1: Quantity :param t2star: t2star, the time decay of the qubit due to pure dephasing :type t2star: Quantity :param temp: temperature of the qubit, used to determine the Boltzmann distribution
of energy level populations
- Parameters
beta. (Class is mostly an exact copy of the Qubit class. The only difference is the added third order non linearity with a prefactor) –
linearity (The only modification is the get hamiltonian and init hamiltonian definition. Also imported the necessary third order non) –
library. (from the hamiltonian) –
- get_Hamiltonian(signal: Optional[Union[dict, bool]] = None, transform: Optional[tensorflow.python.framework.ops.Tensor] = None)[source]¶
Compute the Hamiltonian. Multiplies the number operator with the frequency and anharmonicity with the Duffing part and returns their sum. :returns: Hamiltonian :rtype: tf.Tensor
- class c3.libraries.chip.Transmon(name: str, desc: Optional[str] = None, comment: Optional[str] = None, hilbert_dim: Optional[int] = None, freq: Optional[c3.c3objs.Quantity] = None, anhar: Optional[c3.c3objs.Quantity] = None, phi: Optional[c3.c3objs.Quantity] = None, phi_0: Optional[c3.c3objs.Quantity] = None, gamma: Optional[c3.c3objs.Quantity] = None, d: Optional[c3.c3objs.Quantity] = None, t1: Optional[c3.c3objs.Quantity] = None, t2star: Optional[c3.c3objs.Quantity] = None, temp: Optional[c3.c3objs.Quantity] = None, params=None)[source]¶
Bases:
c3.libraries.chip.PhysicalComponent
Represents the element in a chip functioning as tunanble transmon qubit.
- Parameters
- get_Hamiltonian(signal: Optional[Union[dict, bool]] = None, transform: Optional[tensorflow.python.framework.ops.Tensor] = None)[source]¶
Compute the Hamiltonian. :param signal: dictionary with signals to be used a time dependend Hamiltonian. By default “values” key will be used.
If true value control hamiltonian will be returned, used for later combination of signal and hamiltonians.
- Parameters
transform – transform the hamiltonian, e.g. for expressing the hamiltonian in the expressed basis. Use this function if transform will be necessary and signal is given, in order to apply the transform only on single hamiltonians instead of all timeslices.
- class c3.libraries.chip.TransmonExpanded(name: str, desc: Optional[str] = None, comment: Optional[str] = None, hilbert_dim: Optional[int] = None, freq: Optional[c3.c3objs.Quantity] = None, anhar: Optional[c3.c3objs.Quantity] = None, phi: Optional[c3.c3objs.Quantity] = None, phi_0: Optional[c3.c3objs.Quantity] = None, gamma: Optional[c3.c3objs.Quantity] = None, d: Optional[c3.c3objs.Quantity] = None, t1: Optional[c3.c3objs.Quantity] = None, t2star: Optional[c3.c3objs.Quantity] = None, temp: Optional[c3.c3objs.Quantity] = None, params=None)[source]¶
Bases:
c3.libraries.chip.Transmon
- get_Hamiltonian(signal: Optional[Union[dict, bool]] = None, transform: Optional[tensorflow.python.framework.ops.Tensor] = None)[source]¶
Compute the Hamiltonian. :param signal: dictionary with signals to be used a time dependend Hamiltonian. By default “values” key will be used.
If true value control hamiltonian will be returned, used for later combination of signal and hamiltonians.
- Parameters
transform – transform the hamiltonian, e.g. for expressing the hamiltonian in the expressed basis. Use this function if transform will be necessary and signal is given, in order to apply the transform only on single hamiltonians instead of all timeslices.
Constants module¶
All physical constants used in other code.
Envelopes module¶
Library of envelope functions.
All functions assume the input of a time vector.
- c3.libraries.envelopes.cosine(t, params)[source]¶
Cosine-shaped envelope. Maximum value is 1, area is given by length.
- Parameters
params (dict) –
- t_finalfloat
Total length of the Gaussian.
- sigma: float
Width of the Gaussian.
- c3.libraries.envelopes.cosine_flattop(t, params)[source]¶
Cosine-shaped envelope. Maximum value is 1, area is given by length.
- Parameters
params (dict) –
- t_finalfloat
Total length of the Gaussian.
- sigma: float
Width of the Gaussian.
- c3.libraries.envelopes.delta_pulse(t, params)[source]¶
Pulse shape which gives an output only at a given time bin
- c3.libraries.envelopes.flattop(t, params)[source]¶
Flattop gaussian with width of length risefall, modelled by error functions.
- Parameters
params (dict) –
- t_upfloat
Center of the ramp up.
- t_downfloat
Center of the ramp down.
- risefallfloat
Length of the ramps.
- c3.libraries.envelopes.flattop_cut(t, params)[source]¶
Flattop gaussian with width of length risefall, modelled by error functions.
- Parameters
params (dict) –
- t_upfloat
Center of the ramp up.
- t_downfloat
Center of the ramp down.
- risefallfloat
Length of the ramps.
- c3.libraries.envelopes.flattop_cut_center(t, params)[source]¶
Flattop gaussian with width of length risefall, modelled by error functions.
- Parameters
params (dict) –
- t_upfloat
Center of the ramp up.
- t_downfloat
Center of the ramp down.
- risefallfloat
Length of the ramps.
- c3.libraries.envelopes.flattop_risefall(t, params)[source]¶
Flattop gaussian with width of length risefall, modelled by error functions.
- Parameters
params (dict) –
- t_finalfloat
Total length of pulse.
- risefallfloat
Length of the ramps. Position of ramps is so that the pulse starts with the start of the ramp-up and ends at the end of the ramp-down
- c3.libraries.envelopes.flattop_risefall_1ns(t, params)[source]¶
Flattop gaussian with fixed width of 1ns.
- c3.libraries.envelopes.fourier_cos(t, params)[source]¶
Fourier basis of the pulse constant pulse (cos).
- Parameters
params (dict) –
- ampslist
Weights of the fourier components
- freqslist
Frequencies of the fourier components
- c3.libraries.envelopes.fourier_sin(t, params)[source]¶
Fourier basis of the pulse constant pulse (sin).
- Parameters
params (dict) –
- ampslist
Weights of the fourier components
- freqslist
Frequencies of the fourier components
- c3.libraries.envelopes.gaussian_der(t, params)[source]¶
Derivative of the normalized gaussian (ifself not normalized).
- c3.libraries.envelopes.gaussian_der_nonorm(t, params)[source]¶
Derivative of the normalized gaussian (ifself not normalized).
- c3.libraries.envelopes.gaussian_nonorm(t, params)[source]¶
Non-normalized gaussian. Maximum value is 1, area is given by length.
- Parameters
params (dict) –
- t_finalfloat
Total length of the Gaussian.
- sigma: float
Width of the Gaussian.
- c3.libraries.envelopes.gaussian_sigma(t, params)[source]¶
Normalized gaussian. Total area is 1, maximum is determined accordingly.
- Parameters
params (dict) –
- t_finalfloat
Total length of the Gaussian.
- sigma: float
Width of the Gaussian.
- c3.libraries.envelopes.pwc_shape(t, params)[source]¶
Piecewise constant pulse while defining only a given number of samples, while interpolating linearly between those. :param t: :param params: t_bin_start/t_bin_end can be used to specify specific range. e.g. timepoints taken from awg.
- c3.libraries.envelopes.pwc_symmetric(t, params)[source]¶
symmetic PWC pulse This works only for inphase component
Estimators module¶
Collection of estimator functions, to compare two sets of (noisy) data.
- c3.libraries.estimators.estimator_reg_deco(func)[source]¶
Decorator for making registry of functions
- c3.libraries.estimators.mean_dist(exp_values, sim_values, exp_stds, shots)[source]¶
Return the root mean squared of the differences.
- c3.libraries.estimators.mean_exp_stds_dist(exp_values, sim_values, exp_stds, shots)[source]¶
Return the mean of the distance in number of exp_stds away.
- c3.libraries.estimators.mean_sim_stds_dist(exp_values, sim_values, exp_stds, shots)[source]¶
Return the mean of the distance in number of exp_stds away.
- c3.libraries.estimators.median_dist(exp_values, sim_values, exp_stds, shots)[source]¶
Return the median of the differences.
- c3.libraries.estimators.neg_loglkh_binom(exp_values, sim_values, exp_stds, shots)[source]¶
Average likelihood of the experimental values with binomial distribution.
Return the likelihood of the experimental values given the simulated values, and given a binomial distribution function.
- c3.libraries.estimators.neg_loglkh_binom_norm(exp_values, sim_values, exp_stds, shots)[source]¶
Average likelihood of the exp values with normalised binomial distribution.
Return the likelihood of the experimental values given the simulated values, and given a binomial distribution function that is normalised to give probability 1 at the top of the distribution.
- c3.libraries.estimators.neg_loglkh_gauss(exp_values, sim_values, exp_stds, shots)[source]¶
Likelihood of the experimental values.
The distribution is assumed to be binomial (approximated by a gaussian).
- c3.libraries.estimators.neg_loglkh_gauss_norm(exp_values, sim_values, exp_stds, shots)[source]¶
Likelihood of the experimental values.
The distribution is assumed to be binomial (approximated by a gaussian) that is normalised to give probability 1 at the top of the distribution.
- c3.libraries.estimators.neg_loglkh_gauss_norm_sum(exp_values, sim_values, exp_stds, shots)[source]¶
Likelihood of the experimental values.
The distribution is assumed to be binomial (approximated by a gaussian) that is normalised to give probability 1 at the top of the distribution.
- c3.libraries.estimators.neg_loglkh_multinom(exp_values, sim_values, exp_stds, shots)[source]¶
Average likelihood of the experimental values with multinomial distribution.
Return the likelihood of the experimental values given the simulated values, and given a multinomial distribution function.
- c3.libraries.estimators.neg_loglkh_multinom_norm(exp_values, sim_values, exp_stds, shots)[source]¶
Average likelihood of the experimental values with multinomial distribution.
Return the likelihood of the experimental values given the simulated values, and given a multinomial distribution function that is normalised to give probability 1 at the top of the distribution.
- c3.libraries.estimators.rms_dist(exp_values, sim_values, exp_stds, shots)[source]¶
Return the root mean squared of the differences.
- c3.libraries.estimators.rms_exp_stds_dist(exp_values, sim_values, exp_stds, shots)[source]¶
Return the root mean squared of the differences measured in exp_stds.
Fidelities module¶
Library of fidelity functions.
- c3.libraries.fidelities.RB(propagators, min_length: int = 5, max_length: int = 500, num_lengths: int = 20, num_seqs: int = 30, logspace=False, lindbladian=False, padding='')[source]¶
- c3.libraries.fidelities.average_infid(ideal: numpy.ndarray, actual: tensorflow.python.framework.ops.Tensor, index: List[int] = [0], dims=[2]) tensorflow.python.framework.constant_op.constant [source]¶
Average fidelity uses the Pauli basis to compare. Thus, perfect gates are always 2x2 (per qubit) and the actual unitary needs to be projected down.
- Parameters
ideal (np.ndarray) – Contains ideal unitary representations of the gate
actual (tf.Tensor) – Contains actual unitary representations of the gate
index (List[int]) – Index of the qubit(s) in the Hilbert space to be evaluated
dims (list) – List of dimensions of qubits
- c3.libraries.fidelities.average_infid_seq(propagators: dict, instructions: dict, index, dims, n_eval=- 1)[source]¶
Average sequence fidelity over all gates in propagators.
- Parameters
propagators (dict) – Contains unitary representations of the gates, identified by a key.
index (int) – Index of the qubit(s) in the Hilbert space to be evaluated
dims (list) – List of dimensions of qubits
proj (boolean) – Project to computational subspace
- Returns
Mean average fidelity
- Return type
tf.float64
- c3.libraries.fidelities.average_infid_set(propagators: dict, instructions: dict, index: List[int], dims, n_eval=- 1)[source]¶
Mean average fidelity over all gates in propagators.
- Parameters
propagators (dict) – Contains unitary representations of the gates, identified by a key.
index (int) – Index of the qubit(s) in the Hilbert space to be evaluated
dims (list) – List of dimensions of qubits
proj (boolean) – Project to computational subspace
- Returns
Mean average fidelity
- Return type
tf.float64
- c3.libraries.fidelities.epc_analytical(propagators: dict, index, dims, proj: bool, cliffords=False)[source]¶
- c3.libraries.fidelities.leakage_RB(propagators, min_length: int = 5, max_length: int = 500, num_lengths: int = 20, num_seqs: int = 30, logspace=False, lindbladian=False)[source]¶
- c3.libraries.fidelities.lindbladian_RB_left(propagators: dict, gate: str, index, dims, proj: bool = False)[source]¶
- c3.libraries.fidelities.lindbladian_RB_right(propagators: dict, gate: str, index, dims, proj: bool)[source]¶
- c3.libraries.fidelities.lindbladian_average_infid(ideal: numpy.ndarray, actual: tensorflow.python.framework.constant_op.constant, index=[0], dims=[2]) tensorflow.python.framework.constant_op.constant [source]¶
Average fidelity uses the Pauli basis to compare. Thus, perfect gates are always 2x2 (per qubit) and the actual unitary needs to be projected down.
- Parameters
ideal (np.ndarray) – Contains ideal unitary representations of the gate
actual (tf.Tensor) – Contains actual unitary representations of the gate
index (int) – Index of the qubit(s) in the Hilbert space to be evaluated
dims (list) – List of dimensions of qubits
- c3.libraries.fidelities.lindbladian_average_infid_set(propagators: dict, instructions: Dict[str, c3.signal.gates.Instruction], index, dims, n_eval)[source]¶
Mean average fidelity over all gates in propagators.
- Parameters
propagators (dict) – Contains unitary representations of the gates, identified by a key.
index (int) – Index of the qubit(s) in the Hilbert space to be evaluated
dims (list) – List of dimensions of qubits
proj (boolean) – Project to computational subspace
- Returns
Mean average fidelity
- Return type
tf.float64
- c3.libraries.fidelities.lindbladian_epc_analytical(propagators: dict, index, dims, proj: bool, cliffords=False)[source]¶
- c3.libraries.fidelities.lindbladian_unitary_infid(ideal: numpy.ndarray, actual: tensorflow.python.framework.constant_op.constant, index=[0], dims=[2]) tensorflow.python.framework.constant_op.constant [source]¶
Variant of the unitary fidelity for the Lindbladian propagator.
- Parameters
ideal (np.ndarray) – Contains ideal unitary representations of the gate
actual (tf.Tensor) – Contains actual unitary representations of the gate
index (List[int]) – Index of the qubit(s) in the Hilbert space to be evaluated
dims (list) – List of dimensions of qubits
- Returns
Overlap fidelity for the Lindblad propagator.
- Return type
tf.float
- c3.libraries.fidelities.lindbladian_unitary_infid_set(propagators: dict, instructions: Dict[str, c3.signal.gates.Instruction], index, dims, n_eval)[source]¶
Variant of the mean unitary fidelity for the Lindbladian propagator.
- Parameters
propagators (dict) – Contains actual unitary representations of the gates, resulting from physical simulation
instructions (dict) – Contains the perfect unitary representations of the gates, identified by a key.
index (List[int]) – Index of the qubit(s) in the Hilbert space to be evaluated
dims (list) – List of dimensions of qubits
n_eval (int) – Number of evaluation
- Returns
Mean overlap fidelity for the Lindblad propagator for all gates in propagators.
- Return type
tf.float
- c3.libraries.fidelities.orbit_infid(propagators, RB_number: int = 30, RB_length: int = 20, lindbladian=False, shots: Optional[int] = None, seqs=None, noise=None)[source]¶
- c3.libraries.fidelities.state_transfer_infid(ideal: numpy.ndarray, actual: tensorflow.python.framework.constant_op.constant, index, dims, psi_0)[source]¶
Single gate state transfer infidelity. The dimensions of psi_0 and ideal need to be compatible and index and dims need to project actual to these same dimensions.
- Parameters
ideal (np.ndarray) – Contains ideal unitary representations of the gate
actual (tf.Tensor) – Contains actual unitary representations of the gate
index (int) – Index of the qubit(s) in the Hilbert space to be evaluated
dims (list) – List of dimensions of qubits
psi_0 (tf.Tensor) – Initial state
- Returns
State infidelity for the selected gate
- Return type
tf.float
- c3.libraries.fidelities.state_transfer_infid_set(propagators: dict, instructions: dict, index, dims, psi_0, n_eval=- 1, proj=True)[source]¶
Mean state transfer infidelity.
- Parameters
propagators (dict) – Contains unitary representations of the gates, identified by a key.
index (int) – Index of the qubit(s) in the Hilbert space to be evaluated
dims (list) – List of dimensions of qubits
psi_0 (tf.Tensor) – Initial state of the device
proj (boolean) – Project to computational subspace
- Returns
State infidelity, averaged over the gates in propagators
- Return type
tf.float
- c3.libraries.fidelities.unitary_infid(ideal: numpy.ndarray, actual: tensorflow.python.framework.ops.Tensor, index: Optional[List[int]] = None, dims=None) tensorflow.python.framework.ops.Tensor [source]¶
Unitary overlap between ideal and actually performed gate.
- Parameters
ideal (np.ndarray) – Ideal or goal unitary representation of the gate.
actual (np.ndarray) – Actual, physical unitary representation of the gate.
index (List[int]) – Index of the qubit(s) in the Hilbert space to be evaluated
gate (str) – One of the keys of propagators, selects the gate to be evaluated
dims (list) – List of dimensions of qubits
- Returns
Unitary fidelity.
- Return type
tf.float
- c3.libraries.fidelities.unitary_infid_set(propagators: dict, instructions: dict, index, dims, n_eval=- 1)[source]¶
Mean unitary overlap between ideal and actually performed gate for the gates in propagators.
- Parameters
propagators (dict) – Contains actual unitary representations of the gates, resulting from physical simulation
instructions (dict) – Contains the perfect unitary representations of the gates, identified by a key.
index (List[int]) – Index of the qubit(s) in the Hilbert space to be evaluated
dims (list) – List of dimensions of qubits
n_eval (int) – Number of evaluation
- Returns
Unitary fidelity.
- Return type
tf.float
Hamiltonians module¶
Library of Hamiltonian functions.
- c3.libraries.hamiltonians.duffing(a)[source]¶
Anharmonic part of the duffing oscillator.
- Parameters
a (Tensor) – Annihilator.
- Returns
Number operator.
- Return type
Tensor
- c3.libraries.hamiltonians.hamiltonian_reg_deco(func)[source]¶
Decorator for making registry of functions
- c3.libraries.hamiltonians.int_XX(anhs)[source]¶
Dipole type coupling.
- Parameters
anhs (Tensor list) – Annihilators.
- Returns
coupling
- Return type
Tensor
- c3.libraries.hamiltonians.int_YY(anhs)[source]¶
Dipole type coupling.
- Parameters
anhs (Tensor list) – Annihilators.
- Returns
coupling
- Return type
Tensor
- c3.libraries.hamiltonians.resonator(a)[source]¶
Harmonic oscillator hamiltonian given the annihilation operator.
- Parameters
a (Tensor) – Annihilator.
- Returns
Number operator.
- Return type
Tensor
- c3.libraries.hamiltonians.third_order(a)[source]¶
- Parameters
a (Tensor) – Annihilator.
- Returns
Tensor – Number operator.
return literally the Hamiltonian a_dag a a + a_dag a_dag a for the use in any Hamiltonian that uses more than
just a resonator or Duffing part. A more general type of quantum element on a physical chip can have this type of interaction.
One example is a three wave mixing element used in signal amplification called a Superconducting non-linear asymmetric inductive eLement
(SNAIL in short). The code is a simple modification of the Duffing function and written in the same style.
- c3.libraries.hamiltonians.x_drive(a)[source]¶
Semiclassical drive.
- Parameters
a (Tensor) – Annihilator.
- Returns
Number operator.
- Return type
Tensor
Sampling module¶
Functions to select samples from a dataset by various criteria.
- c3.libraries.sampling.all(learn_from, batch_size)[source]¶
Return all points.
- Parameters
learn_from (list) – List of data points
batch_size (int) – Number of points to select
- Returns
All indeces.
- Return type
list
- c3.libraries.sampling.even(learn_from, batch_size)[source]¶
Select evenly distanced samples across the set.
- Parameters
learn_from (list) – List of data points
batch_size (int) – Number of points to select
- Returns
Selected indices.
- Return type
list
- c3.libraries.sampling.even_fid(learn_from, batch_size)[source]¶
Select evenly among reached fidelities.
- Parameters
learn_from (list) – List of data points.
batch_size (int) – Number of points to select.
- Returns
Selected indices.
- Return type
list
- c3.libraries.sampling.from_end(learn_from, batch_size)[source]¶
Select from the end.
- Parameters
learn_from (list) – List of data points
batch_size (int) – Number of points to select
- Returns
Selected indices.
- Return type
list
- c3.libraries.sampling.from_start(learn_from, batch_size)[source]¶
Select from the beginning.
- Parameters
learn_from (list) – List of data points
batch_size (int) – Number of points to select
- Returns
Selected indices.
- Return type
list
- c3.libraries.sampling.high_std(learn_from, batch_size)[source]¶
Select points that have a high ratio of standard deviation to mean. Sampling from ORBIT data, points with a high std have the most coherent error, thus might be suitable for model learning. This has yet to be confirmed beyond anecdotal observation.
- Parameters
learn_from (list) – List of data points.
batch_size (int) – Number of points to select.
- Returns
Selected indices.
- Return type
list
Tasks module¶
- class c3.libraries.tasks.ConfusionMatrix(name: str = 'conf_matrix', desc: str = ' ', comment: str = ' ', params=None, **confusion_rows)[source]¶
Bases:
c3.libraries.tasks.Task
Allows for misclassificaiton of readout measurement.
- class c3.libraries.tasks.InitialiseGround(name: str = 'init_ground', desc: str = ' ', comment: str = ' ', init_temp: Optional[c3.c3objs.Quantity] = None, params=None)[source]¶
Bases:
c3.libraries.tasks.Task
Initialise the ground state with a given thermal distribution.
- initialise(drift_ham, lindbladian=False, init_temp=None)[source]¶
Prepare the initial state of the system. At the moment finite temperature requires open system dynamics.
- Parameters
drift_ham (tf.Tensor) – Drift Hamiltonian.
lindbladian (boolean) – Whether to include open system dynamics. Required for Temperature > 0.
init_temp (Quantity) – Temperature of the device.
- Returns
State or density vector
- Return type
tf.Tensor
- class c3.libraries.tasks.MeasurementRescale(name: str = 'meas_rescale', desc: str = ' ', comment: str = ' ', meas_offset: Optional[c3.c3objs.Quantity] = None, meas_scale: Optional[c3.c3objs.Quantity] = None, params=None)[source]¶
Bases:
c3.libraries.tasks.Task
Rescale the result of the measurements. This is usually done to account for preparation errors.
- Parameters
- class c3.libraries.tasks.Task(name: str = ' ', desc: str = ' ', comment: str = ' ', params: Optional[dict] = None)[source]¶
Bases:
c3.c3objs.C3obj
Task that is part of the measurement setup.