pygsti.models.modelconstruction.create_cloud_crosstalk_model#
- create_cloud_crosstalk_model(processor_spec, custom_gates=None, depolarization_strengths=None, stochastic_error_probs=None, lindblad_error_coeffs=None, depolarization_parameterization='depolarize', stochastic_parameterization='stochastic', lindblad_parameterization='auto', evotype='default', simulator='auto', independent_gates=False, independent_spam=True, errcomp_type='gates', implicit_idle_mode='none', basis='pp', verbosity=0)#
Create a n-qudit “cloud-crosstalk” model.
In a cloud crosstalk model, gates consist of a (local) ideal gates followed by an error operation that can act nontrivially on any of the processor’s qudits (not just a gate’s target qudits). Typically a gate’s errors are specified relative to the gate’s target qudits, forming a “cloud” of errors around the target qudits using some notion of locality (that may not be spatial, e.g. local in frequency). Currently, the “ideal” portion of each gate can only be created as a static (parameterless) object – all gate parameters come from the error operation.
Errors can be specified using any combination of the 4 error rate/coeff arguments, but each gate name must be provided exclusively to one type of specification. Each specification results in a different type of operation, depending on the parameterization:
depolarization_strengths -> DepolarizeOp, StochasticNoiseOp, or exp(LindbladErrorgen)
stochastic_error_probs -> StochasticNoiseOp or exp(LindbladErrorgen)
lindblad_error_coeffs -> exp(LindbladErrorgen)
In addition to the gate names, the special values “prep” and “povm” may be used as keys to specify the error on the state preparation, measurement, respectively.
- Parameters:
processor_spec (ProcessorSpec) – The processor specification to create a model for. This object specifies the gate names and unitaries for the processor, and their availability on the processor.
custom_gates (dict, optional) – A dictionary that associates with gate labels
LinearOperator,OpFactory, or numpy.ndarray objects. These objects override any other behavior for constructing their designated operations. Keys of this dictionary may be string-type gate names or labels that include target qudits.depolarization_strengths (dict, optional) – A dictionary whose keys are gate names (e.g. “Gx”) and whose values are floats that specify the strength of uniform depolarization.
stochastic_error_probs (dict, optional) – A dictionary whose keys are gate names (e.g. “Gx”) and whose values are tuples that specify Pauli-stochastic rates for each of the non-trivial Paulis (so a 3-tuple would be expected for a 1Q gate and a 15-tuple for a 2Q gate).
lindblad_error_coeffs (dict, optional) – A dictionary whose keys are gate names (e.g. “Gx”) and whose values are dictionaries corresponding to the lindblad_term_dict kwarg taken by LindbladErrorgen. Keys are (termType, basisLabel1, <basisLabel2>) tuples, where termType can be “H” (Hamiltonian), “S” (Stochastic), or “A” (Affine). Hamiltonian and Affine terms always have a single basis label (so key is a 2-tuple) whereas Stochastic tuples with 1 basis label indicate a diagonal term, and are the only types of terms allowed when nonham_mode != “all”. Otherwise, Stochastic term tuples can include 2 basis labels to specify “off-diagonal” non-Hamiltonian Lindblad terms. Basis labels can be strings or integers. Values are complex coefficients.
depolarization_parameterization (str of {"depolarize", "stochastic", or "lindblad"}) – Determines whether a DepolarizeOp, StochasticNoiseOp, or LindbladErrorgen is used to parameterize the depolarization noise, respectively. When “depolarize” (the default), a DepolarizeOp is created with the strength given in depolarization_strengths. When “stochastic”, the depolarization strength is split evenly among the stochastic channels of a StochasticOp. When “lindblad”, the depolarization strength is split evenly among the coefficients of the stochastic error generators (which are exponentiated to form a LindbladErrorgen with the “depol” parameterization).
stochastic_parameterization (str of {"stochastic", or "lindblad"}) – Determines whether a StochasticNoiseOp or LindbladErrorgen is used to parameterize the stochastic noise, respectively. When “stochastic”, elements of stochastic_error_probs are used as coefficients in a linear combination of stochastic channels (the default). When “lindblad”, the elements of stochastic_error_probs are coefficients of stochastic error generators (which are exponentiated to form a LindbladErrorgen with the “cptp” parameterization).
lindblad_parameterization ("auto" or a LindbladErrorgen paramtype) – Determines the parameterization of the LindbladErrorgen. When “auto” (the default), the parameterization is inferred from the types of error generators specified in the lindblad_error_coeffs dictionaries. When not “auto”, the parameterization type is passed through to the LindbladErrorgen.
evotype (Evotype or str, optional) – The evolution type. The special value “default” is equivalent to specifying the value of pygsti.evotypes.Evotype.default_evotype.
simulator (ForwardSimulator or {"auto", "matrix", "map"}) – The simulator used to compute predicted probabilities for the resulting
Model. Using “auto” currently selects “map”.independent_gates (bool, optional) – Whether gates are allowed independent noise or not. If False, then all gates with the same name (e.g. “Gx”) will have the same noise (e.g. an overrotation by 1 degree), and the operation_bks[‘cloudnoise’] dictionary will contains a single key per gate name. If True, then gates with the same name acting on different qudits may have different local noise, and so the operation_bks[‘cloudnoise’] dictionary contains a key for each gate available gate placement.
independent_spam (bool, optional) – Similar to independent_gates but for SPAM operations.
errcomp_type ({'gates', 'errorgens'}) – Whether errors should be combined by composing error maps (gates) or by exponentiating the sum of error generators (composing the error generators, errorgens). The latter is only an option when the noise is given solely in terms of Lindblad error coefficients.
implicit_idle_mode ({'none', 'add_global', 'pad_1Q'}) – The way idle operations are added implicitly within the created model. “none” doesn’t add any “extra” idle operations when there is a layer that contains some gates but not gates on all the qudits. “add_global” adds the global idle operation, i.e., the operation for a global idle layer (zero gates - a completely empty layer), to every layer that is simulated, using the global idle as a background idle that always occurs regardless of the operation. “pad_1Q” applies the 1-qubit idle gate (if one exists) to all idling qubits within a circuit layer.
basis (Basis or str, optional) – The basis to use when constructing operator representations for the elements of the created model.
verbosity (int or VerbosityPrinter, optional) – Amount of detail to print to stdout.
- Return type: