pygsti.modelmembers.operations.stochasticop
The StochasticNoiseOp class and supporting functionality.
Module Contents
Classes
A stochastic noise operation. |
- class pygsti.modelmembers.operations.stochasticop.StochasticNoiseOp(state_space, stochastic_basis='PP', basis='pp', evotype='default', initial_rates=None, seed_or_state=None)
Bases:
pygsti.modelmembers.operations.linearop.LinearOperator,pygsti.modelmembers.operations.krausop.KrausOperatorInterfaceA stochastic noise operation.
Implements the stochastic noise map: rho -> (1-sum(p_i))rho + sum_(i>0) p_i * B_i * rho * B_i^dagger where p_i > 0 and sum(p_i) < 1, and B_i is basis where B_0 is the identity.
In the case of the ‘chp’ evotype, the B_i element is returned with probability p_i, such that the outcome distribution matches the aforementioned stochastic noise map when considered over many samples.
Parameters
- state_spaceStateSpace, optional
The state space for this operation.
- basisBasis or {‘pp’,’gm’,’qt’}, optional
The basis to use, defining the “principle axes” along which there is stochastic noise. We assume that the first element of basis is the identity.
- evotypeEvotype or str, optional
The evolution type. The special value “default” is equivalent to specifying the value of pygsti.evotypes.Evotype.default_evotype.
- initial_rateslist or array
if not None, a list of basis.size-1 initial error rates along each of the directions corresponding to each basis element. If None, then all initial rates are zero.
- seed_or_statefloat or RandomState, optional
Random seed for RandomState (or directly provided RandomState) for sampling stochastic superoperators with the ‘chp’ evotype.
Initialize a new LinearOperator
- property num_params
Get the number of independent parameters which specify this operation.
Returns
- int
the number of independent parameters.
- property total_term_magnitude
Get the total (sum) of the magnitudes of all this operator’s terms.
The magnitude of a term is the absolute value of its coefficient, so this function returns the number you’d get from summing up the absolute-coefficients of all the Taylor terms (at all orders!) you get from expanding this operator in a Taylor series.
Returns
float
- property total_term_magnitude_deriv
The derivative of the sum of all this operator’s terms.
Computes the derivative of the total (sum) of the magnitudes of all this operator’s terms with respect to the operators (local) parameters.
Returns
- numpy array
An array of length self.num_params
- property kraus_operators
A list of this operation’s Kraus operators as numpy arrays.
- to_dense(on_space='minimal')
Return this operation as a dense matrix.
Parameters
- on_space{‘minimal’, ‘Hilbert’, ‘HilbertSchmidt’}
The space that the returned dense operation acts upon. For unitary matrices and bra/ket vectors, use ‘Hilbert’. For superoperator matrices and super-bra/super-ket vectors use ‘HilbertSchmidt’. ‘minimal’ means that ‘Hilbert’ is used if possible given this operator’s evolution type, and otherwise ‘HilbertSchmidt’ is used.
Returns
numpy.ndarray
- to_vector()
Extract a vector of the underlying operation parameters from this operation.
Returns
- numpy array
a 1D numpy array with length == num_params().
- from_vector(v, close=False, dirty_value=True)
Initialize the operation using a vector of parameters.
Parameters
- vnumpy array
The 1D vector of operation parameters. Length must == num_params()
- closebool, optional
Whether v is close to this operation’s current set of parameters. Under some circumstances, when this is true this call can be completed more quickly.
- dirty_valuebool, optional
The value to set this object’s “dirty flag” to before exiting this call. This is passed as an argument so it can be updated recursively. Leave this set to True unless you know what you’re doing.
Returns
None
- taylor_order_terms(order, max_polynomial_vars=100, return_coeff_polys=False)
Get the order-th order Taylor-expansion terms of this operation.
This function either constructs or returns a cached list of the terms at the given order. Each term is “rank-1”, meaning that its action on a density matrix rho can be written:
rho -> A rho B
The coefficients of these terms are typically polynomials of the operation’s parameters, where the polynomial’s variable indices index the global parameters of the operation’s parent (usually a
Model), not the operation’s local parameter array (i.e. that returned from to_vector).Parameters
- orderint
Which order terms (in a Taylor expansion of this
LindbladOp) to retrieve.- max_polynomial_varsint, optional
maximum number of variables the created polynomials can have.
- return_coeff_polysbool
Whether a parallel list of locally-indexed (using variable indices corresponding to this object’s parameters rather than its parent’s) polynomial coefficients should be returned as well.
Returns
- termslist
A list of
RankOneTermobjects.- coefficientslist
Only present when return_coeff_polys == True. A list of compact polynomial objects, meaning that each element is a (vtape,ctape) 2-tuple formed by concatenating together the output of
Polynomial.compact().
- set_kraus_operators(kraus_operators)
Set the parameters of this operation by specifying its Kraus operators.
Parameters
- kraus_operatorslist
A list of numpy arrays, each of which specifies a Kraus operator.
Returns
None
- to_memoized_dict(mmg_memo)
Create a serializable dict with references to other objects in the memo.
Parameters
- mmg_memo: dict
Memo dict from a ModelMemberGraph, i.e. keys are object ids and values are ModelMemberGraphNodes (which contain the serialize_id). This is NOT the same as other memos in ModelMember (e.g. copy, allocate_gpindices, etc.).
Returns
- mm_dict: dict
A dict representation of this ModelMember ready for serialization This must have at least the following fields: module, class, submembers, params, state_space, evotype Additional fields may be added by derived classes.