pygsti.modelmembers.operations.stochasticop

The StochasticNoiseOp class and supporting functionality.

Module Contents

Classes

StochasticNoiseOp

A stochastic noise operation.

class pygsti.modelmembers.operations.stochasticop.StochasticNoiseOp(state_space, basis='pp', evotype='default', initial_rates=None, seed_or_state=None)

Bases: pygsti.modelmembers.operations.linearop.LinearOperator

A 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_space (StateSpace, optional) – The state space for this operation.

  • basis (Basis 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.

  • evotype (Evotype or str, optional) – The evolution type. The special value “default” is equivalent to specifying the value of pygsti.evotypes.Evotype.default_evotype.

  • initial_rates (list 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_state (float or RandomState, optional) – Random seed for RandomState (or directly provided RandomState) for sampling stochastic superoperators with the ‘chp’ evotype.

_update_rep(self)
_rates_to_params(self, rates)
_params_to_rates(self, params)
_get_rate_poly_dicts(self)

Return a list of dicts, one per rate, expressing the rate as a polynomial of the local parameters (tuple keys of dicts <=> poly terms, e.g. (1,1) <=> x1^2)

to_dense(self, 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

property num_params(self)

Get the number of independent parameters which specify this operation.

Returns

int – the number of independent parameters.

to_vector(self)

Extract a vector of the underlying operation parameters from this operation.

Returns

numpy array – a 1D numpy array with length == num_params().

from_vector(self, v, close=False, dirty_value=True)

Initialize the operation using a vector of parameters.

Parameters
  • v (numpy array) – The 1D vector of operation parameters. Length must == num_params()

  • close (bool, 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_value (bool, 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(self, 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
  • order (int) – Which order terms (in a Taylor expansion of this LindbladOp) to retrieve.

  • max_polynomial_vars (int, optional) – maximum number of variables the created polynomials can have.

  • return_coeff_polys (bool) – 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

  • terms (list) – A list of RankOneTerm objects.

  • coefficients (list) – 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 :method:`Polynomial.compact`.

property total_term_magnitude(self)

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(self)

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

to_memoized_dict(self, 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.

classmethod _from_memoized_dict(cls, mm_dict, serial_memo)

For subclasses to implement. Submember-existence checks are performed, and the gpindices of the return value is set, by the non-underscored :method:`from_memoized_dict` implemented in this class.

_is_similar(self, other, rtol, atol)

Returns True if other model member (which it guaranteed to be the same type as self) has the same local structure, i.e., not considering parameter values or submembers

__str__(self)

Return str(self).

_oneline_contents(self)

Summarizes the contents of this object in a single line. Does not summarize submembers.