:py:mod:`pygsti.modelmembers.states.composedstate` ================================================== .. py:module:: pygsti.modelmembers.states.composedstate .. autoapi-nested-parse:: The ComposedState class and supporting functionality. Module Contents --------------- Classes ~~~~~~~ .. autoapisummary:: pygsti.modelmembers.states.composedstate.ComposedState .. py:class:: ComposedState(static_state, errormap) Bases: :py:obj:`pygsti.modelmembers.states.state.State` TODO: update docstring A Lindblad-parameterized State (that is also expandable into terms). Parameters ---------- pure_vec : numpy array or State An array or State in the *full* density-matrix space (this vector will have dimension 4 in the case of a single qubit) which represents a pure-state preparation or projection. This is used as the "base" preparation or projection that is followed or preceded by, respectively, the parameterized Lindblad-form error generator. (This argument is *not* copied if it is a State. A numpy array is converted to a new StaticState.) errormap : MapOperator The error generator action and parameterization, encapsulated in a gate object. Usually a :class:`LindbladOp` or :class:`ComposedOp` object. (This argument is *not* copied, to allow ComposedStates to share error generator parameters with other gates and spam vectors.) Initialize a new state Vector .. py:property:: total_term_magnitude Get the total (sum) of the magnitudes of all this state vector'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 state vector in a Taylor series. Returns ------- float .. py:property:: total_term_magnitude_deriv The derivative of the sum of *all* this state vector's terms. Get the derivative of the total (sum) of the magnitudes of all this state vector's terms with respect to the operators (local) parameters. Returns ------- numpy array An array of length self.num_params .. py:property:: parameter_labels An array of labels (usually strings) describing this model member's parameters. .. py:property:: num_params Get the number of independent parameters which specify this state vector. Returns ------- int the number of independent parameters. .. py:attribute:: state_vec .. py:attribute:: error_map .. py:attribute:: terms .. py:attribute:: local_term_poly_coeffs .. py:method:: submembers() Get the ModelMember-derived objects contained in this one. Returns ------- list .. py:method:: set_gpindices(gpindices, parent, memo=None) Set the parent and indices into the parent's parameter vector that are used by this ModelMember object. Parameters ---------- gpindices : slice or integer ndarray The indices of this objects parameters in its parent's array. parent : Model or ModelMember The parent whose parameter array gpindices references. memo : dict, optional A memo dict used to avoid circular references. Returns ------- None .. py:method:: to_dense(on_space: pygsti.SpaceT = 'minimal', scratch=None) Return this state vector as a (dense) numpy array. The memory in `scratch` maybe used when it is not-None. 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. scratch : numpy.ndarray, optional scratch space available for use. Returns ------- numpy.ndarray .. py:method:: taylor_order_terms(order, max_polynomial_vars=100, return_coeff_polys=False) Get the `order`-th order Taylor-expansion terms of this state vector. This function either constructs or returns a cached list of the terms at the given order. Each term is "rank-1", meaning that it is a state preparation followed by or POVM effect preceded by actions on a density matrix `rho` of the form: `rho -> A rho B` The coefficients of these terms are typically polynomials of the State's parameters, where the polynomial's variable indices index the *global* parameters of the State's parent (usually a :class:`Model`) , not the State's local parameter array (i.e. that returned from `to_vector`). Parameters ---------- order : int The order of terms to get. 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 :class:`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 :meth:`Polynomial.compact`. .. py:method:: taylor_order_terms_above_mag(order, max_polynomial_vars, min_term_mag) Get the `order`-th order Taylor-expansion terms of this state vector that have magnitude above `min_term_mag`. This function constructs the terms at the given order which have a magnitude (given by the absolute value of their coefficient) that is greater than or equal to `min_term_mag`. It calls :meth:`taylor_order_terms` internally, so that all the terms at order `order` are typically cached for future calls. Parameters ---------- order : int The order of terms to get. max_polynomial_vars : int, optional maximum number of variables the created polynomials can have. min_term_mag : float the minimum term magnitude. Returns ------- list .. py:method:: deriv_wrt_params(wrt_filter=None) The element-wise derivative this state vector. Construct a matrix whose columns are the derivatives of the state vector with respect to a single param. Thus, each column is of length dimension and there is one column per state vector parameter. Parameters ---------- wrt_filter : list or numpy.ndarray List of parameter indices to take derivative with respect to. (None means to use all the this operation's parameters.) Returns ------- numpy array Array of derivatives, shape == (dimension, num_params) .. py:method:: hessian_wrt_params(wrt_filter1=None, wrt_filter2=None) Construct the Hessian of this state vector with respect to its parameters. This function returns a tensor whose first axis corresponds to the flattened operation matrix and whose 2nd and 3rd axes correspond to the parameters that are differentiated with respect to. Parameters ---------- wrt_filter1 : list or numpy.ndarray List of parameter indices to take 1st derivatives with respect to. (None means to use all the this operation's parameters.) wrt_filter2 : list or numpy.ndarray List of parameter indices to take 2nd derivatives with respect to. (None means to use all the this operation's parameters.) Returns ------- numpy array Hessian with shape (dimension, num_params1, num_params2) .. py:method:: to_vector() Extract a vector of the underlying gate parameters from this gate. Returns ------- numpy array a 1D numpy array with length == num_params(). .. py:method:: from_vector(v, close=False, dirty_value=True) Initialize the state vector using a 1D array of parameters. Parameters ---------- v : numpy array The 1D vector of state vector parameters. Length must == num_params() close : bool, optional Whether `v` is close to this state vector'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 .. py:method:: transform_inplace(s) Update state (column) vector V as inv(s) * V or s^T * V for preparation or effect state vectors, respectively. Note that this is equivalent to state preparation vectors getting mapped: `rho -> inv(s) * rho` and the *transpose* of effect vectors being mapped as `E^T -> E^T * s`. Generally, the transform function updates the *parameters* of the state vector such that the resulting vector is altered as described above. If such an update cannot be done (because the gate parameters do not allow for it), ValueError is raised. Parameters ---------- s : GaugeGroupElement A gauge group element which specifies the "s" matrix (and it's inverse) used in the above similarity transform. Returns ------- None .. py:method:: depolarize(amount) Depolarize this state vector by the given `amount`. Generally, the depolarize function updates the *parameters* of the State such that the resulting vector is depolarized. If such an update cannot be done (because the gate parameters do not allow for it), ValueError is raised. Parameters ---------- amount : float or tuple The amount to depolarize by. If a tuple, it must have length equal to one less than the dimension of the spam vector. All but the first element of the spam vector (often corresponding to the identity element) are multiplied by `amount` (if a float) or the corresponding `amount[i]` (if a tuple). Returns ------- None .. py:method:: errorgen_coefficient_labels(label_type='global') The elementary error-generator labels corresponding to the elements of :meth:`errorgen_coefficients_array`. Parameters ---------- label_type : str, optional (default 'global') String specifying which type of `ElementaryErrorgenLabel` to use as the keys for the returned dictionary. Allowed options are 'global' for `GlobalElementaryErrorgenLabel` and 'local' for `LocalElementaryErrorgenLabel`. Returns ------- tuple A tuple of (, [,)` tuples, where `termType` is `"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 have 1 basis label to indicate a *diagonal* term and otherwise have 2 basis labels to specify off-diagonal non-Hamiltonian Lindblad terms. Basis labels are integers starting at 0. Values are complex coefficients. basis : Basis A Basis mapping the basis labels used in the keys of `lindblad_term_dict` to basis matrices. .. py:method:: set_errorgen_coefficients(lindblad_term_dict, action='update', logscale_nonham=False, truncate=True) Sets the coefficients of terms in the error generator of this state. The dictionary `lindblad_term_dict` has tuple-keys describing the type of term and the basis elements used to construct it, e.g. `('H','X')`. Parameters ---------- lindblad_term_dict : dict Keys are `(termType, basisLabel1, )` tuples, where `termType` is `"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 have 1 basis label to indicate a *diagonal* term and otherwise have 2 basis labels to specify off-diagonal non-Hamiltonian Lindblad terms. Values are the coefficients of these error generators, and should be real except for the 2-basis-label case. action : {"update","add","reset"} How the values in `lindblad_term_dict` should be combined with existing error-generator coefficients. logscale_nonham : bool, optional Whether or not the values in `lindblad_term_dict` for non-hamiltonian error generators should be interpreted as error *rates* (of an "equivalent" depolarizing channel, see :meth:`errorgen_coefficients`) instead of raw coefficients. If True, then the non-hamiltonian coefficients are set to `-log(1 - d^2*rate)/d^2`, where `rate` is the corresponding value given in `lindblad_term_dict`. This is what is performed by the function :meth:`set_error_rates`. truncate : bool, optional Whether to allow adjustment of the errogen coefficients in order to meet constraints (e.g. to preserve CPTP) when necessary. If False, then an error is thrown when the given coefficients cannot be set as specified. Returns ------- None .. py:method:: errorgen_coefficients_array_deriv_wrt_params() The jacobian of :meth:`errogen_coefficients_array` with respect to this state's parameters. Returns ------- numpy.ndarray A 2D array of shape `(num_coeffs, num_params)` where `num_coeffs` is the number of coefficients of this operation's error generator and `num_params` is this operation's number of parameters.