pygsti.modelmembers.states.computationalstate
¶
The ComputationalBasisState class and supporting functionality.
Module Contents¶
Classes¶
A static state vector that is tensor product of 1-qubit Z-eigenstates. |
Attributes¶
- pygsti.modelmembers.states.computationalstate._fastcalc¶
- class pygsti.modelmembers.states.computationalstate.ComputationalBasisState(zvals, basis='pp', evotype='default', state_space=None)¶
Bases:
pygsti.modelmembers.states.state.State
A static state vector that is tensor product of 1-qubit Z-eigenstates.
This is called a “computational basis state” in many contexts.
- Parameters
zvals (iterable) – A list or other iterable of integer 0 or 1 outcomes specifying which computational basis element this object represents. The length of zvals gives the total number of qubits.
basis (Basis or {'pp','gm','std'}, optional) – The basis used to construct the Hilbert-Schmidt space representation of this state as a super-ket.
evotype (Evotype or str, optional) – The evolution type. The special value “default” is equivalent to specifying the value of pygsti.evotypes.Evotype.default_evotype.
state_space (StateSpace, optional) – The state space for this operation. If None a default state space with the appropriate number of qubits is used.
- classmethod from_state_vector(cls, vec, basis='pp', evotype='default', state_space=None)¶
Create a new ComputationalBasisState from a dense vector.
- Parameters
vec (numpy.ndarray) – A state vector specifying a computational basis state in the standard basis. This vector has length 4^n for n qubits.
basis (Basis or {'pp','gm','std'}, optional) – The basis of vec as a super-ket.
evotype (Evotype or str) – The evolution type. The special value “default” is equivalent to specifying the value of pygsti.evotypes.Evotype.default_evotype.
state_space (StateSpace, optional) – The state space for this operation. If None a default state space with the appropriate number of qubits is used.
- Returns
ComputationalBasisState
- classmethod from_pure_vector(cls, purevec, basis='pp', evotype='default', state_space=None)¶
Create a new ComputationalBasisState from a pure-state vector.
Currently, purevec must be a single computational basis state (it cannot be a superpostion of multiple of them).
- Parameters
purevec (numpy.ndarray) – A complex-valued state vector specifying a pure state in the standard computational basis. This vector has length 2^n for n qubits.
basis (Basis or {'pp','gm','std'}, optional) – The basis of vec as a super-ket.
evotype (Evotype or str, optional) – The evolution type of the resulting effect vector. The special value “default” is equivalent to specifying the value of pygsti.evotypes.Evotype.default_evotype.
state_space (StateSpace, optional) – The state space for this operation. If None a default state space with the appropriate number of qubits is used.
- Returns
ComputationalBasisState
- to_dense(self, on_space='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
- taylor_order_terms(self, 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
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
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 num_params(self)¶
Get the number of independent parameters which specify this state vector.
- Returns
int – the number of independent parameters.
- to_vector(self)¶
Get the state vector parameters as an array of values.
- Returns
numpy array – The parameters as a 1D array with length num_params().
- from_vector(self, 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
- 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).