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tpstate.py
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tpstate.py
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"""
The TPState class and supporting functionality.
"""
#***************************************************************************************************
# Copyright 2015, 2019 National Technology & Engineering Solutions of Sandia, LLC (NTESS).
# Under the terms of Contract DE-NA0003525 with NTESS, the U.S. Government retains certain rights
# in this software.
# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
# in compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0 or in the LICENSE file in the root pyGSTi directory.
#***************************************************************************************************
import numpy as _np
from pygsti.baseobjs import Basis as _Basis
from pygsti.baseobjs import statespace as _statespace
from pygsti.modelmembers.states.densestate import DenseState as _DenseState
from pygsti.modelmembers.states.state import State as _State
from pygsti.baseobjs.protectedarray import ProtectedArray as _ProtectedArray
class TPState(_DenseState):
"""
A fixed-unit-trace state vector.
This state vector is fully parameterized except for the first element, which
is frozen to be 1/(d**0.25). This is so that, when the state vector is
interpreted in the Pauli or Gell-Mann basis, the represented density matrix
has trace == 1. This restriction is frequently used in conjuction with
trace-preserving (TP) gates, hence its name.
Parameters
----------
vec : array_like or State
a 1D numpy array representing the state. The
shape of this array sets the dimension of the state.
basis : Basis or str
The basis that `vec` is in.
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.
"""
#Note: here we assume that the first basis element is (1/sqrt(x) * I),
# where I the d-dimensional identity (where len(vector) == d**2). So
# if Tr(basisEl*basisEl) == Tr(1/x*I) == d/x must == 1, then we must
# have x == d. Thus, we multiply this first basis element by
# alpha = 1/sqrt(d) to obtain a trace-1 matrix, i.e., finding alpha
# s.t. Tr(alpha*[1/sqrt(d)*I]) == 1 => alpha*d/sqrt(d) == 1 =>
# alpha = 1/sqrt(d) = 1/(len(vec)**0.25).
def __init__(self, vec, basis=None, evotype="default", state_space=None):
vector = _State._to_vector(vec)
if basis is not None:
if not isinstance(basis, _Basis):
basis = _Basis.cast(basis, len(vector)) # don't perform this cast if we're given a basis
firstEl = basis.elsize**-0.25 # not dim, as the dimension of the vector space may be less
if not _np.isclose(vector[0], firstEl):
raise ValueError("Cannot create TPState: first element must equal %g!" % firstEl)
# if basis is None, don't check first element (hackfor de-serialization, so we don't need to store basis)
_DenseState.__init__(self, vector, basis, evotype, state_space)
assert(isinstance(self.columnvec, _ProtectedArray))
self._paramlbls = _np.array(["VecElement %d" % i for i in range(1, self.dim)], dtype=object)
@property
def columnvec(self):
"""
Direct access the the underlying data as column vector, i.e, a (dim,1)-shaped array.
"""
bv = self._ptr.view()
bv.shape = (bv.size, 1)
return _ProtectedArray(bv, indices_to_protect=(0, 0))
def set_dense(self, vec):
"""
Set the dense-vector value of this state vector.
Attempts to modify this state vector's parameters so that the raw
state vector becomes `vec`. Will raise ValueError if this operation
is not possible.
Parameters
----------
vec : array_like or State
A numpy array representing a state vector, or a State object.
Returns
-------
None
"""
vec = _State._to_vector(vec)
firstEl = (self.dim)**-0.25
if(vec.size != self.dim):
raise ValueError("Argument must be length %d" % self.dim)
if not _np.isclose(vec[0], firstEl):
raise ValueError("Cannot create TPState: "
"first element must equal %g!" % firstEl)
self._ptr[1:] = vec[1:]
self._ptr_has_changed()
self.dirty = True
@property
def num_params(self):
"""
Get the number of independent parameters which specify this state vector.
Returns
-------
int
the number of independent parameters.
"""
return self.dim - 1
def 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().
"""
return self._ptr[1:] # .real in case of complex matrices?
def 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
"""
#assert(_np.isclose(self._ptr[0], (self.dim)**-0.25)) # takes too much time!
self._ptr[1:] = v
self._ptr_has_changed()
self.dirty = dirty_value
def deriv_wrt_params(self, 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)
"""
derivMx = _np.identity(self.dim, 'd') # TP vecs assumed real
derivMx = derivMx[:, 1:] # remove first col ( <=> first-el parameters )
if wrt_filter is None:
return derivMx
else:
return _np.take(derivMx, wrt_filter, axis=1)
def has_nonzero_hessian(self):
"""
Whether this state vector has a non-zero Hessian with respect to its parameters.
Returns
-------
bool
"""
return False
@classmethod
def _from_memoized_dict(cls, mm_dict, serial_memo):
vec = cls._decodemx(mm_dict['dense_superket_vector'])
state_space = _statespace.StateSpace.from_nice_serialization(mm_dict['state_space'])
return cls(vec, None, mm_dict['evotype'], state_space) # use basis=None to skip 1st element check