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test_spamvecs.py
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test_spamvecs.py
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import itertools
import pickle
import unittest
import numpy as np
from numpy.random import random, seed
import pygsti
from pygsti.baseobjs.basis import Basis
from pygsti.models import modelconstruction
from pygsti.modelpacks.legacy import std1Q_XYI
from pygsti.modelmembers.states import State
from pygsti.modelmembers import states
from pygsti.modelmembers import povms
from ..testutils import BaseTestCase
class SPAMVecTestCase(BaseTestCase):
def test_cptp_state(self):
vec = states.CPTPState([1 / np.sqrt(2), 0, 0, 1 / np.sqrt(2) - 0.1], "pp")
print(vec)
print(vec.base.shape)
v = vec.to_vector()
vec.from_vector(v)
print(v)
print(vec)
vec_std = pygsti.change_basis(vec, "pp", "std")
print(vec_std)
def analyze(spamvec):
stdmx = pygsti.vec_to_stdmx(spamvec, "pp")
evals = np.linalg.eigvals(stdmx)
#print(evals)
assert( np.all(evals > -1e-10) )
assert( np.linalg.norm(np.imag(evals)) < 1e-8)
return np.real(evals)
print( analyze(vec) )
states.check_deriv_wrt_params(vec)
seed(1234)
#Nice cases - when parameters are small
nRandom = 1000
for randvec in random((nRandom,4)):
r = 2*(randvec-0.5)
vec.from_vector(r)
evs = analyze(vec)
states.check_deriv_wrt_params(vec)
#print(r, "->", evs)
print("OK1")
#Mean cases - when parameters are large
nRandom = 1000
for randvec in random((nRandom,4)):
r = 10*(randvec-0.5)
vec.from_vector(r)
evs = analyze(vec)
states.check_deriv_wrt_params(vec)
#print(r, "->", evs)
print("OK2")
vec.depolarize(0.01)
vec.depolarize((0.1,0.09,0.08))
#TODO
def test_complement_spamvec(self):
model = pygsti.models.modelconstruction.create_explicit_model_from_expressions(
[('Q0',)],['Gi','Gx','Gy'],
[ "I(Q0)","X(pi/8,Q0)", "Y(pi/8,Q0)"])
E0 = model.povms['Mdefault']['0']
E1 = model.povms['Mdefault']['1']
Ec = povms.ComplementPOVMEffect(
modelconstruction.create_identity_vec(Basis.cast("pp", [4])),
[E0])
print(Ec.gpindices)
#Test TPPOVM which uses a complement evec
model.povms['Mtest'] = povms.TPPOVM([('+', E0), ('-', E1)])
E0 = model.povms['Mtest']['+']
Ec = model.povms['Mtest']['-']
v = model.to_vector()
model.from_vector(v)
#print(Ec.num_params) #not implemented for complement vecs - only for POVM
identity = np.array([[np.sqrt(2)], [0], [0], [0]],'d')
print("TEST1")
print(E0)
print(Ec)
print(E0 + Ec)
self.assertArraysAlmostEqual(E0+Ec, identity)
#TODO: add back if/when we can set parts of a POVM directly...
#print("TEST2")
#model.effects['E0'] = [1/np.sqrt(2), 0, 0.4, 0.6]
#print(model.effects['E0'])
#print(model.effects['E1'])
#print(model.effects['E0'] + model.effects['E1'])
#self.assertArraysAlmostEqual(model.effects['E0'] + model.effects['E1'], identity)
#
#print("TEST3")
#model.effects['E0'][0,0] = 1.0 #uses dirty processing
#model._update_paramvec(model.effects['E0'])
#print(model.effects['E0'])
#print(model.effects['E1'])
#print(model.effects['E0'] + model.effects['E1'])
#self.assertArraysAlmostEqual(model.effects['E0'] + model.effects['E1'], identity)
def test_povms(self):
model = pygsti.models.modelconstruction.create_explicit_model_from_expressions(
[('Q0',)],['Gi'], ["I(Q0)"])
gateset2Q = pygsti.models.modelconstruction.create_explicit_model_from_expressions(
[('Q0','Q1')],['Gi'], ["I(Q0)"])
povm = model.povms['Mdefault'].copy()
E0 = povm['0']
E1 = povm['1']
model.povms['Munconstrained'] = povm
with self.assertRaises(ValueError):
povms.convert(povm, "foobar", model.basis)
with self.assertRaises(ValueError):
povms.UnconstrainedPOVM("NotAListOrDict")
#povm['0'] = E0 # assignment -- TEMPORARIY DISABLED until we fix this ability (after POVM-using-submembers update)
tp_povm = povms.convert(povm, "full TP", model.basis)
#tp_povm['0'] = E0 # ok -- TEMPORARIY DISABLED until we fix this ability (after POVM-using-submembers update)
#with self.assertRaises(KeyError):
# tp_povm['1'] = E0 # can't assign complement vector
model.povms['Mtp'] = tp_povm # so gpindices get setup
factorPOVMs = [povm, povm.copy()]
tensor_povm = povms.TensorProductPOVM(factorPOVMs)
gateset2Q.povms['Mtensor'] = tensor_povm # so gpindices get setup
for i,p in enumerate([povm, tp_povm, tensor_povm]):
print("Testing POVM of type ", type(p))
Nels = p.num_elements
cpy = p.copy()
s = str(p)
s = pickle.dumps(p)
x = pickle.loads(s)
T = pygsti.models.gaugegroup.FullGaugeGroupElement(
np.array( [ [1,0,0,0],
[0,0,1,0],
[0,1,0,0],
[0,0,0,1]], 'd') )
v = p.to_vector()
p.from_vector(v)
v = model.to_vector() if i < 2 else gateset2Q.to_vector()
effects = p.simplify_effects(prefix="ABC")
for Evec in effects.values():
print("inds = ",Evec.gpindices, len(v))
Evec.from_vector(v[Evec.gpindices]) # gpindices should be setup relative to Model's param vec
try:
p.transform_inplace(T)
except ValueError:
pass #OK - tensorprod doesn't allow transform for instance
try:
p.depolarize(0.01)
except ValueError:
pass #OK - tensorprod doesn't allow transform for instance
def test_compbasis_povm(self):
cv = states.ComputationalBasisState([0, 1], 'pp', 'densitymx')
v = modelconstruction.create_spam_vector("1", ("Q0", "Q1"), "pp")
self.assertTrue(np.linalg.norm(cv.to_dense()-v.flat) < 1e-6)
cv = states.ComputationalBasisState([0, 0, 1], 'pp', 'densitymx')
v = modelconstruction.create_spam_vector("1", ("Q0", "Q1", "Q2"), "pp")
self.assertTrue(np.linalg.norm(cv.to_dense()-v.flat) < 1e-6)
cv = states.ComputationalBasisState([0, 0, 1], 'pp', 'densitymx')
v = modelconstruction.create_spam_vector("1", ("Q0", "Q1", "Q2"), "pp")
self.assertTrue(np.linalg.norm(cv.to_dense()-v.flat) < 1e-6)
cv = states.ComputationalBasisState([0, 0, 1], 'pp', 'densitymx')
v = modelconstruction.create_spam_vector("1", ("Q0", "Q1", "Q2"), "pp")
self.assertTrue(np.linalg.norm(cv.to_dense()-v.flat) < 1e-6)
#Only works with Python replib (only there is to_dense implemented)
#cv = pygsti.baseobjs.ComputationalSPAMVec([0,1,1],'densitymx')
#v = modelconstruction.create_spam_vector("3", pygsti.baseobjs.Basis.cast("pp",4**3))
#s = pygsti.baseobjs.FullSPAMVec(v)
#assert(np.linalg.norm(cv.to_rep("effect").todense(np.empty(cv.dim,'d'))-v.flat) < 1e-6)
#
#cv = pygsti.baseobjs.ComputationalSPAMVec([0,1,0,1],'densitymx')
#v = modelconstruction.create_spam_vector("5", pygsti.baseobjs.Basis.cast("pp",4**4))
#assert(np.linalg.norm(cv.to_rep("effect").todense(np.empty(cv.dim,'d'))-v.flat) < 1e-6)
nqubits = 3
iterover = [(0,1)]*nqubits
items = [ (''.join(map(str,outcomes)), povms.ComputationalBasisPOVMEffect(outcomes, 'pp', "densitymx"))
for outcomes in itertools.product(*iterover) ]
povm = povms.UnconstrainedPOVM(items)
self.assertEqual(povm.num_params,0)
mdl = std1Q_XYI.target_model()
mdl.preps['rho0'] = states.ComputationalBasisState([0], 'pp', 'densitymx')
mdl.povms['Mdefault'] = povms.UnconstrainedPOVM({'0': povms.ComputationalBasisPOVMEffect([0], 'pp', 'densitymx'),
'1': povms.ComputationalBasisPOVMEffect([1], 'pp', 'densitymx')})
ps0 = mdl.probabilities(())
ps1 = mdl.probabilities(('Gx',))
self.assertAlmostEqual(ps0['0'], 1.0)
self.assertAlmostEqual(ps0['1'], 0.0)
self.assertAlmostEqual(ps1['0'], 0.5)
self.assertAlmostEqual(ps1['1'], 0.5)
if __name__ == '__main__':
unittest.main(verbosity=2)