/
test_optools.py
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/
test_optools.py
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import functools
import numpy as np
import scipy
from unittest import mock
from ..util import BaseCase, needs_cvxpy
from pygsti.modelpacks.legacy import std2Q_XXYYII
import pygsti.tools.optools as ot
import pygsti.tools.basistools as bt
from pygsti.objects.basis import Basis
def fake_minimize(fn):
"""Mock scipy.optimize.minimize in the underlying function call to reduce optimization overhead"""
def side_effect(o, mx, **kwargs):
return mock.MagicMock(x=mx)
@functools.wraps(fn)
def wrapper(*args, **kwargs):
with mock.patch.object(scipy.optimize, 'minimize', side_effect=side_effect):
return fn(*args, **kwargs)
return wrapper
class OpToolsTester(BaseCase):
def test_unitary_to_pauligate(self):
theta = np.pi
sigmax = np.array([[0, 1], [1, 0]])
ex = 1j * theta * sigmax / 2
U = scipy.linalg.expm(ex)
# U is 2x2 unitary matrix operating on single qubit in [0,1] basis (X(pi) rotation)
op = ot.unitary_to_pauligate(U)
op_ans = np.array([[ 1., 0., 0., 0.],
[ 0., 1., 0., 0.],
[ 0., 0., -1., 0.],
[ 0., 0., 0., -1.]], 'd')
self.assertArraysAlmostEqual(op, op_ans)
U_2Q = np.identity(4, 'complex'); U_2Q[2:, 2:] = U
# U_2Q is 4x4 unitary matrix operating on isolated two-qubit space (CX(pi) rotation)
op_2Q = ot.unitary_to_pauligate(U_2Q)
# TODO assert correctness
def test_decompose_gate_matrix(self):
oneRealPair = np.array([
[1+1j, 0, 0, 0],
[ 0, 1-1j, 0, 0],
[ 0, 0, 2, 0],
[ 0, 0, 0, 2]
], 'complex')
decomp = ot.decompose_gate_matrix(oneRealPair)
# decompose gate mx whose eigenvalues have a real but non-unit pair
# TODO assert correctness
dblRealPair = np.array([
[ 3, 0, 0, 0],
[ 0, 3, 0, 0],
[ 0, 0, 2, 0],
[ 0, 0, 0, 2]
], 'complex')
decomp = ot.decompose_gate_matrix(dblRealPair)
# decompose gate mx whose eigenvalues have two real but non-unit pairs
# TODO assert correctness
def test_decompose_gate_matrix_invalidates_on_all_complex_eigval(self):
unpairedMx = np.array([
[1+1j, 0, 0, 0],
[ 0, 2-1j, 0, 0],
[ 0, 0, 2+2j, 0],
[ 0, 0, 0, 1.0+3j]
], 'complex')
decomp = ot.decompose_gate_matrix(unpairedMx)
# decompose gate mx which has all complex eigenvalue -> bail out
self.assertFalse(decomp['isValid'])
def test_decompose_gate_matrix_invalidates_on_large_matrix(self):
largeMx = np.identity(16, 'd')
decomp = ot.decompose_gate_matrix(largeMx) # can only handle 1Q mxs
self.assertFalse(decomp['isValid'])
def test_hack_sqrt_m(self):
expected = np.array([
[ 0.55368857+0.46439416j, 0.80696073-0.21242648j],
[ 1.21044109-0.31863972j, 1.76412966+0.14575444j]
])
sqrt = ot._hack_sqrtm(np.array([[1, 2], [3, 4]]))
self.assertArraysAlmostEqual(sqrt, expected)
def test_unitary_to_process_mx(self):
identity = np.identity(2)
processMx = ot.unitary_to_process_mx(identity)
self.assertArraysAlmostEqual(processMx, np.identity(4))
class ProjectModelTester(BaseCase):
def setUp(self):
self.projectionTypes = ('H', 'S', 'H+S', 'LND', 'LNDF')
self.target_model = std2Q_XXYYII.target_model()
self.model = self.target_model.depolarize(op_noise=0.01)
@fake_minimize
def test_log_diff_model_projection(self):
proj_model, Np_dict = ot.project_model(self.model, self.target_model, self.projectionTypes, 'logG-logT')
# TODO assert correctness
def test_logTiG_model_projection(self):
proj_model, Np_dict = ot.project_model(self.model, self.target_model, self.projectionTypes, 'logTiG')
# TODO assert correctness
def test_logGTi_model_projection(self):
proj_model, Np_dict = ot.project_model(self.model, self.target_model, self.projectionTypes, 'logGTi')
# TODO assert correctness
def test_raises_on_basis_mismatch(self):
with self.assertRaises(ValueError):
mdl_target_gm = std2Q_XXYYII.target_model()
mdl_target_gm.basis = Basis.cast("gm", 16)
ot.project_model(self.model, mdl_target_gm, self.projectionTypes, 'logGti') # basis mismatch
class ErrorGenTester(BaseCase):
def setUp(self):
self.target_model = std2Q_XXYYII.target_model()
self.mdl_datagen = self.target_model.depolarize(op_noise=0.1, spam_noise=0.001)
def test_std_errgens(self):
projectionTypes = ['hamiltonian', 'stochastic', 'affine']
basisNames = ['std', 'gm', 'pp'] # , 'qt'] #dim must == 3 for qt
for projectionType in projectionTypes:
ot.std_scale_factor(4, projectionType)
for basisName in basisNames:
ot.std_error_generators(4, projectionType, basisName)
def test_std_errgens_raise_on_bad_projection_type(self):
with self.assertRaises(ValueError):
ot.std_scale_factor(4, "foobar")
with self.assertRaises(ValueError):
ot.std_error_generators(4, "foobar", 'gm')
def test_lind_errgens(self):
basis = Basis.cast('gm', 4)
normalize = False
other_mode = "all"
ot.lindblad_error_generators(basis, basis, normalize, other_mode)
ot.lindblad_error_generators(None, basis, normalize, other_mode)
ot.lindblad_error_generators(basis, None, normalize, other_mode)
ot.lindblad_error_generators(None, None, normalize, other_mode)
normalize = True
other_mode = "all"
ot.lindblad_error_generators(basis, basis, normalize, other_mode)
ot.lindblad_error_generators(None, basis, normalize, other_mode)
ot.lindblad_error_generators(basis, None, normalize, other_mode)
ot.lindblad_error_generators(None, None, normalize, other_mode)
normalize = True
other_mode = "diagonal"
ot.lindblad_error_generators(basis, basis, normalize, other_mode)
ot.lindblad_error_generators(None, basis, normalize, other_mode)
ot.lindblad_error_generators(basis, None, normalize, other_mode)
ot.lindblad_error_generators(None, None, normalize, other_mode)
basis = Basis.cast('gm', 16)
mxBasis = Basis.cast('gm', 16)
errgen = np.identity(16, 'd')
ot.lindblad_errgen_projections(errgen, basis, basis, mxBasis,
normalize=True, return_generators=False,
other_mode="all", sparse=False)
ot.lindblad_errgen_projections(errgen, None, 'gm', mxBasis,
normalize=True, return_generators=False,
other_mode="all", sparse=False)
ot.lindblad_errgen_projections(errgen, 'gm', None, mxBasis,
normalize=True, return_generators=True,
other_mode="diagonal", sparse=False)
basisMxs = bt.basis_matrices('gm', 16, sparse=False)
ot.lindblad_errgen_projections(errgen, basisMxs, basisMxs, mxBasis,
normalize=True, return_generators=False,
other_mode="all", sparse=False)
ot.lindblad_errgen_projections(errgen, None, None, mxBasis,
normalize=True, return_generators=False,
other_mode="all", sparse=False)
# TODO assert correctness
@fake_minimize
def test_err_gen(self):
projectionTypes = ['hamiltonian', 'stochastic', 'affine']
basisNames = ['std', 'gm', 'pp'] # , 'qt'] #dim must == 3 for qt
for (lbl, gateTarget), gate in zip(self.target_model.operations.items(), self.mdl_datagen.operations.values()):
errgen = ot.error_generator(gate, gateTarget, self.target_model.basis, 'logG-logT')
altErrgen = ot.error_generator(gate, gateTarget, self.target_model.basis, 'logTiG')
altErrgen2 = ot.error_generator(gate, gateTarget, self.target_model.basis, 'logGTi')
with self.assertRaises(ValueError):
ot.error_generator(gate, gateTarget, self.target_model.basis, 'adsf')
for projectionType in projectionTypes:
for basisName in basisNames:
ot.std_errgen_projections(errgen, projectionType, basisName)
originalGate = ot.operation_from_error_generator(errgen, gateTarget, 'logG-logT')
altOriginalGate = ot.operation_from_error_generator(altErrgen, gateTarget, 'logTiG')
altOriginalGate2 = ot.operation_from_error_generator(altErrgen, gateTarget, 'logGTi')
with self.assertRaises(ValueError):
ot.operation_from_error_generator(errgen, gateTarget, 'adsf')
#self.assertArraysAlmostEqual(originalGate, gate) # sometimes need to approximate the log for this one
self.assertArraysAlmostEqual(altOriginalGate, gate)
self.assertArraysAlmostEqual(altOriginalGate2, gate)
@fake_minimize
def test_err_gen_nonunitary(self):
errgen_nonunitary = ot.error_generator(self.mdl_datagen.operations['Gxi'],
self.mdl_datagen.operations['Gxi'],
self.mdl_datagen.basis)
# TODO assert correctness
def test_err_gen_not_near_gate(self):
errgen_notsmall = ot.error_generator(self.mdl_datagen.operations['Gxi'], self.target_model.operations['Gix'],
self.target_model.basis, 'logTiG')
errgen_notsmall = ot.error_generator(self.mdl_datagen.operations['Gxi'], self.target_model.operations['Gix'],
self.target_model.basis, 'logGTi')
# TODO assert correctness
def test_err_gen_raises_on_bad_type(self):
with self.assertRaises(ValueError):
ot.error_generator(self.mdl_datagen.operations['Gxi'], self.target_model.operations['Gxi'],
self.target_model.basis, 'foobar')
def test_err_gen_assert_shape_raises_on_ndims_too_high(self):
# Check helper routine _assert_shape
with self.assertRaises(NotImplementedError): # boundary case
ot._assert_shape(np.zeros((2, 2, 2, 2, 2), 'd'), (2, 2, 2, 2, 2), sparse=True) # ndims must be <= 4
class GateOpsTester(BaseCase):
def setUp(self):
self.A = np.array([
[ 0.9, 0, 0.1j, 0],
[ 0, 0, 0, 0],
[-0.1j, 0, 0, 0],
[ 0, 0, 0, 0.1]
], 'complex')
self.B = np.array([
[ 0.5, 0, 0, -0.2j],
[ 0, 0.25, 0, 0],
[ 0, 0, 0.25, 0],
[0.2j, 0, 0, 0.1]
], 'complex')
def test_frobenius_distance(self):
self.assertAlmostEqual(ot.frobeniusdist(self.A, self.A), 0.0)
self.assertAlmostEqual(ot.frobeniusdist(self.A, self.B), (0.430116263352+0j))
self.assertAlmostEqual(ot.frobeniusdist2(self.A, self.A), 0.0)
self.assertAlmostEqual(ot.frobeniusdist2(self.A, self.B), (0.185+0j))
def test_jtrace_distance(self):
self.assertAlmostEqual(ot.jtracedist(self.A, self.A, mxBasis="std"), 0.0)
self.assertAlmostEqual(ot.jtracedist(self.A, self.B, mxBasis="std"), 0.26430148) # OLD: 0.2601 ?
@needs_cvxpy
def test_diamond_distance(self):
self.assertAlmostEqual(ot.diamonddist(self.A, self.A, mxBasis="std"), 0.0)
self.assertAlmostEqual(ot.diamonddist(self.A, self.B, mxBasis="std"), 0.614258836298)
def test_frobenius_norm_equiv(self):
from pygsti.tools import matrixtools as mt
self.assertAlmostEqual(ot.frobeniusdist(self.A, self.B), mt.frobeniusnorm(self.A - self.B))
self.assertAlmostEqual(ot.frobeniusdist(self.A, self.B), np.sqrt(mt.frobeniusnorm2(self.A - self.B)))
def test_entanglement_fidelity(self):
fidelity = ot.entanglement_fidelity(self.A, self.B)
self.assertAlmostEqual(fidelity, 0.42686642003)
def test_fidelity_upper_bound(self):
upperBound = ot.fidelity_upper_bound(self.A)
expected = (
np.array([[ 0.25]]),
np.array([[ 1.00000000e+00, -8.27013523e-16, 8.57305616e-33, 1.95140273e-15],
[ -8.27013523e-16, 1.00000000e+00, 6.28036983e-16, -8.74760501e-31],
[ 5.68444574e-33, -6.28036983e-16, 1.00000000e+00, -2.84689309e-16],
[ 1.95140273e-15, -9.27538795e-31, 2.84689309e-16, 1.00000000e+00]])
)
self.assertArraysAlmostEqual(upperBound[0], expected[0])
self.assertArraysAlmostEqual(upperBound[1], expected[1])