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Bugfixes and efficiency improvements for basis conversion, inner products, and computing Frobenius distance #432

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merged 10 commits into from May 7, 2024
Merged

Bugfixes and efficiency improvements for basis conversion, inner products, and computing Frobenius distance #432

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9795d59
remove frobeniusnorm and frobeniusnorm_squared functions (which were …
rileyjmurray Apr 25, 2024
57d3aef
remove .ravel() when computing Frobenius norm
rileyjmurray May 6, 2024
3f04a92
remove _hack_sqrtm and improve efficiency of fidelity calculation
rileyjmurray May 6, 2024
9714edb
check hermicity for both arguments to fidelity funciton
rileyjmurray May 6, 2024
c21a2c4
fix awkward function name
rileyjmurray May 6, 2024
7c2e160
fix namespace issue from copy-pasted code; fix nonsense tests in test…
rileyjmurray May 6, 2024
183c633
left out of last commit
rileyjmurray May 6, 2024
01c636a
address Corey`s comments
rileyjmurray May 6, 2024
ac71e54
remove commented out old code
rileyjmurray May 6, 2024
2f3ca3f
left out of last commit
rileyjmurray May 6, 2024
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47 changes: 19 additions & 28 deletions test/unit/tools/test_optools.py
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Expand Up @@ -108,14 +108,6 @@ def test_decompose_gate_matrix_invalidates_on_large_matrix(self):
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_std_process_mx(identity)
Expand Down Expand Up @@ -388,36 +380,35 @@ def setUp(self):
[ 0.71538573+0.j, 0.2680266 +0.36300238j, 0.2680266 -0.36300238j, 0.28461427+0.j]])

def test_jtrace_distance(self):
self.assertAlmostEqual(ot.jtracedist(self.A, self.A, mx_basis="std"), 0.0)
self.assertAlmostEqual(ot.jtracedist(self.A, self.B, mx_basis="std"), 0.26430148) # OLD: 0.2601 ?
val = ot.jtracedist(self.A_TP, self.A_TP, mx_basis="pp")
self.assertAlmostEqual(val, 0.0)
val = ot.jtracedist(self.A_TP, self.B_unitary, mx_basis="pp")
self.assertGreaterEqual(val, 0.5)

@needs_cvxpy
def test_diamond_distance(self):
if SKIP_DIAMONDIST_ON_WIN and sys.platform.startswith('win'): return
self.assertAlmostEqual(ot.diamonddist(self.A, self.A, mx_basis="std"), 0.0)
self.assertAlmostEqual(ot.diamonddist(self.A, self.B, mx_basis="std"), 0.614258836298)
val = ot.diamonddist(self.A_TP, self.A_TP, mx_basis="pp")
self.assertAlmostEqual(val, 0.0)
val = ot.diamonddist(self.A_TP, self.B_unitary, mx_basis="pp")
self.assertGreaterEqual(val, 0.7)

def test_entanglement_fidelity(self):
fidelity = ot.entanglement_fidelity(self.A, self.B)
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fidelity_TP_unitary= ot.entanglement_fidelity(self.A_TP, self.B_unitary, is_tp=True, is_unitary=True)
fidelity_TP_unitary_no_flag= ot.entanglement_fidelity(self.A_TP, self.B_unitary)
fidelity_TP_unitary_jam= ot.entanglement_fidelity(self.A_TP, self.B_unitary, is_tp=False, is_unitary=False)
fidelity_TP_unitary_std= ot.entanglement_fidelity(self.A_TP_std, self.B_unitary_std, mx_basis='std')

self.assertAlmostEqual(fidelity, 0.42686642003)
self.assertAlmostEqual(fidelity_TP_unitary, 0.4804724656092404)
self.assertAlmostEqual(fidelity_TP_unitary_no_flag, 0.4804724656092404)
self.assertAlmostEqual(fidelity_TP_unitary, fidelity_TP_unitary_jam)
self.assertAlmostEqual(fidelity_TP_unitary_std, 0.4804724656092404)
expect = 0.4804724656092404
self.assertAlmostEqual(fidelity_TP_unitary, expect)
self.assertAlmostEqual(fidelity_TP_unitary_no_flag, expect)
self.assertAlmostEqual(fidelity_TP_unitary_jam, expect)
self.assertAlmostEqual(fidelity_TP_unitary_std, expect)

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])
np.random.seed(0)
Q = np.linalg.qr(np.random.randn(4,4) + 1j*np.random.randn(4,4))[0]
Q[:, 0] = 0.0 # zero out the first column
bad_superoperator = ot.unitary_to_superop(Q)
upperBound, _ = ot.fidelity_upper_bound(bad_superoperator)
self.assertAlmostEqual(upperBound, 0.75)