add pytest in unit tests for is_separable

toqito/state_props/tests/test_is_separable.py

@@ -1,6 +1,7 @@
 """Test is_separable."""
 
 import numpy as np
+import pytest
 
 from toqito.channels import partial_trace
 from toqito.matrix_props import is_density
@@ -9,34 +10,63 @@
 from toqito.states import basis, bell, tile
 
 
-def test_non_positive_semidefinite_matrix():
-    """Ensure separability of non-positive semidefinite matrix is invalid."""
-    with np.testing.assert_raises(ValueError):
-        state = np.array([[-1, -1], [-1, -1]])
-        is_separable(state)
-
-
-def test_psd_matrix_local_dim_one():
-    """Every positive semidefinite matrix is separable when one of the local dimensions is 1."""
-    np.testing.assert_equal(is_separable(np.identity(2)), True)
-
-
-
-def test_entangled_ppt_criterion():
-    """Determined to be entangled via the PPT criterion."""
-    rho = bell(0) * bell(0).conj().T
-    np.testing.assert_equal(is_separable(rho), False)
-
-
-def test_ppt_small_dimensions():
-    """Determined to be separable via sufficiency of the PPT criterion in small dimensions."""
-    e_0, e_1, e_2 = basis(3, 0), basis(3, 1), basis(3, 2)
-    psi = 1 / np.sqrt(3) * e_0 + 1 / np.sqrt(3) * e_1 + 1 / np.sqrt(3) * e_2
-
-    e_0, e_1 = basis(2, 0), basis(2, 1)
-    phi = np.kron((1 / np.sqrt(2) * e_0 + 1 / np.sqrt(2) * e_1), psi)
-    sigma = phi * phi.conj().T
-    np.testing.assert_equal(is_separable(sigma), True)
+@pytest.mark.parametrize(
+    "test_input",
+    [
+        # Ensure separability of non-positive semidefinite matrix is invalid
+        (np.array([[-1, -1], [-1, -1]])),
+    ],
+)
+def test_werner_state_invalid(test_input):
+    """Test function works as expected for an invalid input."""
+    with pytest.raises(ValueError):
+        is_separable(test_input)
+
+
+p_var, a_var, b_var = 0.4, 0.8, 0.64
+rho_chen = np.array(
+    [
+        [p_var * a_var**2, 0, 0, p_var * a_var * b_var],
+        [0, (1 - p_var) * a_var**2, (1 - p_var) * a_var * b_var, 0],
+        [0, (1 - p_var) * a_var * b_var, (1 - p_var) * a_var**2, 0],
+        [p_var * a_var * b_var, 0, 0, p_var * a_var**2],
+    ]
+)
+
+
+@pytest.mark.parametrize(
+    "test_input",
+    [
+        # Determined to be entangled via the PPT criterion.
+        (bell(0) * bell(0).conj().T),
+        # Determined to be entangled by using Theorem 1 and Remark 1 of :cite:`Chen_2003_Matrix`.
+        (rho_chen),
+    ],
+)
+def test_not_is_separable(test_input):
+    """Check an expected non seperable test input is identified correctly."""
+    assert not is_separable(test_input)
+
+
+e_0, e_1, e_2 = basis(3, 0), basis(3, 1), basis(3, 2)
+psi = 1 / np.sqrt(3) * e_0 + 1 / np.sqrt(3) * e_1 + 1 / np.sqrt(3) * e_2
+
+e_0, e_1 = basis(2, 0), basis(2, 1)
+phi = np.kron((1 / np.sqrt(2) * e_0 + 1 / np.sqrt(2) * e_1), psi)
+
+
+@pytest.mark.parametrize(
+    "test_input",
+    [
+        # Every positive semidefinite matrix is separable when one of the local dimensions is 1.
+        (np.identity(2)),
+        # Determined to be separable via sufficiency of the PPT criterion in small dimensions.
+        (phi * phi.conj().T),
+    ],
+)
+def test_is_separable(test_input):
+    """Check an expected seperable test input is identified correctly."""
+    assert is_separable(test_input)
 
 
 def test_ppt_low_rank():
@@ -49,12 +79,9 @@ def test_ppt_low_rank():
     rho_cut = rho_cut / np.trace(rho_cut)
     pt_state_alice = partial_trace(rho_cut, [1], [3, 2])
 
-    np.testing.assert_equal(is_density(rho_cut), True)
-    np.testing.assert_equal(is_density(np.array(pt_state_alice)), True)
-    np.testing.assert_equal(
-        np.linalg.matrix_rank(rho_cut) + np.linalg.matrix_rank(pt_state_alice) <= 2 * m * n - m - n + 2,
-        True,
-    )
+    assert is_density(rho_cut)
+    assert is_density(np.array(pt_state_alice))
+    assert np.linalg.matrix_rank(rho_cut) + np.linalg.matrix_rank(pt_state_alice) - 2 * m * n - m - n + 2 <= 1e-1
     # TODO
     # np.testing.assert_equal(is_separable(rho), True)
 
@@ -67,19 +94,5 @@ def test_entangled_realignment_criterion():
     for i in range(5):
         rho = rho - tile(i) * tile(i).conj().T
     rho = rho / 4
-    np.testing.assert_equal(is_density(rho), True)
-    np.testing.assert_equal(is_separable(rho), False)
-
-
-def test_entangled_cross_norm_realignment_criterion():
-    """Determined to be entangled by using Theorem 1 and Remark 1 of :cite:`Chen_2003_Matrix`."""
-    p_var, a_var, b_var = 0.4, 0.8, 0.64
-    rho = np.array(
-        [
-            [p_var * a_var**2, 0, 0, p_var * a_var * b_var],
-            [0, (1 - p_var) * a_var**2, (1 - p_var) * a_var * b_var, 0],
-            [0, (1 - p_var) * a_var * b_var, (1 - p_var) * a_var**2, 0],
-            [p_var * a_var * b_var, 0, 0, p_var * a_var**2],
-        ]
-    )
-    np.testing.assert_equal(is_separable(rho), False)
+    assert is_density(rho)
+    assert not is_separable(rho)