Fix tests to accomodate scipy 1.16.0 changes

Author: woodsp-ibm

test/eigensolvers/test_vqd.py

@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2022, 2024.
+# (C) Copyright IBM 2022, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -16,7 +16,9 @@
 from test import QiskitAlgorithmsTestCase
 
 import numpy as np
+import scipy
 from ddt import data, ddt
+from packaging.version import Version
 
 from qiskit import QuantumCircuit
 from qiskit.circuit.library import TwoLocal, RealAmplitudes
@@ -175,7 +177,7 @@ def store_intermediate_result(eval_count, parameters, mean, metadata, step):
             history["metadata"].append(metadata)
             history["step"].append(step)
 
-        optimizer = COBYLA(maxiter=3)
+        optimizer = COBYLA(maxiter=12)
         wavefunction = self.ry_wavefunction
 
         vqd = VQD(
@@ -185,6 +187,7 @@ def store_intermediate_result(eval_count, parameters, mean, metadata, step):
             optimizer=optimizer,
             callback=store_intermediate_result,
             betas=self.betas,
+            initial_point=[1] * 8,
         )
 
         vqd.compute_eigenvalues(operator=op)
@@ -196,11 +199,98 @@ def store_intermediate_result(eval_count, parameters, mean, metadata, step):
         for params in history["parameters"]:
             self.assertTrue(all(isinstance(param, float) for param in params))
 
-        ref_eval_count = [1, 2, 3, 1, 2, 3]
-        ref_mean = [-1.07, -1.45, -1.36, 1.24, 1.55, 1.07]
-        # new ref_mean since the betas were changed
+        ref_eval_count = [
+            1,
+            2,
+            3,
+            4,
+            5,
+            6,
+            7,
+            8,
+            9,
+            10,
+            11,
+            12,
+            1,
+            2,
+            3,
+            4,
+            5,
+            6,
+            7,
+            8,
+            9,
+            10,
+            11,
+            12,
+        ]
 
-        ref_step = [1, 1, 1, 2, 2, 2]
+        ref_mean_pre_1_16 = [
+            -1.08,
+            -1.08,
+            -1.0,
+            -1.14,
+            -1.17,
+            -1.38,
+            -1.0,
+            -1.63,
+            -1.45,
+            -1.55,
+            -1.63,
+            -1.75,
+            -1.04,
+            -1.07,
+            -0.72,
+            -0.46,
+            -0.71,
+            -0.56,
+            -0.92,
+            -0.29,
+            -0.89,
+            -0.38,
+            -1.06,
+            -1.05,
+        ]
+        ref_mean_1_16 = [
+            -1.08,
+            -1.08,
+            -1.0,
+            -1.14,
+            -1.17,
+            -1.38,
+            -1.0,
+            -1.63,
+            -1.45,
+            -1.55,
+            -1.63,
+            -1.75,
+            -1.04,
+            -1.07,
+            -0.72,
+            -0.46,
+            -0.71,
+            -0.56,
+            -0.92,
+            -0.29,
+            -0.89,
+            -0.38,
+            -0.97,
+            -1.16,
+        ]
+        # Unlike in other places where COYBLA is used in tests and differences arose between
+        # pre 1.16.0 versions and after, where in 1.16.0 scipy changed the COBYLA
+        # implementation, I was not able to find changes that would reproduce the outcome so
+        # tests passed no matter whether 1.16 or before was installed. Here the mean outcomes
+        # match all but the last 2 values so I thought about comparing a subset but in the
+        # end decided to go with different reference values based on scipy version
+        ref_mean = (
+            ref_mean_pre_1_16
+            if Version(scipy.version.version) < Version("1.16.0")
+            else ref_mean_1_16
+        )
+
+        ref_step = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]
 
         np.testing.assert_array_almost_equal(history["eval_count"], ref_eval_count, decimal=0)
         np.testing.assert_array_almost_equal(history["mean"], ref_mean, decimal=2)
@@ -410,7 +500,7 @@ def test_aux_operator_std_dev(self, op):
                 0.2442925,
                 -1.51638917,
             ],
-            optimizer=COBYLA(maxiter=0),
+            optimizer=COBYLA(maxiter=10),
             betas=self.betas,
         )
 
@@ -423,7 +513,7 @@ def test_aux_operator_std_dev(self, op):
         # expectation values
         self.assertAlmostEqual(result.aux_operators_evaluated[0][0][0], 2.0, places=1)
         self.assertAlmostEqual(
-            result.aux_operators_evaluated[0][1][0], 0.0019531249999999445, places=1
+            result.aux_operators_evaluated[0][1][0], 0.7432341813894455, places=1
         )
         # metadata
         self.assertIsInstance(result.aux_operators_evaluated[0][0][1], dict)
@@ -435,9 +525,7 @@ def test_aux_operator_std_dev(self, op):
         self.assertEqual(len(result.aux_operators_evaluated[0]), 4)
         # expectation values
         self.assertAlmostEqual(result.aux_operators_evaluated[0][0][0], 2.0, places=1)
-        self.assertAlmostEqual(
-            result.aux_operators_evaluated[0][1][0], 0.0019531249999999445, places=1
-        )
+        self.assertAlmostEqual(result.aux_operators_evaluated[0][1][0], 0.743234181389445, places=1)
         self.assertEqual(result.aux_operators_evaluated[0][2][0], 0.0)
         self.assertEqual(result.aux_operators_evaluated[0][3][0], 0.0)
         # metadata

test/minimum_eigensolvers/test_qaoa.py

@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2022, 2023.
+# (C) Copyright IBM 2022, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -180,7 +180,7 @@ def test_change_operator_size(self):
         with self.subTest(msg="QAOA 6x6"):
             self.assertIn(graph_solution, {"010101", "101010"})
 
-    @idata([[W2, S2, None], [W2, S2, [0.0, 0.0]], [W2, S2, [1.0, 0.8]]])
+    @idata([[W2, S2, None], [W2, S2, [0.0, 0.5]], [W2, S2, [1.0, 0.8]]])
     @unpack
     def test_qaoa_initial_point(self, w, solutions, init_pt):
         """Check first parameter value used is initial point as expected"""

test/minimum_eigensolvers/test_sampling_vqe.py

@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2018, 2024.
+# (C) Copyright IBM 2018, 2025.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -185,10 +185,10 @@ def test_optimizer_scipy_callable(self):
         vqe = SamplingVQE(
             Sampler(),
             RealAmplitudes(),
-            partial(scipy_minimize, method="COBYLA", options={"maxiter": 2}),
+            partial(scipy_minimize, method="COBYLA", options={"maxiter": 8}),
         )
         result = vqe.compute_minimum_eigenvalue(Pauli("Z"))
-        self.assertEqual(result.cost_function_evals, 2)
+        self.assertEqual(result.cost_function_evals, 8)
 
     def test_optimizer_callable(self):
         """Test passing a optimizer directly as callable."""

test/optimizers/test_optimizers.py

@@ -75,7 +75,7 @@ def run_optimizer(
             grad: Whether to pass the gradient function as input.
             bounds: Optimizer bounds.
         """
-        x_0 = np.asarray([1.3, 0.7, 0.8, 1.9, 1.2])
+        x_0 = np.asarray([1.13, 0.7, 0.8, 1.9, 1.2])
         jac = rosen_der if grad else None
 
         res = optimizer.minimize(rosen, x_0, jac, bounds)