Hotfix qiskit

docs/source/conf.py

@@ -128,4 +128,4 @@
 #     app.connect(skip)
 
 # pygments_style = 'sphinx'
-# suppress_warnings = ["myst_header"]
+# suppress_warnings = ["myst_header"]

examples/03_qaoa_on_qpus.ipynb

@@ -250,7 +250,7 @@
    "outputs": [],
    "source": [
     "# device. If qpu_crendetials is not specified,the default hub, group and provider is used.\n",
-    "qiskit_cloud = create_device(location='ibmq', name='ibm_perth', **qpu_credentials, as_emulator=True)\n",
+    "qiskit_cloud = create_device(location='ibmq', name='ibm_kyoto', **qpu_credentials, as_emulator=True)\n",
     "q_qiskit.set_device(qiskit_cloud)\n",
     "\n",
     "# circuit properties\n",

examples/14_qaoa_benchmark.ipynb

@@ -69,7 +69,7 @@
     "q = QAOA()\n",
     "\n",
     "# set device and backend properties\n",
-    "qiskit_cloud = create_device(location='ibmq', name='ibm_perth', **qpu_credentials, as_emulator=True)\n",
+    "qiskit_cloud = create_device(location='ibmq', name='ibm_kyoto', **qpu_credentials, as_emulator=True)\n",
     "q.set_device(qiskit_cloud)\n",
     "q.set_backend_properties(n_shots=1000)\n",
     "\n",
@@ -602,7 +602,7 @@
     "q_ibm = QAOA()\n",
     "\n",
     "# set device and backend properties\n",
-    "qiskit_cloud = create_device(location='ibmq', name='ibm_perth', **qpu_credentials, as_emulator=True)\n",
+    "qiskit_cloud = create_device(location='ibmq', name='ibm_kyoto', **qpu_credentials, as_emulator=True)\n",
     "q_ibm.set_device(qiskit_cloud)\n",
     "q_ibm.set_backend_properties(n_shots=1000)\n",
     "\n",

src/openqaoa-core/tests/test_bpsp.py

@@ -19,11 +19,12 @@ def terms_list_equality(terms_list1, terms_list2):
 
     return bool
 
+
 def fisher_yates_shuffle(arr, rng):
     """
     Perform an in-place shuffle of a list using the Fisher-Yates shuffle algorithm.
 
-    This algorithm runs in O(n) time and ensures that every permutation of the array is equally likely. 
+    This algorithm runs in O(n) time and ensures that every permutation of the array is equally likely.
     The shuffle is performed in-place, meaning that the input array `arr` is modified directly.
 
     Parameters:
@@ -59,6 +60,7 @@ def fisher_yates_shuffle(arr, rng):
     # Return the shuffled array
     return arr
 
+
 class TestBPSP(unittest.TestCase):
     """
     Test suite for the BPSP problem.
@@ -71,12 +73,14 @@ def test_bpsp_qubo(self):
         """
         Test the correct QUBO formation from a provided graph.
 
-        This method validates that BPSP creates the expected QUBO by comparing 
+        This method validates that BPSP creates the expected QUBO by comparing
         its terms and weights with predefined expected values.
         """
         car_sequence = [3, 1, 0, 0, 2, 2, 4, 4, 3, 1]
         G = nx.Graph()
-        G.add_weighted_edges_from([[3, 1, -2], [3, 4, -1], [1, 0, -1], [0, 2, 1], [2, 4, 1]])
+        G.add_weighted_edges_from(
+            [[3, 1, -2], [3, 4, -1], [1, 0, -1], [0, 2, 1], [2, 4, 1]]
+        )
 
         gr_edges = [[3, 1], [3, 4], [1, 0], [0, 2], [2, 4]]
         gr_weights = [-2, -1, -1, 1, 1]
@@ -103,12 +107,12 @@ def test_bpsp_random_instance(self):
         """
         Test the random instance generation of the Binary Paint Shop Problem (BPSP).
 
-        This test verifies whether the `random_instance` method of the BPSP class 
+        This test verifies whether the `random_instance` method of the BPSP class
         correctly creates a randomized car sequence based on a given number of cars and seed.
         The car sequence should be a list containing two occurrences of each car ID,
         randomly shuffled using the Fisher-Yates shuffle algorithm.
 
-        The function asserts that the sequence generated by the BPSP class's method 
+        The function asserts that the sequence generated by the BPSP class's method
         is identical to the sequence produced by an explicitly defined Fisher-Yates shuffle process,
         ensuring both consistency and correctness in the shuffling method based on a fixed seed.
         """
@@ -120,16 +124,19 @@ def test_bpsp_random_instance(self):
         # Lambda function to create and shuffle car sequence using Fisher-Yates algorithm
         # The lambda function first duplicates each car ID using np.tile, then applies the shuffle.
         create_car_seq = lambda num_cars, seed: fisher_yates_shuffle(
-            np.tile(np.arange(num_cars), 2), np.random.default_rng(seed))
+            np.tile(np.arange(num_cars), 2), np.random.default_rng(seed)
+        )
 
         # Generating a random BPSP instance using the class method
         bpsp = BPSP.random_instance(num_cars=num_cars, seed=seed)
 
         # Asserting if the generated car sequence in BPSP instance is identical
         # to the sequence produced by our lambda function.
         # This confirms the random instance generation works as expected.
-        self.assertTrue(all(bpsp.car_sequence == create_car_seq(num_cars, seed)),
-                        "The generated car sequence does not match the expected shuffled sequence.")
+        self.assertTrue(
+            all(bpsp.car_sequence == create_car_seq(num_cars, seed)),
+            "The generated car sequence does not match the expected shuffled sequence.",
+        )
 
     def test_bpsp_car_pos(self):
         """Test the retrieval of car positions."""
@@ -140,22 +147,26 @@ def test_bpsp_graph(self):
         """Test the generation of a graph representation of the BPSP instance."""
         car_sequence = [3, 1, 0, 0, 2, 2, 4, 4, 3, 1]
         G = nx.Graph()
-        G.add_weighted_edges_from([[3, 1, -2], [3, 4, -1], [1, 0, -1], [0, 2, 1], [2, 4, 1]])
+        G.add_weighted_edges_from(
+            [[3, 1, -2], [3, 4, -1], [1, 0, -1], [0, 2, 1], [2, 4, 1]]
+        )
 
         bpsp_graph = BPSP(car_sequence).graph
 
-        assert nx.is_isomorphic(G, bpsp_graph), "The created graph and BPSP graph are not identical,"
+        assert nx.is_isomorphic(
+            G, bpsp_graph
+        ), "The created graph and BPSP graph are not identical,"
 
     def test_bpsp_docplex_bpsp_model(self):
         """Test if the docplex model representation of BPSP is generated."""
         bpsp = BPSP.random_instance(num_cars=2, seed=1234)
-        
+
         # Obtain the model
         mdl = bpsp.docplex_bpsp_model  # Access as attribute due to @property decorator
-        
+
         # Verify that the function returns a valid model instance
         self.assertIsInstance(mdl, Model, "Model is not an instance of DOcplex Model")
-        
+
         # Manually retrieve binary variables based on their expected names
         sequence = bpsp.car_sequence
         expected_var_names = [f"w_{w}_{i}" for i, w in enumerate(sequence)]
@@ -168,13 +179,19 @@ def test_bpsp_docplex_bpsp_model(self):
             self.assertTrue(var.is_binary(), f"Variable {var} is not binary")
 
         # Check number of constraints
-        self.assertEqual(mdl.number_of_constraints, 11, "Unexpected number of constraints")
-
+        self.assertEqual(
+            mdl.number_of_constraints, 11, "Unexpected number of constraints"
+        )
+
         # Check if objective function exists
         self.assertIsNotNone(mdl.objective_expr, "Objective function is missing")
-        
+
         # Check if the objective is to minimize
-        self.assertEqual(mdl.objective_sense, ObjectiveSense.Minimize, "Objective should be of type 'minimize'")
+        self.assertEqual(
+            mdl.objective_sense,
+            ObjectiveSense.Minimize,
+            "Objective should be of type 'minimize'",
+        )
 
     def test_bpsp_solve_cplex(self):
         """
@@ -191,7 +208,7 @@ def test_bpsp_solve_cplex(self):
     def test_bpsp_paintseq_from_bits(self):
         """Test the solution of the BPSP problem using QAOA."""
         bpsp = BPSP(np.array([0, 1, 0, 1]))
-        sequence, color_swaps = bpsp.paintseq_from_bits('10')
+        sequence, color_swaps = bpsp.paintseq_from_bits("10")
         self.assertEqual(sequence, [1, 0, 0, 1])
         self.assertEqual(color_swaps, 2)
 
@@ -209,5 +226,6 @@ def test_bpsp_solve_greedy(self):
         self.assertEqual(sequence, [0, 0, 1, 1])
         self.assertEqual(color_swaps, 1)
 
+
 if __name__ == "__main__":
     unittest.main()

src/openqaoa-core/tests/test_qubo.py

@@ -159,7 +159,12 @@ def test_problem_instance(self):
             ],
             "sherrington_kirkpatrick": ["problem_type", "G"],
             "k_color": ["problem_type", "G", "k", "penalty"],
-            "binary_paint_shop_problem": ["problem_type", "car_sequence", "car_positions", "bpsp_graph"],
+            "binary_paint_shop_problem": [
+                "problem_type",
+                "car_sequence",
+                "car_positions",
+                "bpsp_graph",
+            ],
             "generic_qubo": ["problem_type"],
         }
 

src/openqaoa-qiskit/requirements.txt

@@ -1,3 +1,3 @@
-qiskit>=0.36.1
+qiskit>=0.36.1,<1.0
 qiskit-ibm-provider
 qiskit-aer