test: test merging encoding_layer and entangle_layer

quafu/algorithms/templates/basic_entangle.py

@@ -82,7 +82,6 @@ def _build(self):
                 Parameter("theta_%d" % j, np.round(np.random.rand(), 3))
                 for j in range(repeat * self.num_qubits)
             ]
-        print(theta)
         for layer in range(repeat):
             j = layer * self.num_qubits
             for i in range(self.num_qubits):

tests/quafu/algorithms/merge_circuits_test.py

@@ -0,0 +1,40 @@
+# (C) Copyright 2023 Beijing Academy of Quantum Information Sciences
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import numpy as np
+from quafu.algorithms import AmplitudeEmbedding, AngleEmbedding
+from quafu.algorithms.ansatz import QuantumNeuralNetwork
+from quafu.algorithms.templates.basic_entangle import BasicEntangleLayers
+from quafu.circuits.quantum_circuit import QuantumCircuit
+
+
+class TestMergeCircuits:
+    """Example of merging circuits"""
+
+    def test_merge_circuits(self):
+        state = np.array([7, 2, 3, 4])
+        encoding_layer = AmplitudeEmbedding(state=state, num_qubits=2, normalize=True)
+
+        # feature = np.array([[6, -12.5], [8, 9.5], [5, 0.5]])
+        # encoding_layer = AngleEmbedding(features=feature, num_qubits=2, rotation="Y")
+
+        weights = np.array([[-0.850, 1.287], [0.871, 0.184]])
+        entangle_layer = BasicEntangleLayers(weights=weights, num_qubits=2)
+
+        # entangle_layer2 = BasicEntangleLayers(num_qubits=2, repeat=3)
+
+        circuit = encoding_layer + entangle_layer
+
+        qnn = QuantumNeuralNetwork(2, circuit)
+
+        qnn.draw_circuit(width=2)