Update app.py

app.py

@@ -1,14 +1,18 @@
 import numpy as np
-
+import qiskit.tools.jupyter
 from qiskit import Aer, QuantumCircuit
 from qiskit.circuit import Parameter
 from qiskit.circuit.library import RealAmplitudes, ZZFeatureMap
 from qiskit.opflow import StateFn, PauliSumOp, AerPauliExpectation, ListOp, Gradient
 from qiskit.utils import QuantumInstance, algorithm_globals
+from qiskit_machine_learning.neural_networks import CircuitQNN
+from qiskit_machine_learning.neural_networks import OpflowQNN
+from qiskit_machine_learning.neural_networks import TwoLayerQNN
 
+#random seed
 algorithm_globals.random_seed = 42
 
-# set method to calculcate expected values
+# set method to calculate expected values
 expval = AerPauliExpectation()
 
 # define gradient method
@@ -20,7 +24,6 @@ qi_sv = QuantumInstance(Aer.get_backend("aer_simulator_statevector"))
 # we set shots to 10 as this will determine the number of samples later on.
 qi_qasm = QuantumInstance(Aer.get_backend("aer_simulator"), shots=10)
 
-from qiskit_machine_learning.neural_networks import OpflowQNN
 # construct parametrized circuit
 params1 = [Parameter("input1"), Parameter("weight1")]
 qc1 = QuantumCircuit(1)
@@ -39,9 +42,11 @@ print(op1)
 # construct OpflowQNN with the operator, the input parameters, the weight parameters,
 # the expected value, gradient, and quantum instance.
 qnn1 = OpflowQNN(op1, [params1[0]], [params1[1]], expval, gradient, qi_sv)
+
 # define (random) input and weights
 input1 = algorithm_globals.random.random(qnn1.num_inputs)
 weights1 = algorithm_globals.random.random(qnn1.num_weights)
+
 # QNN forward pass
 qnn1.forward(input1, weights1)
 
@@ -53,18 +58,18 @@ qnn1.backward(input1, weights1)
 
 # QNN batched backward pass
 qnn1.backward([input1, input1], weights1)
-
 op2 = ListOp([op1, op1])
 qnn2 = OpflowQNN(op2, [params1[0]], [params1[1]], expval, gradient, qi_sv)
+
 # QNN forward pass
 qnn2.forward(input1, weights1)
 
 # QNN backward pass
 qnn2.backward(input1, weights1)
 
-from qiskit_machine_learning.neural_networks import TwoLayerQNN
 # specify the number of qubits
 num_qubits = 3
+
 # specify the feature map
 fm = ZZFeatureMap(num_qubits, reps=2)
 fm.draw(output="mpl")
@@ -78,27 +83,27 @@ observable = PauliSumOp.from_list([("Z" * num_qubits, 1)])
 print(observable)
 
 # define two layer QNN
-qnn3 = TwoLayerQNN(
-    num_qubits, feature_map=fm, ansatz=ansatz, observable=observable, quantum_instance=qi_sv
-)
+qnn3 = TwoLayerQNN(num_qubits, feature_map=fm, ansatz=ansatz, observable=observable, quantum_instance=qi_sv)
+
 # define (random) input and weights
 input3 = algorithm_globals.random.random(qnn3.num_inputs)
 weights3 = algorithm_globals.random.random(qnn3.num_weights)
+
 # QNN forward pass
 qnn3.forward(input3, weights3)
 
 # QNN backward pass
 qnn3.backward(input3, weights3)
-
-from qiskit_machine_learning.neural_networks import CircuitQNN
 qc = RealAmplitudes(num_qubits, entanglement="linear", reps=1)
 qc.draw(output="mpl")
 
 # specify circuit QNN
 qnn4 = CircuitQNN(qc, [], qc.parameters, sparse=True, quantum_instance=qi_qasm)
+
 # define (random) input and weights
 input4 = algorithm_globals.random.random(qnn4.num_inputs)
 weights4 = algorithm_globals.random.random(qnn4.num_weights)
+
 # QNN forward pass
 qnn4.forward(input4, weights4).todense()  # returned as a sparse matrix
 
@@ -117,21 +122,24 @@ qnn6 = CircuitQNN(
     output_shape=output_shape,
     quantum_instance=qi_qasm,
 )
+
 # define (random) input and weights
 input6 = algorithm_globals.random.random(qnn6.num_inputs)
 weights6 = algorithm_globals.random.random(qnn6.num_weights)
+
 # QNN forward pass
 qnn6.forward(input6, weights6)
 
-
 # QNN backward pass
 qnn6.backward(input6, weights6)
 
 # specify circuit QNN
 qnn7 = CircuitQNN(qc, [], qc.parameters, sampling=True, quantum_instance=qi_qasm)
+
 # define (random) input and weights
 input7 = algorithm_globals.random.random(qnn7.num_inputs)
 weights7 = algorithm_globals.random.random(qnn7.num_weights)
+
 # QNN forward pass, results in samples of measured bit strings mapped to integers
 qnn7.forward(input7, weights7)
 
@@ -140,17 +148,15 @@ qnn7.backward(input7, weights7)
 
 # specify circuit QNN
 qnn8 = CircuitQNN(qc, [], qc.parameters, sampling=True, interpret=parity, quantum_instance=qi_qasm)
+
 # define (random) input and weights
 input8 = algorithm_globals.random.random(qnn8.num_inputs)
 weights8 = algorithm_globals.random.random(qnn8.num_weights)
+
 # QNN forward pass, results in samples of measured bit strings
 qnn8.forward(input8, weights8)
 
 # QNN backward pass
 qnn8.backward(input8, weights8)
 
-import qiskit.tools.jupyter
-
-#%qiskit_version_table
-#%qiskit_copyright