app.py

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 calculate expected values
expval = AerPauliExpectation()

# define gradient method
gradient = Gradient()

# define quantum instances (statevector and sample based)
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)

# construct parametrized circuit
params1 = [Parameter("input1"), Parameter("weight1")]
qc1 = QuantumCircuit(1)
qc1.h(0)
qc1.ry(params1[0], 0)
qc1.rx(params1[1], 0)
qc_sfn1 = StateFn(qc1)

# construct cost operator
H1 = StateFn(PauliSumOp.from_list([("Z", 1.0), ("X", 1.0)]))

# combine operator and circuit to objective function
op1 = ~H1 @ qc_sfn1
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)

# QNN batched forward pass
qnn1.forward([input1, input1], weights1)

# QNN backward pass
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)

# specify the number of qubits
num_qubits = 3

# specify the feature map
fm = ZZFeatureMap(num_qubits, reps=2)
fm.draw(output="mpl")

# specify the ansatz
ansatz = RealAmplitudes(num_qubits, reps=1)
ansatz.draw(output="mpl")

# specify the observable
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)

# 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)
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

# QNN backward pass, returns a tuple of sparse matrices
qnn4.backward(input4, weights4)

# specify circuit QNN
parity = lambda x: "{:b}".format(x).count("1") % 2
output_shape = 2  # this is required in case of a callable with dense output
qnn6 = CircuitQNN(
    qc,
    [],
    qc.parameters,
    sparse=False,
    interpret=parity,
    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)

# QNN backward pass
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)