!pip install qiskit

import numpy as np
import matplotlib.pyplot as plt

import torch
from torch.autograd import Function
from torchvision import datasets, transforms
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional as F

import qiskit
from qiskit import transpile, assemble
from qiskit.visualization import *

def to_numbers(tensor_list):
    num_list = []
    for tensor in tensor_list:
        num_list += [tensor.item()]
    return num_list


import numpy as np
import torch
from torch.autograd import Function
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional as F
import torchvision
from torchvision import datasets, transforms
from qiskit import QuantumRegister, QuantumCircuit, ClassicalRegister, execute
from qiskit.circuit import Parameter
from qiskit import Aer
from tqdm import tqdm
from matplotlib import pyplot as plt
%matplotlib inline

class QiskitCircuit():
    
    # Specify initial parameters and the quantum circuit
    
    def __init__(self,shots):
        self.theta = Parameter('Theta')
        self.shots = shots
        
        def create_circuit():
            qr = QuantumRegister(1,'q')
            cr = ClassicalRegister(1,'c')
            ckt = QuantumCircuit(qr,cr)
            ckt.h(qr[0])
            ckt.barrier()
            ckt.ry(self.theta,qr[0])
            ckt.barrier()
            ckt.measure(qr,cr)
            return ckt
        
        self.circuit = create_circuit()
        
    def N_qubit_expectation_Z(self,counts, shots, nr_qubits):
        expects = np.zeros(nr_qubits)
        for key in counts.keys():
            perc = counts[key]/shots
            check = np.array([(float(key[i])-1/2)*2*perc for i in range(nr_qubits)])
            expects += check   
        return expects    
    
    def bind(self, parameters):
        [self.theta] = to_numbers(parameters)
        self.circuit.data[2][0]._params = to_numbers(parameters)
    
    def run(self, i):
        self.bind(i)
        backend = Aer.get_backend('qasm_simulator')
        job_sim = execute(self.circuit,backend,shots=self.shots)
        result_sim = job_sim.result()
        counts = result_sim.get_counts(self.circuit)
        return self.N_qubit_expectation_Z(counts,self.shots,1)

class TorchCircuit(Function):    

    @staticmethod
    def forward(ctx, i):
        if not hasattr(ctx, 'QiskitCirc'):
            ctx.QiskitCirc = QiskitCircuit(shots=100)
            
        exp_value = ctx.QiskitCirc.run(i[0])
        
        result = torch.tensor([exp_value]) # store the result as a torch tensor
        
        ctx.save_for_backward(result, i)
        
        return result
    
    @staticmethod
    def backward(ctx, grad_output):
        s = np.pi/2
        
        forward_tensor, i = ctx.saved_tensors  
        
        # Obtain paramaters 
        input_numbers = to_numbers(i[0])
        
        gradient = []
        
        for k in range(len(input_numbers)):
            input_plus_s = input_numbers
            input_plus_s[k] = input_numbers[k] + s  # Shift up by s
            
            exp_value_plus = ctx.QiskitCirc.run(torch.tensor(input_plus_s))[0]
            result_plus_s = torch.tensor([exp_value_plus])
            
            input_minus_s = input_numbers
            input_minus_s[k] = input_numbers[k] - s # Shift down by s
            
            exp_value_minus = ctx.QiskitCirc.run(torch.tensor(input_minus_s))[0]
            result_minus_s = torch.tensor([exp_value_minus])

            gradient_result = (result_plus_s - result_minus_s)

            gradient.append(gradient_result)
            
        result = torch.tensor([gradient])
        
        return result.float() * grad_output.float()

import torchvision
transform = torchvision.transforms.Compose([torchvision.transforms.ToTensor()]) # transform images to tensors/vectors
cifar_trainset = datasets.CIFAR10(root='./data1', train=True, download=True, transform=transform)

labels = cifar_trainset.targets # get the labels for the data
labels = np.array(labels)

idx1 = np.where(labels == 0) # filter on aeroplanes
idx2 = np.where(labels == 1) # filter on automobiles

# Specify number of datapoints per class (i.e. there will be n pictures of automobiles and n pictures of aeroplanes in the training set)
n=100

# concatenate the data indices
idx = np.concatenate((idx1[0][0:n],idx2[0][0:n])) 

# create the filtered dataset for our training set
cifar_trainset.targets = labels[idx] 
cifar_trainset.data = cifar_trainset.data[idx]

train_loader = torch.utils.data.DataLoader(cifar_trainset, batch_size=1, shuffle=True)

import tensorflow as tf

n_samples_show = 6

data_iter = iter(train_loader)
fig, axes = plt.subplots(nrows=1, ncols=n_samples_show, figsize=(10, 2))

while n_samples_show > 0:
    images, targets = data_iter.__next__()
    #axes[n_samples_show - 1].imshow( tf.shape( tf.squeeze(images[0]) ),cmap='gray' )
    #plt.imshow((tf.squeeze(images[0])))
      
    #plt.imshow( tf.shape( tf.squeeze(x_train) ) )
    axes[n_samples_show - 1].imshow(images[0].numpy().squeeze(), cmap='gray')
    axes[n_samples_show - 1].set_xticks([])
    axes[n_samples_show - 1].set_yticks([])
    axes[n_samples_show - 1].set_title("Labeled: {}".format(targets.item()))
    
    n_samples_show -= 1

#Testing data

transform = torchvision.transforms.Compose([torchvision.transforms.ToTensor()]) # transform images to tensors/vectors
cifar_testset = datasets.CIFAR10(root='./data1', train=False, download=True, transform=transform)

labels1 = cifar_testset.targets # get the labels for the data
labels1 = np.array(labels1)

idx1_ae = np.where(labels1 == 0) # filter on aeroplanes
idx2_au = np.where(labels1 == 1) # filter on automobiles

# Specify number of datapoints per class (i.e. there will be n pictures of automobiles and n pictures of aeroplanes in the training set)

n=50

# concatenate the data indices
idxa = np.concatenate((idx1_ae[0][0:n],idx2_au[0][0:n])) 

# create the filtered dataset for our training set
cifar_testset.targets = labels[idxa] 
cifar_testset.data = cifar_testset.data[idxa]

test_loader = torch.utils.data.DataLoader(cifar_testset, batch_size=1, shuffle=True)

class QuantumCircuit:
    """ 
    This class provides a simple interface for interaction 
    with the quantum circuit 
    """
    
    def __init__(self, n_qubits, backend, shots):
        # --- Circuit definition ---
        self._circuit = qiskit.QuantumCircuit(n_qubits)
        
        all_qubits = [i for i in range(n_qubits)]
        self.theta = qiskit.circuit.Parameter('theta')
        
        self._circuit.h(all_qubits)
        self._circuit.barrier()
        self._circuit.ry(self.theta, all_qubits)
        
        self._circuit.measure_all()
        # ---------------------------

        self.backend = backend
        self.shots = shots
    
    def run(self, thetas):
        t_qc = transpile(self._circuit,
                         self.backend)
        qobj = assemble(t_qc,
                        shots=self.shots,
                        parameter_binds = [{self.theta: theta} for theta in thetas])
        job = self.backend.run(qobj)
        result = job.result().get_counts()
        
        counts = np.array(list(result.values()))
        states = np.array(list(result.keys())).astype(float)
        
        # Compute probabilities for each state
        probabilities = counts / self.shots
        # Get state expectation
        expectation = np.sum(states * probabilities)
        
        return np.array([expectation])

simulator = qiskit.Aer.get_backend('qasm_simulator')

circuit = QuantumCircuit(1, simulator, 100)
print('Expected value for rotation pi {}'.format(circuit.run([np.pi])[0]))
circuit._circuit.draw()

class HybridFunction(Function):
    """ Hybrid quantum - classical function definition """
    
    @staticmethod
    def forward(ctx, input, quantum_circuit, shift):
        """ Forward pass computation """
        ctx.shift = shift
        ctx.quantum_circuit = quantum_circuit

        expectation_z = ctx.quantum_circuit.run(input[0].tolist())
        result = torch.tensor([expectation_z])
        ctx.save_for_backward(input, result)

        return result
        
    @staticmethod
    def backward(ctx, grad_output):
        """ Backward pass computation """
        input, expectation_z = ctx.saved_tensors
        input_list = np.array(input.tolist())
        
        shift_right = input_list + np.ones(input_list.shape) * ctx.shift
        shift_left = input_list - np.ones(input_list.shape) * ctx.shift
        
        gradients = []
        for i in range(len(input_list)):
            expectation_right = ctx.quantum_circuit.run(shift_right[i])
            expectation_left  = ctx.quantum_circuit.run(shift_left[i])
            
            gradient = torch.tensor([expectation_right]) - torch.tensor([expectation_left])
            gradients.append(gradient)
        gradients = np.array([gradients]).T
        return torch.tensor([gradients]).float() * grad_output.float(), None, None

class Hybrid(nn.Module):
    """ Hybrid quantum - classical layer definition """
    
    def __init__(self, backend, shots, shift):
        super(Hybrid, self).__init__()
        self.quantum_circuit = QuantumCircuit(1, backend, shots)
        self.shift = shift
        
    def forward(self, input):
        return HybridFunction.apply(input, self.quantum_circuit, self.shift)

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.dropout = nn.Dropout2d()
        self.fc1 = nn.Linear(500, 500)
        self.fc2 = nn.Linear(500, 1)
        self.hybrid = Hybrid(qiskit.Aer.get_backend('qasm_simulator'), 100, np.pi / 2)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = F.max_pool2d(x, 2)
        x = F.relu(self.conv2(x))
        x = F.max_pool2d(x, 2)
        x = self.dropout(x)
        x = x.view(1, -1)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        x = self.hybrid(x)
        return torch.cat((x, 1 - x), -1)

#qc = TorchCircuit.apply 
Ignore this cell
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.conv2_drop = nn.Dropout2d()
        self.h1 = nn.Linear(500, 500)
        self.h2 = nn.Linear(500, 1)

    def forward(self,x):
        x = F.relu(F.max_pool2d(self.conv1(x), 2))
        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
        x = x.view(-1, 500)
        x = F.relu(self.h1(x))
        x = F.dropout(x, training=self.training)
        x = self.h2(x)
        x = qc(x)
        x = (x+1)/2  # Normalise the inputs to 1 or 0
        x = torch.cat((x, 1-x), -1)
        return x

model = Net()
optimizer = optim.Adam(model.parameters(), lr=0.001)
loss_func = nn.NLLLoss()

epochs = 5
loss_list = []

model.train()
for epoch in range(epochs):
    total_loss = []
    for batch_idx, (data, target) in enumerate(train_loader):
        optimizer.zero_grad()
        # Forward pass
        output = model(data)
        # Calculating loss
        loss = loss_func(output, target)
        # Backward pass
        loss.backward()
        # Optimize the weights
        optimizer.step()
        
        total_loss.append(loss.item())
    loss_list.append(sum(total_loss)/len(total_loss))
    print('Training [{:.0f}%]\tLoss: {:.4f}'.format(
        100. * (epoch + 1) / epochs, loss_list[-1]))

plt.plot(loss_list)
plt.title('Hybrid NN Training Convergence')
plt.xlabel('Training Iterations')
plt.ylabel('Neg Log Likelihood Loss')

model = Net()
optimizer = optim.Adam(model.parameters(), lr=0.001)
loss_func = nn.NLLLoss()

epochs = 10
loss_list = []

model.train()
for epoch in range(epochs):
    total_loss = []
    for batch_idx, (data, target) in enumerate(train_loader):
        optimizer.zero_grad()
        # Forward pass
        output = model(data)
        # Calculating loss
        loss = loss_func(output, target)
        # Backward pass
        loss.backward()
        # Optimize the weights
        optimizer.step()
        
        total_loss.append(loss.item())
    loss_list.append(sum(total_loss)/len(total_loss))
    print('Training [{:.0f}%]\tLoss: {:.4f}'.format(
        100. * (epoch + 1) / epochs, loss_list[-1]))

plt.plot(loss_list)
plt.title('Hybrid NN Training Convergence')
plt.xlabel('Training Iterations')
plt.ylabel('Neg Log Likelihood Loss')

model = Net()
optimizer = optim.Adam(model.parameters(), lr=0.001)
loss_func = nn.NLLLoss()

epochs = 20
loss_list = []

model.train()
for epoch in range(epochs):
    total_loss = []
    for batch_idx, (data, target) in enumerate(train_loader):
        optimizer.zero_grad()
        # Forward pass
        output = model(data)
        # Calculating loss
        loss = loss_func(output, target)
        # Backward pass
        loss.backward()
        # Optimize the weights
        optimizer.step()
        
        total_loss.append(loss.item())
    loss_list.append(sum(total_loss)/len(total_loss))
    print('Training [{:.0f}%]\tLoss: {:.4f}'.format(
        100. * (epoch + 1) / epochs, loss_list[-1]))

plt.plot(loss_list)
plt.title('Hybrid NN Training Convergence')
plt.xlabel('Training Iterations')
plt.ylabel('Neg Log Likelihood Loss')

#Testing the  quantum hybrid in order to comapre it with the classical one
model.eval()
with torch.no_grad():
    
    correct = 0
    for batch_idx, (data, target) in enumerate(test_loader):
        output = model(data)
        
        pred = output.argmax(dim=1, keepdim=True) 
        correct += pred.eq(target.view_as(pred)).sum().item()
        
        loss = loss_func(output, target)
        total_loss.append(loss.item())
        
    print('Performance on test data:\n\tLoss: {:.4f}\n\tAccuracy: {:.1f}%'.format(
        sum(total_loss) / len(total_loss),
        correct / len(test_loader) * 100)
        )

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.dropout = nn.Dropout2d()
        self.fc1 = nn.Linear(500, 500)
        self.fc2 = nn.Linear(500,1)
        self.hybrid = Hybrid(qiskit.Aer.get_backend('qasm_simulator'), 100, np.pi / 2)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = F.max_pool2d(x, 2)
        x = F.relu(self.conv2(x))
        x = F.max_pool2d(x, 2)
        x = self.dropout(x)
        x = x.view(1, -1)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        x = self.hybrid(x)
        return torch.cat((x, 1 - x), -1)

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.conv2_drop = nn.Dropout2d()
        self.fc1 = nn.Linear(320, 50)
        self.fc2 = nn.Linear(50, 2)

    def forward(self, x):
        x = F.relu(F.max_pool2d(self.conv1(x), 2))
        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
        x = x.view(-1, 320)
        x = F.relu(self.fc1(x))
        x = F.dropout(x, training=self.training)
        x = self.fc2(x)
        return F.softmax(x)