!pip install qiskit

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 matplotlib import pyplot as plt
from time import time
%matplotlib inline






def preprocess(trainset):
    labels = trainset.targets
    labels = labels.numpy()
    index1 = np.where(labels == 0) # filter 3's
    index2 = np.where(labels == 1) # filter on 5's
    n=200 # Number of datapoints per class 
    index = np.concatenate((index1[0][0:n],index2[0][0:n])) 
    trainset.targets = labels[index] 
    trainset.data = trainset.data[index]
    return trainset

#converts the image into tensors
transform = torchvision.transforms.Compose([torchvision.transforms.ToTensor()]) 
trainset = datasets.MNIST(root='./mnistdata', train=True, download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(preprocess(trainset), batch_size=1, shuffle=True)
dataiter = iter(trainloader)
images, labels = dataiter.next()
print(images.shape)
print(labels.shape)
plt.imshow(images[0].numpy().squeeze(), cmap='gray_r');

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

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()

qc = TorchCircuit.apply 

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.h1 = nn.Linear(320, 50)
        self.h2 = nn.Linear(50, 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, 320)
        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

network = Net()
print(network)
optimizer = optim.SGD(network.parameters(), lr=0.003, momentum=0.9)
epochs = 30
time0 = time()
loss_list = []
for epoch in range(epochs):
    total_loss = []
    target_list = []
    for batch_idx, (data, target) in enumerate(trainloader):
        target_list.append(target.item())
        optimizer.zero_grad()
        output = network(data)
        loss = F.nll_loss(output, target)
        loss.backward()
        optimizer.step()
        total_loss.append(loss.item())
    loss_list.append(sum(total_loss)/len(total_loss))
    print("Loss = {:.2f} after epoch #{:2d}".format(loss_list[-1],epoch+1))
    #print(loss_list[-1])

# Normalise the loss between 0 and 1
print("Training finished, took {:.2f}s  after epoch #{:2d}".format(time() - time0,epochs))
for i in range(len(loss_list)):
    loss_list[i] += 1

# Plot the loss per epoch
plt.plot(np.arange(epochs),loss_list)
plt.title("Hybrid NN Training Convergence")
plt.xlabel('Training Iterations')
plt.ylabel('Loss')

