टिप्पणी

यह पृष्ठ docs/tutorials/05_torch_connector.ipynb से उत्पन्न किया गया है।

टॉर्च कनेक्टर और संकर QNNs

यह ट्यूटोरियल Qiskit के TorchConnector वर्ग का परिचय देता है, और दर्शाता है कि कैसे TorchConnector Qiskit Machine Learning से PyTorch वर्कफ़्लो में किसी भी NeuralNetwork के प्राकृतिक एकीकरण की अनुमति देता है। TorchConnector एक Qiskit NeuralNetwork लेता है और इसे एक PyTorch Module के रूप में उपलब्ध कराता है। परिणामी मॉड्यूल को मूल रूप से PyTorch शास्त्रीय आर्किटेक्चर में शामिल किया जा सकता है और अतिरिक्त विचारों के बिना संयुक्त रूप से प्रशिक्षित किया जा सकता है, जिससे उपन्यास hybrid quantum-classical मशीन लर्निंग आर्किटेक्चर के विकास और परीक्षण को सक्षम किया जा सकता है।

विषय-सूची

भाग 1: सरल वर्गीकरण और प्रतिगमन

इस ट्यूटोरियल का पहला भाग दिखाता है कि कैसे PyTorch के स्वचालित विभेदन इंजन का उपयोग करके क्वांटम न्यूरल नेटवर्क को प्रशिक्षित किया जा सकता है (torch.autograd, link) सरल वर्गीकरण और प्रतिगमन कार्यों के लिए।

1. वर्गीकरण

   1. Classification with PyTorch and EstimatorQNN

   2. Classification with PyTorch and SamplerQNN

2. प्रतिगमन

   1. Regression with PyTorch and SamplerQNN

भाग 2: MNIST वर्गीकरण, हाइब्रिड QNNs

इस ट्यूटोरियल का दूसरा भाग बताता है कि हाइब्रिड क्वांटम-शास्त्रीय तरीके से MNIST डेटा को वर्गीकृत करने के लिए एक (क्वांटम) न्यूरलनेटवर्क को लक्ष्य PyTorch वर्कफ़्लो (इस मामले में, एक विशिष्ट CNN आर्किटेक्चर) में कैसे एम्बेड किया जाए।

---

# Necessary imports

import numpy as np
import matplotlib.pyplot as plt

from torch import Tensor
from torch.nn import Linear, CrossEntropyLoss, MSELoss
from torch.optim import LBFGS

from qiskit import QuantumCircuit
from qiskit.utils import algorithm_globals
from qiskit.circuit import Parameter
from qiskit.circuit.library import RealAmplitudes, ZZFeatureMap
from qiskit_machine_learning.neural_networks import SamplerQNN, EstimatorQNN
from qiskit_machine_learning.connectors import TorchConnector

# Set seed for random generators
algorithm_globals.random_seed = 42

भाग 1: सरल वर्गीकरण एवं प्रतिगमन

1. वर्गीकरण

सबसे पहले, हम दिखाते हैं कि कैसे TorchConnector क्वांटम NeuralNetwork को प्रशिक्षित करने की अनुमति देता है ताकि PyTorch के स्वचालित विभेदन इंजन का उपयोग करके वर्गीकरण कार्यों को हल किया जा सके। इसे स्पष्ट करने के लिए, हम बेतरतीब ढंग से उत्पन्न डेटासेट पर binary classification करेंगे।

# Generate random dataset

# Select dataset dimension (num_inputs) and size (num_samples)
num_inputs = 2
num_samples = 20

# Generate random input coordinates (X) and binary labels (y)
X = 2 * algorithm_globals.random.random([num_samples, num_inputs]) - 1
y01 = 1 * (np.sum(X, axis=1) >= 0)  # in { 0,  1}, y01 will be used for SamplerQNN example
y = 2 * y01 - 1  # in {-1, +1}, y will be used for EstimatorQNN example

# Convert to torch Tensors
X_ = Tensor(X)
y01_ = Tensor(y01).reshape(len(y)).long()
y_ = Tensor(y).reshape(len(y), 1)

# Plot dataset
for x, y_target in zip(X, y):
    if y_target == 1:
        plt.plot(x[0], x[1], "bo")
    else:
        plt.plot(x[0], x[1], "go")
plt.plot([-1, 1], [1, -1], "--", color="black")
plt.show()

A. Classification with PyTorch and EstimatorQNN

Linking an EstimatorQNN to PyTorch is relatively straightforward. Here we illustrate this by using the EstimatorQNN constructed from a feature map and an ansatz.

# Set up a circuit
feature_map = ZZFeatureMap(num_inputs)
ansatz = RealAmplitudes(num_inputs)
qc = QuantumCircuit(num_inputs)
qc.compose(feature_map, inplace=True)
qc.compose(ansatz, inplace=True)
qc.draw("mpl")

# Setup QNN
qnn1 = EstimatorQNN(
    circuit=qc, input_params=feature_map.parameters, weight_params=ansatz.parameters
)

# Set up PyTorch module
# Note: If we don't explicitly declare the initial weights
# they are chosen uniformly at random from [-1, 1].
initial_weights = 0.1 * (2 * algorithm_globals.random.random(qnn1.num_weights) - 1)
model1 = TorchConnector(qnn1, initial_weights=initial_weights)
print("Initial weights: ", initial_weights)

Initial weights:  [-0.01256962  0.06653564  0.04005302 -0.03752667  0.06645196  0.06095287
 -0.02250432 -0.04233438]

# Test with a single input
model1(X_[0, :])

tensor([-0.3285], grad_fn=<_TorchNNFunctionBackward>)

आप्टमाइज़र

किसी भी मशीन लर्निंग मॉडल के प्रशिक्षण के लिए अनुकूलक का चुनाव हमारे प्रशिक्षण के परिणाम की सफलता को निर्धारित करने में महत्वपूर्ण हो सकता है। TorchConnector का उपयोग करते समय, हमें [torch.optim] पैकेज (link <https://pytorch.org/docs/stable/optim.html> `__)में परिभाषित सभी अनुकूलक एल्गोरिदम तक पहुंच प्राप्त होती है। लोकप्रिय मशीन लर्निंग आर्किटेक्चर में उपयोग किए जाने वाले कुछ सबसे प्रसिद्ध एल्गोरिदम में *Adam*, *SGD*, या *Adagrad* शामिल हैं। हालाँकि, इस ट्यूटोरियल के लिए हम L-BFGS एल्गोरिथम (``torch.optim.LBFGS`) का उपयोग करेंगे, जो संख्यात्मक अनुकूलन के लिए सबसे अच्छी तरह से ज्ञात दूसरे क्रम के अनुकूलन एल्गोरिदम में से एक है।

लॉस फंकशन

हानि फ़ंक्शन के लिए, हम torch.nn से PyTorch के पूर्व-निर्धारित मॉड्यूल का भी लाभ उठा सकते हैं, जैसे कि Cross-Entropy या माध्य चुकता त्रुटि हानियाँ।

💡 Clarification : In classical machine learning, the general rule of thumb is to apply a Cross-Entropy loss to classification tasks, and MSE loss to regression tasks. However, this recommendation is given under the assumption that the output of the classification network is a class probability value in the \([0, 1]\) range (usually this is achieved through a Softmax layer). Because the following example for EstimatorQNN does not include such layer, and we don’t apply any mapping to the output (the following section shows an example of application of parity mapping with SamplerQNNs), the QNN’s output can take any value in the range \([-1, 1]\). In case you were wondering, this is the reason why this particular example uses MSELoss for classification despite it not being the norm (but we encourage you to experiment with different loss functions and see how they can impact training results).

# Define optimizer and loss
optimizer = LBFGS(model1.parameters())
f_loss = MSELoss(reduction="sum")

# Start training
model1.train()  # set model to training mode


# Note from (https://pytorch.org/docs/stable/optim.html):
# Some optimization algorithms such as LBFGS need to
# reevaluate the function multiple times, so you have to
# pass in a closure that allows them to recompute your model.
# The closure should clear the gradients, compute the loss,
# and return it.
def closure():
    optimizer.zero_grad()  # Initialize/clear gradients
    loss = f_loss(model1(X_), y_)  # Evaluate loss function
    loss.backward()  # Backward pass
    print(loss.item())  # Print loss
    return loss


# Run optimizer step4
optimizer.step(closure)

25.535646438598633
22.696760177612305
20.039228439331055
19.687908172607422
19.267208099365234
19.025373458862305
18.154708862304688
17.337854385375977
19.082578659057617
17.073287963867188
16.21839141845703
14.992582321166992
14.929339408874512
14.914533615112305
14.907636642456055
14.902364730834961
14.902134895324707
14.90211009979248
14.902111053466797

tensor(25.5356, grad_fn=<MseLossBackward0>)

# Evaluate model and compute accuracy
y_predict = []
for x, y_target in zip(X, y):
    output = model1(Tensor(x))
    y_predict += [np.sign(output.detach().numpy())[0]]

print("Accuracy:", sum(y_predict == y) / len(y))

# Plot results
# red == wrongly classified
for x, y_target, y_p in zip(X, y, y_predict):
    if y_target == 1:
        plt.plot(x[0], x[1], "bo")
    else:
        plt.plot(x[0], x[1], "go")
    if y_target != y_p:
        plt.scatter(x[0], x[1], s=200, facecolors="none", edgecolors="r", linewidths=2)
plt.plot([-1, 1], [1, -1], "--", color="black")
plt.show()

Accuracy: 0.8

लाल घेरे गलत रूप से वर्गीकृत डेटा बिंदुओं को दर्शाते हैं।

B. Classification with PyTorch and SamplerQNN

Linking a SamplerQNN to PyTorch requires a bit more attention than EstimatorQNN. Without the correct setup, backpropagation is not possible.

विशेष रूप से, हमें यह सुनिश्चित करना चाहिए कि हम नेटवर्क के फॉरवर्ड पास (sparse=False) में संभावनाओं की एक सघन सरणी लौटा रहे हैं। यह पैरामीटर डिफ़ॉल्ट रूप से गलत पर सेट है, इसलिए हमें केवल यह सुनिश्चित करना है कि इसे बदला नहीं गया है।

⚠️ Attention: If we define a custom interpret function ( in the example: parity), we must remember to explicitly provide the desired output shape ( in the example: 2). For more info on the initial parameter setup for SamplerQNN, please check out the official qiskit documentation.

# Define feature map and ansatz
feature_map = ZZFeatureMap(num_inputs)
ansatz = RealAmplitudes(num_inputs, entanglement="linear", reps=1)

# Define quantum circuit of num_qubits = input dim
# Append feature map and ansatz
qc = QuantumCircuit(num_inputs)
qc.compose(feature_map, inplace=True)
qc.compose(ansatz, inplace=True)

# Define SamplerQNN and initial setup
parity = lambda x: "{:b}".format(x).count("1") % 2  # optional interpret function
output_shape = 2  # parity = 0, 1
qnn2 = SamplerQNN(
    circuit=qc,
    input_params=feature_map.parameters,
    weight_params=ansatz.parameters,
    interpret=parity,
    output_shape=output_shape,
)

# Set up PyTorch module
# Reminder: If we don't explicitly declare the initial weights
# they are chosen uniformly at random from [-1, 1].
initial_weights = 0.1 * (2 * algorithm_globals.random.random(qnn2.num_weights) - 1)
print("Initial weights: ", initial_weights)
model2 = TorchConnector(qnn2, initial_weights)

Initial weights:  [ 0.0364991  -0.0720495  -0.06001836 -0.09852755]

ऑप्टिमाइज़र और हानि फ़ंक्शन विकल्पों पर अनुस्मारक के लिए, आप इस अनुभाग पर वापस जा सकते हैं।

# Define model, optimizer, and loss
optimizer = LBFGS(model2.parameters())
f_loss = CrossEntropyLoss()  # Our output will be in the [0,1] range

# Start training
model2.train()

# Define LBFGS closure method (explained in previous section)
def closure():
    optimizer.zero_grad(set_to_none=True)  # Initialize gradient
    loss = f_loss(model2(X_), y01_)  # Calculate loss
    loss.backward()  # Backward pass

    print(loss.item())  # Print loss
    return loss


# Run optimizer (LBFGS requires closure)
optimizer.step(closure);

0.6925069093704224
0.6881508231163025
0.6516683101654053
0.6485998034477234
0.6394743919372559
0.7057444453239441
0.669085681438446
0.766187310218811
0.7188469171524048
0.7919709086418152
0.7598814964294434
0.7028256058692932
0.7486447095870972
0.6890242695808411
0.7760348916053772
0.7892935276031494
0.7556288242340088
0.7058126330375671
0.7203161716461182
0.7030722498893738

# Evaluate model and compute accuracy
y_predict = []
for x in X:
    output = model2(Tensor(x))
    y_predict += [np.argmax(output.detach().numpy())]

print("Accuracy:", sum(y_predict == y01) / len(y01))

# plot results
# red == wrongly classified
for x, y_target, y_ in zip(X, y01, y_predict):
    if y_target == 1:
        plt.plot(x[0], x[1], "bo")
    else:
        plt.plot(x[0], x[1], "go")
    if y_target != y_:
        plt.scatter(x[0], x[1], s=200, facecolors="none", edgecolors="r", linewidths=2)
plt.plot([-1, 1], [1, -1], "--", color="black")
plt.show()

Accuracy: 0.5

लाल घेरे गलत रूप से वर्गीकृत डेटा बिंदुओं को दर्शाते हैं।

2. प्रतिगमन

We use a model based on the EstimatorQNN to also illustrate how to perform a regression task. The chosen dataset in this case is randomly generated following a sine wave.

# Generate random dataset

num_samples = 20
eps = 0.2
lb, ub = -np.pi, np.pi
f = lambda x: np.sin(x)

X = (ub - lb) * algorithm_globals.random.random([num_samples, 1]) + lb
y = f(X) + eps * (2 * algorithm_globals.random.random([num_samples, 1]) - 1)
plt.plot(np.linspace(lb, ub), f(np.linspace(lb, ub)), "r--")
plt.plot(X, y, "bo")
plt.show()

A. Regression with PyTorch and EstimatorQNN

The network definition and training loop will be analogous to those of the classification task using EstimatorQNN. In this case, we define our own feature map and ansatz, but let’s do it a little different.

# Construct simple feature map
param_x = Parameter("x")
feature_map = QuantumCircuit(1, name="fm")
feature_map.ry(param_x, 0)

# Construct simple feature map
param_y = Parameter("y")
ansatz = QuantumCircuit(1, name="vf")
ansatz.ry(param_y, 0)

qc = QuantumCircuit(1)
qc.compose(feature_map, inplace=True)
qc.compose(ansatz, inplace=True)

# Construct QNN
qnn3 = EstimatorQNN(circuit=qc, input_params=[param_x], weight_params=[param_y])

# Set up PyTorch module
# Reminder: If we don't explicitly declare the initial weights
# they are chosen uniformly at random from [-1, 1].
initial_weights = 0.1 * (2 * algorithm_globals.random.random(qnn3.num_weights) - 1)
model3 = TorchConnector(qnn3, initial_weights)

ऑप्टिमाइज़र और हानि फ़ंक्शन विकल्पों पर अनुस्मारक के लिए, आप इस अनुभाग पर वापस जा सकते हैं।

# Define optimizer and loss function
optimizer = LBFGS(model3.parameters())
f_loss = MSELoss(reduction="sum")

# Start training
model3.train()  # set model to training mode

# Define objective function
def closure():
    optimizer.zero_grad(set_to_none=True)  # Initialize gradient
    loss = f_loss(model3(Tensor(X)), Tensor(y))  # Compute batch loss
    loss.backward()  # Backward pass
    print(loss.item())  # Print loss
    return loss


# Run optimizer
optimizer.step(closure)

14.947757720947266
2.948650360107422
8.952412605285645
0.37905153632164
0.24995625019073486
0.2483610212802887
0.24835753440856934

tensor(14.9478, grad_fn=<MseLossBackward0>)

# Plot target function
plt.plot(np.linspace(lb, ub), f(np.linspace(lb, ub)), "r--")

# Plot data
plt.plot(X, y, "bo")

# Plot fitted line
y_ = []
for x in np.linspace(lb, ub):
    output = model3(Tensor([x]))
    y_ += [output.detach().numpy()[0]]
plt.plot(np.linspace(lb, ub), y_, "g-")
plt.show()

---

भाग 2: MNIST वर्गीकरण, हाइब्रिड QNNs

इस दूसरे भाग में, हम दिखाते हैं कि MNIST हस्तलिखित अंकों के डेटासेट पर अधिक जटिल छवि वर्गीकरण कार्य करने के लिए ``TorchConnector` का उपयोग करके एक हाइब्रिड क्वांटम-शास्त्रीय तंत्रिका नेटवर्क का लाभ कैसे उठाया जाए।

हाइब्रिड क्वांटम-शास्त्रीय तंत्रिका नेटवर्क पर अधिक विस्तृत (पूर्व-`` टॉर्च कनेक्टर`) स्पष्टीकरण के लिए, आप किस्किट पाठ्यपुस्तक में संबंधित अनुभाग देख सकते हैं।.

# Additional torch-related imports
import torch
from torch import cat, no_grad, manual_seed
from torch.utils.data import DataLoader
from torchvision import datasets, transforms
import torch.optim as optim
from torch.nn import (
    Module,
    Conv2d,
    Linear,
    Dropout2d,
    NLLLoss,
    MaxPool2d,
    Flatten,
    Sequential,
    ReLU,
)
import torch.nn.functional as F

चरण 1: ट्रेन और परीक्षण के लिए डेटा लोडर को परिभाषित करना

हम torchvision API का लाभ उठाते हुए MNIST डेटासेट <https://en.wikipedia.org/wiki/MNIST_database>`__के सबसेट को सीधे लोड करते हैं और ट्रेन और परीक्षण के लिए मशाल ``DataLoader`s (लिंक) को परिभाषित करें।

# Train Dataset
# -------------

# Set train shuffle seed (for reproducibility)
manual_seed(42)

batch_size = 1
n_samples = 100  # We will concentrate on the first 100 samples

# Use pre-defined torchvision function to load MNIST train data
X_train = datasets.MNIST(
    root="./data", train=True, download=True, transform=transforms.Compose([transforms.ToTensor()])
)

# Filter out labels (originally 0-9), leaving only labels 0 and 1
idx = np.append(
    np.where(X_train.targets == 0)[0][:n_samples], np.where(X_train.targets == 1)[0][:n_samples]
)
X_train.data = X_train.data[idx]
X_train.targets = X_train.targets[idx]

# Define torch dataloader with filtered data
train_loader = DataLoader(X_train, batch_size=batch_size, shuffle=True)

यदि हम एक त्वरित विज़ुअलाइज़ेशन करते हैं तो हम देख सकते हैं कि ट्रेन डेटासेट में हस्तलिखित 0s और 1s की छवियां होती हैं।

n_samples_show = 6

data_iter = iter(train_loader)
fig, axes = plt.subplots(nrows=1, ncols=n_samples_show, figsize=(10, 3))

while n_samples_show > 0:
    images, targets = data_iter.__next__()

    axes[n_samples_show - 1].imshow(images[0, 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[0].item()))

    n_samples_show -= 1

# Test Dataset
# -------------

# Set test shuffle seed (for reproducibility)
# manual_seed(5)

n_samples = 50

# Use pre-defined torchvision function to load MNIST test data
X_test = datasets.MNIST(
    root="./data", train=False, download=True, transform=transforms.Compose([transforms.ToTensor()])
)

# Filter out labels (originally 0-9), leaving only labels 0 and 1
idx = np.append(
    np.where(X_test.targets == 0)[0][:n_samples], np.where(X_test.targets == 1)[0][:n_samples]
)
X_test.data = X_test.data[idx]
X_test.targets = X_test.targets[idx]

# Define torch dataloader with filtered data
test_loader = DataLoader(X_test, batch_size=batch_size, shuffle=True)

चरण 2: QNN और हाइब्रिड मॉडल को परिभाषित करना

This second step shows the power of the TorchConnector. After defining our quantum neural network layer (in this case, a EstimatorQNN), we can embed it into a layer in our torch Module by initializing a torch connector as TorchConnector(qnn).

⚠️ ध्यान दें: हाइब्रिड मॉडल में पर्याप्त ग्रेडिएंट बैकप्रोपेगेशन के लिए, हमें qnn इनिशियलाइज़ेशन के दौरान प्रारंभिक पैरामीटर ``input_gradients` को TRUE पर सेट करना होगा।

# Define and create QNN
def create_qnn():
    feature_map = ZZFeatureMap(2)
    ansatz = RealAmplitudes(2, reps=1)
    qc = QuantumCircuit(2)
    qc.compose(feature_map, inplace=True)
    qc.compose(ansatz, inplace=True)

    # REMEMBER TO SET input_gradients=True FOR ENABLING HYBRID GRADIENT BACKPROP
    qnn = EstimatorQNN(
        circuit=qc,
        input_params=feature_map.parameters,
        weight_params=ansatz.parameters,
        input_gradients=True,
    )
    return qnn


qnn4 = create_qnn()

# Define torch NN module


class Net(Module):
    def __init__(self, qnn):
        super().__init__()
        self.conv1 = Conv2d(1, 2, kernel_size=5)
        self.conv2 = Conv2d(2, 16, kernel_size=5)
        self.dropout = Dropout2d()
        self.fc1 = Linear(256, 64)
        self.fc2 = Linear(64, 2)  # 2-dimensional input to QNN
        self.qnn = TorchConnector(qnn)  # Apply torch connector, weights chosen
        # uniformly at random from interval [-1,1].
        self.fc3 = Linear(1, 1)  # 1-dimensional output from QNN

    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(x.shape[0], -1)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        x = self.qnn(x)  # apply QNN
        x = self.fc3(x)
        return cat((x, 1 - x), -1)


model4 = Net(qnn4)

चरण 3: प्रशिक्षण

# Define model, optimizer, and loss function
optimizer = optim.Adam(model4.parameters(), lr=0.001)
loss_func = NLLLoss()

# Start training
epochs = 10  # Set number of epochs
loss_list = []  # Store loss history
model4.train()  # Set model to training mode

for epoch in range(epochs):
    total_loss = []
    for batch_idx, (data, target) in enumerate(train_loader):
        optimizer.zero_grad(set_to_none=True)  # Initialize gradient
        output = model4(data)  # Forward pass
        loss = loss_func(output, target)  # Calculate loss
        loss.backward()  # Backward pass
        optimizer.step()  # Optimize weights
        total_loss.append(loss.item())  # Store loss
    loss_list.append(sum(total_loss) / len(total_loss))
    print("Training [{:.0f}%]\tLoss: {:.4f}".format(100.0 * (epoch + 1) / epochs, loss_list[-1]))

Training [10%]  Loss: -1.1630
Training [20%]  Loss: -1.5294
Training [30%]  Loss: -1.7855
Training [40%]  Loss: -1.9863
Training [50%]  Loss: -2.2257
Training [60%]  Loss: -2.4513
Training [70%]  Loss: -2.6758
Training [80%]  Loss: -2.8832
Training [90%]  Loss: -3.1006
Training [100%] Loss: -3.3061

# Plot loss convergence
plt.plot(loss_list)
plt.title("Hybrid NN Training Convergence")
plt.xlabel("Training Iterations")
plt.ylabel("Neg. Log Likelihood Loss")
plt.show()

Now we’ll save the trained model, just to show how a hybrid model can be saved and re-used later for inference. To save and load hybrid models, when using the TorchConnector, follow the PyTorch recommendations of saving and loading the models.

torch.save(model4.state_dict(), "model4.pt")

चरण 4: मूल्यांकन

We start from recreating the model and loading the state from the previously saved file. You create a QNN layer using another simulator or a real hardware. So, you can train a model on real hardware available on the cloud and then for inference use a simulator or vice verse. For a sake of simplicity we create a new quantum neural network in the same way as above.

qnn5 = create_qnn()
model5 = Net(qnn5)
model5.load_state_dict(torch.load("model4.pt"))

<All keys matched successfully>

model5.eval()  # set model to evaluation mode
with no_grad():

    correct = 0
    for batch_idx, (data, target) in enumerate(test_loader):
        output = model5(data)
        if len(output.shape) == 1:
            output = output.reshape(1, *output.shape)

        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) / batch_size * 100
        )
    )

Performance on test data:
        Loss: -3.3585
        Accuracy: 100.0%

# Plot predicted labels

n_samples_show = 6
count = 0
fig, axes = plt.subplots(nrows=1, ncols=n_samples_show, figsize=(10, 3))

model5.eval()
with no_grad():
    for batch_idx, (data, target) in enumerate(test_loader):
        if count == n_samples_show:
            break
        output = model5(data[0:1])
        if len(output.shape) == 1:
            output = output.reshape(1, *output.shape)

        pred = output.argmax(dim=1, keepdim=True)

        axes[count].imshow(data[0].numpy().squeeze(), cmap="gray")

        axes[count].set_xticks([])
        axes[count].set_yticks([])
        axes[count].set_title("Predicted {}".format(pred.item()))

        count += 1

🎉🎉🎉🎉 अब आप Qiskit Machine Learning का उपयोग करके अपने स्वयं के हाइब्रिड डेटासेट और आर्किटेक्चर के साथ प्रयोग करने में सक्षम हैं। सौभाग्य!

import qiskit.tools.jupyter

%qiskit_version_table
%qiskit_copyright

Version Information

Qiskit Software	Version
qiskit-terra	0.22.0
qiskit-aer	0.11.1
qiskit-ignis	0.7.0
qiskit	0.33.0
qiskit-machine-learning	0.5.0
System information
Python version	3.7.9
Python compiler	MSC v.1916 64 bit (AMD64)
Python build	default, Aug 31 2020 17:10:11
OS	Windows
CPUs	4
Memory (Gb)	31.837730407714844
Thu Nov 03 09:57:38 2022 GMT Standard Time

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