 Here is the Python code using the nni library to perform the tasks as instructed:

```python
import torch
import torchvision
import torchvision.transforms as transforms
from nni.optimizer import NNIOptimizer
from nni.nas.pytorch import PyTorchParamSearch, PyTorchMutator
from nni.nas.pytorch.mutation import TaylorPruner, AGPPruner, QATQuantizer, DynamicLayerwiseDistiller, ModelSpeedup
from nni.nas.pytorch.utils import _get_layer_and_inputs_from_tuner

# Load Cifar10 dataset
transform = transforms.Compose([
    transforms.RandomHorizontalFlip(),
    transforms.RandomCrop(32, padding=4),
    transforms.ToTensor(),
    transforms.Normalize(torch.tensor([0.5, 0.5, 0.5]), torch.tensor([0.5, 0.5, 0.5]))
])

train_dataset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
test_dataset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)

train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=128, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=128, shuffle=False)

# Define ResNet18 model
model = torchvision.models.resnet18(pretrained=False)

# Fine-tune the model
optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9)
criterion = torch.nn.CrossEntropyLoss()

def train(epoch):
    model.train()
    running_loss = 0.0
    for i, data in enumerate(train_loader, 0):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
    print(f'Epoch {epoch + 1}, Loss: {running_loss / (i + 1)}')

def test(epoch):
    model.eval()
    correct = 0
    total = 0
    with torch.no_grad():
        for data in test_loader:
            inputs, labels = data
            outputs = model(inputs)
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()
    print(f'Accuracy of the model on the 10000 test images: {100 * correct / total}%')

# Fine-tune the model for 30 epochs
for epoch in range(30):
    train(epoch)
    test(epoch)

# Create teacher model
teacher_model = model.clone()

# Create pruner
pruner = TaylorPruner(model, target_ops=['Conv2d'], sparse_ratio=0.5)
pruner_agp = AGPPruner(model, target_ops=['Conv2d'], sparse_ratio=0.5)

# Configure pruner and quantizer
pruner_config = {'training_steps': 100, 'total_times': 30}
quantizer_config = {'training_steps_start': 100, 'target_ops': ['Conv2d', 'BatchNorm2d'], 'quant_type': 'int8'}

# Create pruner and quantizer
pruner_task = pruner.apply_and_evaluate(pruner_config, train, test)
quantizer = QATQuantizer(model, **quantizer_config)

# Create distiller
distiller = DynamicLayerwiseDistiller(teacher_model, model, target_ops=['Conv2d'], loss_type='mse')

# Compress the model
for _ in range(60):
    distiller.compress(train, train_loader, **pruner_config)

# Speed up the model
speedup = ModelSpeedup(model, pruner.masks)

# Evaluate the accuracy of the compressed model
test(30)

# Simulate quantization
quantizer.update_calibration()

# Evaluate the accuracy of the compressed and quantized model
test(30)
```

Please note that you need to install the nni library and its dependencies before running the code. You can install it using pip:

```
pip install nni
```

Also, make sure to have PyTorch and torchvision installed:

```
pip install torch torchvision
```