```python
import torch
import torchvision
import torchvision.transforms as transforms
import nni
from nni.compression.pytorch import ModelSpeedup, apply_compression_results
from nni.compression.pytorch.pruning import TaylorFOWeightFilterPruner, AGPPruner
from nni.compression.pytorch.quantization import QAT_Quantizer
from nni.compression.pytorch.distillation import DynamicLayerwiseDistiller
from nni.algorithms.compression.v2.pytorch.utils.quantization import update_ema
from torchvision.models import resnet18

# Load and normalize CIFAR10
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4, shuffle=True, num_workers=2)
testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=4, shuffle=False, num_workers=2)
classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')

# Define the network
net = resnet18(pretrained=True)
net.fc = torch.nn.Linear(net.fc.in_features, 10)  # Adjust for CIFAR10

# Fine-tune the model
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
for epoch in range(30):  # loop over the dataset multiple times
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

# Duplicate the model for the teacher
teacher_model = type(net)(pretrained=False)  # Create a new instance of the same class
teacher_model.load_state_dict(net.state_dict())

# Pruning configuration
pruning_config_list = [{
    'sparsity': 0.5,
    'op_types': ['Conv2d'],
}]

# Create pruners
taylor_pruner = TaylorFOWeightFilterPruner(net, pruning_config_list, trainer=trainloader, criterion=criterion, training_epochs=100)
agp_pruner = AGPPruner(net, pruning_config_list, pruning_algorithm='l1', num_iterations=30, epochs_per_iteration=100)

# Quantization configuration
quantization_config_list = [{
    'quant_types': ['weight', 'input', 'output'],
    'quant_bits': {'weight': 8, 'input': 8, 'output': 8},
    'op_types': ['Conv2d', 'BatchNorm2d'],
}]

# Create quantizer
quantizer = QAT_Quantizer(net, quantization_config_list, start_step=100)

# Distillation configuration
distillation_config_list = [{
    'op_types': ['Conv2d'],
    'distill_method': 'mse',
}]

# Create distiller
distiller = DynamicLayerwiseDistiller(net, teacher_model, distillation_config_list, trainloader, optimizer, criterion, num_iterations=60, epochs_per_iteration=100)

# Compress the model
distiller.compress()

# Speed up the model
ModelSpeedup(net, inputs, masks_file=None).speedup_model()

# Evaluate the accuracy of the compressed model
# (Assuming the existence of a function `evaluate_accuracy` that you should implement based on your requirements)

# Simulate quantization
update_ema(net, trainloader, num_steps=100)

# Evaluate the accuracy of the compressed and quantized model
# (Again, assuming the existence of a function `evaluate_accuracy`)
```