  Here is the Python code that performs the tasks you specified using the nni library:
```
import nni

# Fine-tune a ResNet18 model on the Cifar10 dataset for 30 epochs and evaluate its accuracy
nni.fine_tune(model='ResNet18', dataset='Cifar10', epochs=30, evaluate_accuracy=True)

# Create a teacher model by duplicating the fine-tuned model
teacher_model = nni.duplicate_model(model='ResNet18')

# Create a pruner using the TaylorPruner and AGPPruner classes
pruner = nni.Pruner(model=teacher_model, config_list=[{'op_types': ['Conv2d'], 'sparse_ratio': 0.5}])

# Create a quantizer using the QATQuantizer class
quantizer = nni.Quantizer(model=teacher_model, config_list=[{'op_types': ['Conv2d', 'BatchNorm2d'], 'quant_bits': 8}])

# Create a distiller using the DynamicLayerwiseDistiller class
distiller = nni.Distiller(model=teacher_model, config_list=[{'op_types': ['Conv2d'], 'method': 'mean_squared_error'}])

# Compress the model using the distiller for 60 iterations of 100 steps each
for i in range(60):
    distiller.compress(steps=100)

# Speeds up the model using the ModelSpeedup class and the masks from the pruner
speedup = nni.ModelSpeedup(model=teacher_model, masks=pruner.masks)

# Evaluate the accuracy of the compressed model
accuracy = nni.evaluate_accuracy(model=speedup.model, dataset='Cifar10')
print(f'Compressed model accuracy: {accuracy}')

# Simulate quantization by updating the calibration configuration of the model using the Quantizer class
quantizer.calibration_config = {'op_types': ['Conv2d', 'BatchNorm2d'], 'quant_bits': 8}

# Evaluate the accuracy of the compressed and quantized model
accuracy = nni.evaluate_accuracy(model=quantizer.model, dataset='Cifar10')
print(f'Compressed and quantized model accuracy: {accuracy}')
```