  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
model = nni.models.ResNet18(num_classes=10)
trainer = nni.trainers.Trainer(model, nni.datasets.Cifar10())
trainer.train(30)
accuracy = trainer.evaluate()
print(f"Accuracy: {accuracy}")

# Create a teacher model by duplicating the fine-tuned model
teacher_model = nni.models.ResNet18(num_classes=10)
teacher_model.load_state_dict(model.state_dict())

# Create a pruner using the TaylorPruner and AGPPruner classes
pruner = nni.pruners.TaylorPruner(teacher_model, nni.datasets.Cifar10())
pruner.set_sparse_ratio(0.5)
pruner.set_num_iterations(100)
pruner.set_num_trials(30)

# Create a quantizer using the QATQuantizer class
quantizer = nni.quantizers.QATQuantizer(teacher_model, nni.datasets.Cifar10())
quantizer.set_quantization_method(nni.quantizers.QuantizationMethod.INT8)
quantizer.set_start_step(100)

# Create a distiller using the DynamicLayerwiseDistiller class
distiller = nni.distillers.DynamicLayerwiseDistiller(teacher_model, nni.datasets.Cifar10())
distiller.set_distillation_method(nni.distillers.DistillationMethod.MEAN_SQUARED_ERROR)
distiller.set_num_iterations(100)

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

# Speeds up the model using the ModelSpeedup class and the masks from the pruner
speedup = nni.speedup.ModelSpeedup(teacher_model, pruner.get_masks())
speedup.train(100)
speedup.evaluate()

# Evaluate the accuracy of the compressed model
accuracy = speedup.evaluate()
print(f"Compressed accuracy: {accuracy}")

# Simulate quantization by updating the calibration configuration of the model using the Quantizer class
quantizer.set_calibration_method(nni.quantizers.CalibrationMethod.UNIFORM)
quantizer.set_calibration_range(0, 255)
quantizer.set_calibration_num_samples(1000)

# Evaluate the accuracy of the compressed and quantized model
accuracy = quantizer.evaluate()
print(f"Compressed and quantized accuracy: {accuracy}")
```
Note that this code assumes that you have already installed the nni library and have the necessary dependencies installed. Additionally, this code assumes that you have already fine-tuned a ResNet18 model on the Cifar10 dataset and have the fine-tuned model available.