# Simulation

During development, the speed of homomorphic execution can be a blocker for fast prototyping. You could call the function you're trying to compile directly, of course, but it won't be exactly the same as FHE execution, which has a certain probability of error (see [Exactness](../core-features/table\_lookups.md#table-lookup-exactness)).

To overcome this issue, simulation is introduced:

```python
from concrete import fhe
import numpy as np

@fhe.compiler({"x": "encrypted"})
def f(x):
    return (x + 1) ** 2

inputset = [np.random.randint(0, 10, size=(10,)) for _ in range(10)]
circuit = f.compile(inputset, p_error=0.1, fhe_simulation=True)

sample = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

actual = f(sample)
simulation = circuit.simulate(sample)

print(actual.tolist())
print(simulation.tolist())
```

After the simulation runs, it prints the following:

```
[1, 4, 9, 16, 25, 36, 49, 64, 81, 100]
[1, 4, 9, 16, 16, 36, 49, 64, 81, 100]
```

{% hint style="warning" %}
There are some operations which are not supported in simulation yet. They will result in compilation failures. You can revert to simulation using graph execution using `circuit.graph(...)` instead of `circuit.simulate(...)`, which won't simulate FHE, but it will evaluate the computation graph, which is like simulating the operations without any errors due to FHE.
{% endhint %}
