Fix conflicts.

docs/tutorials/getting_started.md

@@ -100,13 +100,8 @@ each sub-block and results in a `AddBlock` type which can be used to construct P
 Please note that `AddBlock` can give rise to non-unitary computations that might not be supported by all backends.
 
 ??? note "Get the matrix of a block"
-<<<<<<< HEAD
     It is always possible to retrieve the matrix representation of a block by calling the `block.tensor()` method.
 	Please note that the returned tensor contains a batch dimension for the purposes of block parametrization.
-=======
-    It is always possible to retrieve the matrix representation of a block. Please note that the returned tensor
-    contains a batch dimension for the purposes of block parametrization.
->>>>>>> main
 
     ```python exec="on" source="material-block" result="json" session="i-xx"
     print(f"X(0) * X(0) tensor = {chain_x.tensor()}") # markdown-exec: hide
@@ -207,52 +202,3 @@ print(f"{xs = }") # markdown-exec: hide
 ```
 
 For more details on `QuantumModel`, see [here](/tutorials/quantummodels).
-<<<<<<< HEAD
-=======
-
-## State initialization
-
-Qadence offers convenience routines for preparing initial quantum states.
-These routines are divided into two approaches:
-
-- As a dense matrix.
-- From a suitable quantum circuit. This is available for every backend and it should be added
-in front of the desired quantum circuit to simulate.
-
-Let's illustrate the usage of the state preparation routine. For more details,
-please refer to the [API reference](/qadence/index).
-
-```python exec="on" source="material-block" result="json" session="seralize"
-from qadence import random_state, product_state, is_normalized, StateGeneratorType
-
-# Random initial state.
-# the default `type` is StateGeneratorType.HaarMeasureFast
-state = random_state(n_qubits=2, type=StateGeneratorType.RANDOM_ROTATIONS)
-print(f"{Random initial state generated with rotations:\n {state.detach().numpy().flatten()}}") # markdown-exec: hide
-
-# Check the normalization.
-assert is_normalized(state)
-
-# Product state from a given bitstring.
-# NB: Qadence follows the big endian convention.
-state = product_state("01")
-print(f"{Product state corresponding to bitstring '10':\n {state.detach().numpy().flatten()}}") # markdown-exec: hide
-```
-
-Now we see how to generate the product state corresponding to the one above with
-a suitable quantum circuit.
-
-```python
-from qadence import product_block, tag, QuantumCircuit
-
-state_prep_b = product_block("10")
-display(state_prep_b)
-
-# let's now prepare a circuit
-state_prep_b = product_block("1000")
-tag(state_prep_b, "prep")
-qc_with_state_prep = QuantumCircuit(4, state_prep_b, fourier_b, hea_b)
-
-print(html_string(qc_with_state_prep), size="4,4")) # markdown-exec: hide
-```
->>>>>>> main