> ## Documentation Index
> Fetch the complete documentation index at: https://prod-mint.classiq.io/llms.txt
> Use this file to discover all available pages before exploring further.
# Variational Data Encoding
Open this notebook in GitHub to run it yourself
Encoding classical data on quantum states is an important subroutine in variational quantum circuits, such as Quantum Singular Vector Machine (QSVM) and Quantum Neural Networks (QNN).
## Encode in angle
This function encodes $n$ data points on $n$ qubits, mapping the data point $x_i$ to a RY rotation on the $i$-th qubit with a $\pi x_i$ angle.
Function: `encode_in_angle`
Arguments:
* `data`: `CArray[Creal]`
* `qba`: `Output[QArray[QBit]]`
The `qba` quantum argument is the quantum state on which we encode the classical array `data`.
## Example
```python theme={null}
from classiq import *
@qfunc
def main(data: CArray[CReal, 4], x: Output[QArray[QBit]]):
encode_in_angle(data, x)
qmod = create_model(main)
```
```python theme={null}
from classiq import synthesize
qprog = synthesize(qmod)
```
## Encode on Bloch
This function encodes $n$ data points on $\lceil n/2 \rceil$, mapping pairs of data points $(x_{2i}, x_{2i+1})$ to the bloch sphere via RX rotation with an angle $\pi x_{2i}$ followed by a RZ rotation with an angle $\pi x_{2i+1}$. If the number of data points is odd then a single RX rotation is applied to the last qubit, with an angle of $2\pi x_n$.
Function: `encode_on_bloch`
Arguments:
* `data`: `CArray[Creal]`
* `qba`: `Output[QArray[QBit]]`
The `qba` quantum argument is the quantum state on which we encode the classical array `data`.
## Example
```python theme={null}
from classiq import *
@qfunc
def main(data: CArray[CReal, 7], x: Output[QArray[QBit]]):
encode_on_bloch(data, x)
qmod = create_model(main)
```
```python theme={null}
from classiq import synthesize
qprog = synthesize(qmod)
```
