> For the complete documentation index, see [llms.txt](https://quantum-computing.gitbook.io/introduction-to-quantum-computing/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://quantum-computing.gitbook.io/introduction-to-quantum-computing/quantum-machine-learning/project-theme/documentation.md).

# Documentation

naturally, a good way to start ... remember 80 % documentation and engineering and 20% coding

### More details

In the following sections you can learn more about the key features of PennyLane:

![../\_images/code.png](https://pennylane.readthedocs.io/en/stable/_images/code.png)

1. [Quantum circuits](https://pennylane.readthedocs.io/en/stable/introduction/circuits.html) shows how PennyLane unifies and simplifies the process of programming quantum circuits with trainable parameters.
2. [Gradients and training](https://pennylane.readthedocs.io/en/stable/introduction/interfaces.html) introduces how PennyLane is used with different optimization libraries to optimize quantum circuits or hybrid computations.
3. [Quantum operations](https://pennylane.readthedocs.io/en/stable/introduction/operations.html) outlines the various quantum circuit building blocks provided in PennyLane.
4. [Measurements](https://pennylane.readthedocs.io/en/stable/introduction/measurements.html) presents the different options available to measure the output of quantum circuits.
5. [Templates](https://pennylane.readthedocs.io/en/stable/introduction/templates.html) gives an overview of different larger-scale composable layers for building quantum algorithms.
6. [Optimizers](https://pennylane.readthedocs.io/en/stable/introduction/optimizers.html) details the built-in tools for optimizing and training quantum computing and quantum machine learning circuits.
7. [Configuration](https://pennylane.readthedocs.io/en/stable/introduction/configuration.html) provides details about how to customize PennyLane and provide credentials for quantum hardware access.

## more developer focus ....

qml.state : <https://pennylane.readthedocs.io/en/stable/code/api/pennylane.state.html>

qml.kernels: <https://pennylane.readthedocs.io/en/stable/code/qml_kernels.html>

## Gradients and training ( @Variational circuits)

Step 1: <https://pennylane.readthedocs.io/en/stable/introduction/interfaces.html>

Step 2:<https://pennylane.readthedocs.io/en/stable/introduction/interfaces.html>

For Variational circuits: <https://pennylane.ai/qml/glossary/variational_circuit.html>