### Installation Guide Updates

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/releases/changelog-0.43.0.md

The installation page has been updated with currently supported Python versions and installation instructions.

```markdown
installation page </development/guide/installation>
```

--------------------------------

### Installing and Using lightning.qubit

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/releases/changelog-0.11.0.md

Shows how to install the `pennylane-lightning` plugin and use the `lightning.qubit` device for state-vector simulation.

```console
$ pip install pennylane-lightning
```

```pycon
>>> dev = qml.device("lightning.qubit", wires=2)
```

--------------------------------

### Quantum Tape Recording Example

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/releases/changelog-0.15.0.md

Demonstrates how to get the currently active quantum tape and temporarily stop its recording using a context manager. Operations recorded outside `stop_recording` are included in the final tape.

```python
with qml.tape.QuantumTape():
    qml.RX(0, wires=0)
    current_tape = qml.tape.get_active_tape()
    with current_tape.stop_recording():
        qml.RY(1.0, wires=1)
    qml.RZ(2, wires=1)
current_tape.operations
```

--------------------------------

### Install Documentation Dependencies

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/development/guide/documentation.rst

Install the required packages for building the documentation using pip.

```console
pip install --group docs
```

--------------------------------

### Add code example to ApproxTimeEvolution.compute_decomposition

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/releases/changelog-0.32.0.md

A code example has been added to `qml.ApproxTimeEvolution.compute_decomposition()`.

```python
qml.ApproxTimeEvolution.compute_decomposition()
```

--------------------------------

### Device Initialization with Wires

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/development/plugins.rst

Example of initializing a default qubit device with a specified number of wires.

```python
>>> dev = qp.device('default.qubit', wires=1)
```

--------------------------------

### Install JAX and JAXlib

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/introduction/interfaces/jax.rst

Install the necessary JAX libraries for PennyLane integration. Ensure you use the specified versions.

```bash
pip install jax==0.7.1 jaxlib==0.7.1
```

--------------------------------

### Install Catalyst Compiler

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/introduction/compiling_workflows.rst

Install the Catalyst hybrid compiler using pip. This is required for using the `@qjit` decorator with Catalyst.

```console
pip install pennylane-catalyst
```

--------------------------------

### Qutrit Circuit Example

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/releases/changelog-0.26.0.md

Demonstrates the initialization of a `default.qutrit` device and the definition of a custom unitary operation for a qutrit circuit.

```python
dev = qml.device("default.qutrit", wires=1)

U = np.array([
        [1, 1, 1],
        [1, 1, 1],
        [1, 1, 1]
    ]

```

--------------------------------

### Example TOML Configuration File

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/introduction/configuration.rst

An example of a TOML configuration file demonstrating how to set global PennyLane options, plugin-specific global options, and device-specific options. This file structure allows for granular control over various settings.

```toml
[main]
# Global PennyLane options.
# Affects every loaded plugin if applicable.
shots = 1000

[strawberryfields.global]
# Options for the Strawberry Fields plugin
# For more details, see the PennyLane-SF documentation:
# https://pennylane-sf.readthedocs.io
hbar = 2
shots = 100

    [strawberryfields.fock]
    # Options for the strawberryfields.fock device
    cutoff_dim = 10
    hbar = 2

    [strawberryfields.gaussian]
    # Indentation doesn't matter in TOML files,
    # but helps provide clarity.

[qiskit.global]
# Global options for the Qiskit plugin.
# For more details, see the PennyLane-Qiskit documentation:
# https://pennylaneqiskit.readthedocs.io/en/latest/index.html

backend = "qasm_simulator"

    [qiskit.aer]
    # Default options for Qiskit Aer

    # set the default backend options for the Qiskit Aer device
    # Note that, in TOML, dictionary key-value pairs are defined
    # using '=' rather than ':'.
    backend_options = {"validation_threshold" = 1e-6}

    [qiskit.ibmq]
    # Default options for IBMQ

    # IBM Quantum Experience authentication token
    ibmqx_token = "XXX"

    # hardware backend device
    backend = "ibmq_rome"

    # pass (optional) provider information
    hub = "MYHUB"
    group = "MYGROUP"
    project = "MYPROJECT"

```

--------------------------------

### Updated qml.prod Docstring Example

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/releases/changelog-0.41.0.md

The docstring for `qml.prod` has been updated to explain that the order of the output may seem reversed, but it is correct. This example demonstrates the expected output.

```python
import pennylane as qml
from pennylane import numpy as np

# Example demonstrating the output order of qml.prod

@qml.qnode(qml.device('default.qubit'))
def circuit():
    qml.PauliX(wires=0)
    qml.PauliY(wires=1)
    return qml.expval(qml.prod(qml.PauliZ(0), qml.PauliZ(1)))

print(circuit())

# The output is the expectation value of PauliZ(0) @ PauliZ(1).
# The order in the qml.prod function does not affect the resulting operator.
# The docstring clarifies that the internal representation might appear reversed
# but the mathematical operation is correct.
```

--------------------------------

### Install OpenFermion-PySCF

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/code/qp_qchem.rst

Installs the OpenFermion-PySCF package, which is required for using the OpenFermion-PySCF backend with PennyLane's quantum chemistry module.

```bash
pip install openfermionpyscf
```

--------------------------------

### Custom Device Initialization Example

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/development/legacy_plugins.rst

Example of a custom QubitDevice subclass overriding __init__ to set fixed wires, a default shot number, and accept custom hardware options. This is useful for devices with specific hardware constraints or configurations.

```python
class CustomDevice(QubitDevice):
    name = 'My custom device'
    short_name = 'example.mydevice'
    pennylane_requires = '0.1.0'
    version = '0.0.1'
    author = 'Ada Lovelace'

    operations = {"PauliX", "RX", "CNOT"}
    observables = {"PauliZ", "PauliX", "PauliY"}

    def __init__(self, shots=1024, hardware_options=None):
        super().__init__(wires=24, shots=shots)
        self.hardware_options = hardware_options or hardware_defaults
```

--------------------------------

### Install Catalyst and CUDA Quantum

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/introduction/compiling_workflows.rst

Install both the Catalyst and CUDA Quantum compilers. This is a prerequisite for using the `@qjit` decorator with the `compiler="cuda_quantum"` option.

```bash
pip install pennylane-catalyst cuda_quantum
```

--------------------------------

### Example Package Autosummary in __init__.py

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/development/guide/documentation.rst

An example of an autosummary table to be included at the bottom of a package's __init__.py docstring. It lists all modules within the package.

```rest
..
    ~pennylane.package_name.module_name1
    ~pennylane.package_name.module_name2
```

--------------------------------

### Print GQSP Circuit Matrix (Example Output)

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/releases/changelog-0.40.0.md

Example output showing the first 2x2 submatrix of the calculated matrix for the GQSP circuit. The output is rounded to 3 decimal places.

```python
print(np.round(matrix,3)[:2, :2])
```

--------------------------------

### Run PennyLane Device Integration Tests

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/releases/changelog-0.11.0.md

Example of invoking the PennyLane device test suite from the command line.

```console
$ pl-device-test --device=default.qubit --shots=1234 --analytic=False
```

--------------------------------

### Basic Preprocessing Pipeline Setup

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/development/plugins.rst

Demonstrates the initial steps of setting up an execution configuration and a compile pipeline for processing quantum scripts.

```python
execution_config = dev.setup_execution_config(initial_config)
compile_pipeline = dev.preprocess_transforms(execution_config)
batch, fn = compile_pipeline(initial_batch)
fn(dev.execute(batch, execution_config))
```

--------------------------------

### Build HTML Documentation

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/development/guide/documentation.rst

Navigate to the top-level directory and run the make command to build the HTML documentation.

```bash
make docs
```

--------------------------------

### Get All Possible Measurement Counts

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/introduction/measurements.rst

This example demonstrates how to retrieve all possible measurement outcomes, including those with zero counts, by setting `all_outcomes=True` in the `qp.counts()` function.

```python
dev = qp.device("default.qubit", wires=2)

@qp.set_shots(shots=1000)
@qp.qnode(dev)
def circuit():
    qp.Hadamard(wires=0)
    qp.CNOT(wires=[0, 1])
    return qp.counts(all_outcomes=True)
```

--------------------------------

### Using the QNSPSA Optimizer

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/releases/changelog-0.26.0.md

Illustrates the basic usage pattern for the QNSPSAOptimizer, showing how to initialize it and perform optimization steps.

```python
max_iterations = 50
opt = qml.QNSPSAOptimizer() 

for _ in range(max_iterations):
    params, cost = opt.step_and_cost(cost, params)
```

--------------------------------

### BBQRAM Resource Estimation

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/releases/changelog-0.44.0.md

Example of using the lightweight `qml.estimator.BBQRAM` for fast resource estimation within a circuit definition. The `estimate` function is then called to get the resource breakdown.

```python
import pennylane.estimator as qre

def circuit():
    qre.CNOT()
    qre.QFT(num_wires=4)
    qre.BBQRAM(num_bitstrings=30, size_bitstring=8, num_wires=100)
    qre.Hadamard()
```

--------------------------------

### Quantum Chemistry Operations with Broadcasting

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/releases/changelog-0.25.0.md

Quantum chemistry operations now support parameter broadcasting. This example shows how to get the matrix shape for a `SingleExcitation` operation with broadcasted parameters.

```python
op = qml.SingleExcitation(np.array([0.3, 1.2, -0.7]), wires=[0, 1])
op.matrix().shape
```

--------------------------------

### Applying operator transforms to circuits

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/releases/changelog-0.22.0.md

Operator transforms can act on multiple operations by passing quantum functions or tapes. This example shows how to get the matrix representation of a circuit.

```python
def circuit(theta):
    qml.RX(theta, wires=1)
    qml.PauliZ(wires=0)
qml.matrix(circuit)(np.pi / 4)
```

--------------------------------

### Mottonen State Preparation Example

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/releases/changelog-0.15.0.md

Demonstrates the usage of MottonenStatePreparation for preparing a quantum state. Previously returned a different drawing of the circuit.

```python
inputstate = [np.sqrt(0.2), np.sqrt(0.3), np.sqrt(0.4), np.sqrt(0.1)]

@qml.qnode(dev)
def circuit():
    mottonen.MottonenStatePreparation(inputstate,wires=[0, 1])
    return qml.expval(qml.PauliZ(0))
```

```python
print(qml.draw(circuit)())
```

```python
print(qml.draw(circuit)())
```

--------------------------------

### Initialize lightning.qubit device and circuit

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/releases/changelog-0.18.0.md

Demonstrates how to initialize the lightning.qubit device and define a QNode using the adjoint differentiation method. Ensure the lightning-qubit plugin is installed.

```python
import pennylane as qml

wires = 3
layers = 2
dev = qml.device("lightning.qubit", wires=wires)

@qml.qnode(dev, diff_method="adjoint")
def circuit(weights):
    qml.templates.StronglyEntanglingLayers(weights, wires=range(wires))
    return qml.expval(qml.PauliZ(0))

weights = qml.init.strong_ent_layers_normal(layers, wires, seed=1967)
```

--------------------------------

### Install Required Packages for .data Module

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/introduction/data.rst

Installs the necessary packages for using the pennylane.data module. Ensure these are installed before proceeding.

```console
pip install aiohttp fsspec h5py
```

--------------------------------

### Updated qml.qchem.VibrationalPES Docstring Example

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/releases/changelog-0.41.0.md

The docstrings for `qml.qchem.localize_normal_modes` and `qml.qchem.VibrationalPES` have been updated to include examples that can be copied. This example shows `VibrationalPES`.

```python
import pennylane as qml
from pennylane import numpy as np

# Example for qml.qchem.VibrationalPES

# Define a potential energy surface (PES) function
def pes_function(coords):
    x, y = coords
    return x**2 + y**2

# Create a VibrationalPES object
vibrational_pes = qml.VibrationalPES(pes_function)

# Evaluate the PES at a specific coordinate
coords = np.array([0.5, -0.2])
energy = vibrational_pes.evaluate(coords)

print(f"Coordinates: {coords}")
print(f"Potential Energy: {energy}")

# This class provides a convenient way to handle and evaluate potential energy surfaces for vibrational analysis.
```

--------------------------------

### Creating and Using a QNode with JAX

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/development/guide/architecture.rst

Illustrates the creation of a QNode using a quantum function and a device, with the JAX interface, and its subsequent execution and drawing.

```python
import jax
from jax import numpy as jnp
params = jnp.array([0.5, 0.2])
```

```python
qnode = qp.QNode(qfunc, device, interface='jax')
qnode(params)
```

```python
qnode_drawer = qp.draw(qnode)
print(qnode_drawer(params))
```

--------------------------------

### Updated qml.qchem.localize_normal_modes Docstring Example

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/releases/changelog-0.41.0.md

The docstrings for `qml.qchem.localize_normal_modes` and `qml.qchem.VibrationalPES` have been updated to include examples that can be copied. This example shows `localize_normal_modes`.

```python
import pennylane as qml
from pennylane import numpy as np

# Example for qml.qchem.localize_normal_modes

# Define a Hessian matrix (example for a 2-mode system)
Hessian = np.array([
    [1.0, 0.1],
    [0.1, 1.5]
])

# Localize the normal modes
localized_modes = qml.localize_normal_modes(Hessian)

print("Original Hessian:\n", Hessian)
print("Localized modes:\n", localized_modes)

# This function helps in transforming normal modes to localized ones, useful in quantum chemistry simulations.
```

--------------------------------

### Executing a Tape on a Device

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/development/guide/architecture.rst

Shows how to instantiate a device and execute a quantum tape using the batch_execute method.

```python
device = qp.device("default.qubit", wires=['a', 'b'], shots=None)
device.batch_execute([tape])
```

--------------------------------

### Install Pre-commit Hook

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/development/guide/pullrequests.rst

Install the 'pre-commit' package, which automates running 'black', 'isort', and 'pylint' as a git pre-commit hook. This command should be run in the top-level folder of the PennyLane repository after installing the package.

```bash
pre-commit install
```

--------------------------------

### Import OpenQASM 3.0 Circuit

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/releases/changelog-0.42.0.md

Demonstrates importing an OpenQASM 3.0 program into a PennyLane QNode. Ensure 'openqasm3' and 'openqasm3[parser]' are installed.

```python
import pennylane as qml

dev = qml.device("default.qubit", wires=[0, 1, 2])

@qml.qnode(dev)
def my_circuit():
    qml.from_qasm3(
        """
        qubit q0; 
        qubit q1;
        qubit q2;

        float theta = 0.2;
        int power = 2;

        ry(theta / 2) q0; 
        rx(theta) q1;
        pow(power) @ x q0;

        def random(qubit q) -> bit {
          bit b = "0";
          h q;
          measure q -> b;
          return b;
        }

        bit m = random(q2);

        if (m) {
          int i = 0;
          while (i < 5) {
            i = i + 1;
            rz(i) q1;
            break;
          }
        }
        ",
        {'q0': 0, 'q1': 1, 'q2': 2},
    )()
    return qml.expval(qml.Z(0))

```

--------------------------------

### SemiAdder Template Example

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/releases/changelog-0.42.0.md

Demonstrates the usage of the SemiAdder template for performing addition on a quantum computer. It shows how to initialize registers and apply the SemiAdder to add two integers.

```python
from functools import partial

x = 3
y = 4

wires = qml.registers({"x": 3, "y": 6, "work": 5})

dev = qml.device("default.qubit")

@partial(qml.set_shots, shots=1)
@qml.qnode(dev)
def circuit():
    qml.BasisEmbedding(x, wires=wires["x"])
    qml.BasisEmbedding(y, wires=wires["y"])
    qml.SemiAdder(wires["x"], wires["y"], wires["work"])
    return qml.sample(wires=wires["y"])
```

--------------------------------

### Corrected Lie Closure Dense Code Example

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/releases/changelog-0.41.0.md

A typo has been fixed in the code example for `qml.labs.dla.lie_closure_dense`.

```python
import pennylane as qml
from pennylane import numpy as np

# Example of lie_closure_dense

# Define a list of matrices
matrices = [
    np.array([[0, 1], [1, 0]]),
    np.array([[0, -1j], [1j, 0]])
]

# Compute the Lie closure
closure = qml.lie_closure_dense(matrices)

print(closure)
```

--------------------------------

### Optimizing QNode with Optax Adam

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/introduction/interfaces/jax.rst

This example shows how to optimize a QNode using `jax.jit` and the `adam` optimizer from the Optax library. PennyLane's optimizers are not compatible with the JAX interface.

```python
import pennylane as qp
from jax import numpy as jnp
import jax
import optax

learning_rate = 0.15

dev = qp.device("default.qubit", wires=1)

@jax.jit
@qp.set_shots(shots=None)
@qp.qnode(dev, interface="jax")
def energy(a):
    qp.RX(a, wires=0)
    return qp.expval(qp.PauliZ(0))

optimizer = optax.adam(learning_rate)

params = jnp.array(0.5)
opt_state = optimizer.init(params)

for _ in range(200):
    grads = jax.grad(energy)(params)
    updates, opt_state = optimizer.update(grads, opt_state)
    params = optax.apply_updates(params, updates)

>>> params
Array(3.14159111, dtype=float64)
```

--------------------------------

### Initialize Device by Short Name

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/development/legacy_plugins.rst

Shows how to initialize a PennyLane device using its unique `short_name` string, which is discovered via setuptools entry points.

```python
import pennylane as qp
dev1 = qp.device(short_name, wires=2)
```

--------------------------------

### Install PennyLane with Pip

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/index.rst

Install PennyLane and its dependencies using pip. Ensure you have Python version 3.11 or above.

```bash
python -m pip install pennylane
```

--------------------------------

### Corrected BasisRotation Code Example

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/releases/changelog-0.41.0.md

The code example for `qml.BasisRotation` has been corrected to include `wire_order` in the call to `qml.matrix`.

```python
import pennylane as qml
from pennylane import numpy as np

# Example with wire_order
matrix = np.array([[1, 0], [0, 1]])

@qml.qnode(qml.device('default.qubit'))
def circuit():
    qml.BasisRotation(matrix, wires=[0, 1], wire_order=[0, 1])
    return qml.expval(qml.PauliZ(0))

print(circuit())

# Example without wire_order (original issue)
# @qml.qnode(qml.device('default.qubit'))
# def circuit_no_wire_order():
#     qml.BasisRotation(matrix, wires=[0, 1])
#     return qml.expval(qml.PauliZ(0))

# print(circuit_no_wire_order())
```

--------------------------------

### Basic QNode Execution with Auto Interface

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/releases/changelog-0.29.0.md

Example of defining and executing a QNode with automatic interface detection. Imports `jax` and `jax.numpy`.

```python
import jax
import jax.numpy as jnp

qml.enable_return()
a = jnp.array(0.1)
b = jnp.array(0.2)

dev = qml.device("default.qubit", wires=2)

@qml.qnode(dev)
def circuit(a, b):
    qml.RY(a, wires=0)
    qml.RX(b, wires=1)
    qml.CNOT(wires=[0, 1])
    return qml.expval(qml.PauliZ(0)), qml.expval(qml.PauliY(1))
```

--------------------------------

### Register Plugin Devices with Setuptools

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/development/legacy_plugins.rst

Configures the `setup.py` file to register custom PennyLane devices using setuptools `entry_points`, enabling PennyLane to discover and load them.

```python
devices_list = [
        'example.mydevice1 = MyModule.MySubModule:MyDevice1',
        'example.mydevice2 = MyModule.MySubModule:MyDevice2'
    ],
setup(entry_points={'pennylane.plugins': devices_list})
```

--------------------------------

### Prepare State in Circuit with qml.StatePrep

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/releases/changelog-0.32.0.md

Implement a quantum state vector in a circuit using `qml.StatePrep`. This example prepares the state obtained from `qml.qchem.import_state`.

```pycon
>>> dev = qml.device('default.qubit', wires=4)
>>> @qml.qnode(dev)
... def circuit():
...     qml.StatePrep(wf_cisd, wires=range(4))
...     return qml.state()
>>> print(circuit())
[ 0.        +0.j  0.        +0.j  0.        +0.j  0.1066467 +0.j
  1.        +0.j  0.        +0.j  0.        +0.j  0.        +0.j
  2.        +0.j  0.        +0.j  0.        +0.j  0.        +0.j
 -0.99429698+0.j  0.        +0.j  0.        +0.j  0.        +0.j]
```

--------------------------------

### Install PySCF

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/code/qp_qchem.rst

Installs the PySCF package, which is required for using the PySCF backend with PennyLane's quantum chemistry module.

```bash
pip install pyscf
```

--------------------------------

### Enable and Use PennyLane Logging

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/development/guide/logging.rst

Enables PennyLane logging and demonstrates its use within a QNode execution. This example shows how to log device creation, QNode definition, and evaluation steps.

```python
import pennylane as qp
import logging

qp.logging.enable_logging()

logger = logging.getLogger(__name__)
def circuit(param):
   logger.info(f"Creating {dev_name} device with {num_wires} wires")
   dev = qp.device(dev_name, wires=num_wires)

   @qp.set_shots(shots=num_shots)
   @qp.qnode(dev, diff_method="adjoint")
   def my_circuit(param):
       qp.RX(param, wires=0)
       qp.CNOT(wires=[0, 1])
       return qp.expval(qp.PauliZ(0))

   logger.info(f"Created QNODE={my_circuit}")
   res =  my_circuit(param)
   logger.info(f"Created QNODE evaluation={res}")

   return res

par = qp.numpy.array([0.1,0.2])

logger.info(f"Running circuit with par={par[0]}")
circuit(par[0])
logger.info(f"Running circuit with par={par[1]}")
circuit(par[1])
logger.info(f"Calculating jacobian circuit with par={par}")
logger.info(f"Jacobian={qp.jacobian(circuit)(par[0])}")
```

--------------------------------

### Optimizing QNode with JAXopt GradientDescent

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/introduction/interfaces/jax.rst

This example demonstrates optimizing a QNode using `jax.jit` and the `GradientDescent` optimizer from the JAXopt library. PennyLane's optimizers are not compatible with the JAX interface.

```python
import pennylane as qp
import jax
import jaxopt

jax.config.update("jax_enable_x64", True)

dev = qp.device("default.qubit", wires=1)

@jax.jit
@qp.set_shots(shots=None)
@qp.qnode(dev, interface="jax")
def energy(a):
    qp.RX(a, wires=0)
    return qp.expval(qp.PauliZ(0))

gd = jaxopt.GradientDescent(energy, maxiter=5)

res = gd.run(0.5)
optimized_params = res.params

>>> optimized_params
Array(3.1415861, dtype=float64, weak_type=True)
```

--------------------------------

### Idempotent run_autograph Example

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/releases/changelog-0.41.0.md

`qml.capture.run_autograph` is now idempotent, meaning applying it multiple times has the same effect as applying it once. This example illustrates the concept.

```python
import pennylane as qml
from pennylane import numpy as np

# Define a simple function
def my_function(x):
    return x * 2

# Apply run_autograph once
processed_function_1 = qml.capture.run_autograph(my_function)

# Apply run_autograph again to the already processed function
processed_function_2 = qml.capture.run_autograph(processed_function_1)

# Test both
result_1 = processed_function_1(5)
result_2 = processed_function_2(5)

print(f"Result after one run_autograph: {result_1}")
print(f"Result after two runs of run_autograph: {result_2}")

# The results should be identical, demonstrating idempotency.
```

--------------------------------

### Example Device Execution with Tracker

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/development/plugins.rst

Demonstrates how to use a device tracker to record execution details like batches, simulations, and resources. This snippet shows the typical usage within an execute method.

```python
def execute(self, circuits, execution_config: ExecutionConfig | None = None):
            return tuple(0.0 for _ in circuits)

>>> dev = MyDevice()
>>> tape = qp.tape.QuantumTape([qp.S(0)], [qp.expval(qp.X(0))])
>>> with dev.tracker:
...     out = dev.execute(tape)
>>> out
0.0
>>> dev.tracker.history
{'batches': [1],
 'simulations': [1],
 'executions': [1],
 'results': [0.0],
 'resources': [Resources(num_wires=1, num_gates=1,
 gate_types=defaultdict(<class 'int'>, {'S': 1}),
 gate_sizes=defaultdict(<class 'int'>, {1: 1}), depth=1,
 shots=Shots(total_shots=None, shot_vector=()))]}
```

--------------------------------

### Updated qml.PauliSentence Docstring Code Example

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/releases/changelog-0.41.0.md

The code example in the docstring for `qml.PauliSentence` has been updated to ensure it properly copy-pastes and runs.

```python
import pennylane as qml
from pennylane import numpy as np

# Example for qml.PauliSentence

# Define a PauliSentence
pauli_sentence = qml.PauliSentence({
    "PauliZ(0)@PauliX(1)": 2.0,
    "PauliX(0)@PauliY(1)": -1.5
})

print("PauliSentence:", pauli_sentence)

# Convert to a list of Pauli terms
pauli_terms = list(pauli_sentence.terms())
print("Pauli terms:", pauli_terms)

# This demonstrates how to create and inspect a PauliSentence object.
```

--------------------------------

### Improve Device.experimental.Device documentation

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/releases/changelog-0.32.0.md

Documentation for `qml.devices.experimental.Device` has been improved to clarify its use.

```python
qml.devices.experimental.Device
```

--------------------------------

### Deprecated qml.device Usage Examples

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/releases/changelog-0.43.0.md

Three examples of deprecated usage of 'qml.device(..., shots=...)' in the documentation have been updated.

```python
qml.device(..., shots=...)
```

--------------------------------

### Setup Entry Points in __init__.py

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/development/guide/documentation.rst

Import and use _setup_entry_points to make Catalyst features accessible from a PennyLane module. This is typically done in the module's __init__.py file.

```python3
from .._entry_points_utils import _setup_entry_points

__all__, __getattr__, __dir__ = _setup_entry_points(__name__, "pennylane.drawer")
```

--------------------------------

### Experimental Functionality Example

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/releases/changelog-0.13.0.md

Example of using an experimental function `ones_like` which returns a tensor of ones with the same shape and dtype as the input.

```python
>>> qml.proc.ones_like(y, dtype=np.complex128)
```

--------------------------------

### JAX-JIT Gradient Support for QNodes

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/releases/changelog-0.27.0.md

Illustrates the use of `jax.jit` with PennyLane QNodes for computing gradients. This example demonstrates computing the Jacobian of a circuit returning multiple expectation values using JAX.

```python
import jax
from jax import numpy as jnp
from jax.config import config
config.update("jax_enable_x64", True)

dev = qml.device("lightning.qubit", wires=2)

@jax.jit
@qml.qnode(dev, diff_method="parameter-shift", interface="jax")
def circuit(x, y):
    qml.RY(x, wires=0)
    qml.RY(y, wires=1)
    qml.CNOT(wires=[0, 1])
    return qml.expval(qml.PauliZ(0)), qml.expval(qml.PauliZ(1))

x = jnp.array(1.0)
y = jnp.array(2.0)
```

```python
>>> jax.jacobian(circuit, argnums=[0, 1])(x, y)
(Array([-0.84147098,  0.35017549], dtype=float64, weak_type=True),
 Array([ 4.47445479e-18, -4.91295496e-01], dtype=float64, weak_type=True))
```

--------------------------------

### Install Plugin in Developer Mode

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/development/legacy_plugins.rst

Install a plugin directly from its source path for development. Replace 'pluginpath' with the actual directory of your plugin.

```bash
pip install -e pluginpath
```

--------------------------------

### Install Development Dependencies

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/development/guide/installation.rst

Install additional packages required for development processes such as linting, testing, and pre-commit checks using pip.

```bash
python -m pip install --group dev
```

--------------------------------

### Execute a circuit with custom shot scaling

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/releases/changelog-0.32.0.md

This example demonstrates executing a QNode decorated with a custom transform that scales shots. The output shows the result of sampling from the circuit with the scaled number of shots.

```python
>>> circuit(shots=1)
array([False, False])
```

--------------------------------

### Install Pre-commit Package

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/development/guide/pullrequests.rst

Install the 'pre-commit' package using pip. This package is used to configure git hooks for code formatting and linting.

```bash
pip install pre-commit
```

--------------------------------

### Updated qml.unary_mapping Docstring Example

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/releases/changelog-0.41.0.md

The docstrings for several mapping functions, including `qml.unary_mapping`, have been updated to include better, copy-pasteable code examples.

```python
import pennylane as qml
from pennylane import numpy as np

# Example of qml.unary_mapping

def my_unary_op(x):
    return qml.RX(x, wires=0)

# Apply the unary mapping to a circuit
@qml.qnode(qml.device('default.qubit'))
def circuit(param):
    qml.unary_mapping(param, my_unary_op)
    return qml.expval(qml.PauliZ(0))

print(circuit(np.pi/2))

# This demonstrates how to define and use custom unary operations within PennyLane.
```

--------------------------------

### Update specs Function Examples

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/releases/changelog-0.43.0.md

Code examples in the documentation of ':func:`~.specs`' have been updated to replace keyword arguments with 'gradient_kwargs' in the QNode definition.

```python
.specs
```

--------------------------------

### PrepSelPrep Template Equivalence

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/releases/changelog-0.38.0.md

Demonstrates the equivalence between using the qml.PrepSelPrep template and a manual sequence of qml.StatePrep, qml.Select, and qml.adjoint(qml.StatePrep). Requires numpy for calculations.

```python
import numpy as np

coeffs = [0.3, 0.1]
alphas = (np.sqrt(coeffs) / np.linalg.norm(np.sqrt(coeffs)))
unitaries = [qml.X(2), qml.Z(2)]

lcu = qml.dot(coeffs, unitaries)
control = [0, 1]

def prep_sel_prep(alphas, unitaries):
    qml.StatePrep(alphas, wires=control, pad_with=0)
    qml.Select(unitaries, control=control)
    qml.adjoint(qml.StatePrep)(alphas, wires=control, pad_with=0)

@qml.qnode(qml.device("default.qubit"))
def circuit(lcu, control, alphas, unitaries):
    qml.PrepSelPrep(lcu, control)
    qml.adjoint(prep_sel_prep)(alphas, unitaries)
    return qml.state()
```

--------------------------------

### Get Canonical Interface Name with Enum

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/releases/changelog-0.43.0.md

Use the Enum directly to get the canonical interface name. This replaces the deprecated get_canonical_interface_name function.

```python
from pennylane.math.interface_utils import Interface
Interface("torch")
```

```python
Interface("jax-jit")
```

--------------------------------

### Initialize PennyLane Device with Custom Wires

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/releases/changelog-0.11.0.md

Demonstrates initializing a PennyLane device with custom wire labels for quantum operations.

```python
dev = qml.device("default.qubit", wires=['anc1', 'anc2', 0, 1, 3])
```

--------------------------------

### Example Package RST Content

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/development/guide/documentation.rst

This is an example of the content for a package's .rst file in the documentation. It uses the literalinclude directive to embed content from another file.

```rest
.. automodule:: pennylane.package_name
   :members:
   :undoc-members:
   :show-inheritance:
```

--------------------------------

### Use JAX Interface with default.qubit

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/releases/changelog-0.14.0.md

Example of creating and differentiating hybrid quantum-classical models using JAX with the `default.qubit.jax` device. Requires `interface="jax"` and `diff_method="backprop"`.

```python
import pennylane as qml
import jax
from jax import numpy as jnp

dev = qml.device("default.qubit", wires=1)
@qml.qnode(dev, interface="jax", diff_method="backprop")
def circuit(x):
    qml.RX(x[1], wires=0)
    qml.Rot(x[0], x[1], x[2], wires=0)
    return qml.expval(qml.PauliZ(0))

weights = jnp.array([0.2, 0.5, 0.1])
grad_fn = jax.grad(circuit)
print(grad_fn(weights))
```

--------------------------------

### Workflow Inspection with qjit specs

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/releases/changelog-0.45.0.md

Example of using qp.specs with qp.qjit and level="all" to display circuit resource evolution through compilation stages. Shows how transformations like cancel_inverses and merge_rotations affect gate counts and wire usage.

```python
@qp.qjit
@qp.transforms.merge_rotations
@qp.transforms.cancel_inverses
@qp.qnode(qp.device("lightning.qubit", wires=2))
def circuit():
    qp.RX(1.23,0)
    qp.RX(1.23,0)
    qp.X(0)
    qp.H(0)
    qp.H(0)
    return qp.probs()
```

```python
print(qp.specs(circuit, level="all")())
```

--------------------------------

### Example Module RST Content

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/development/guide/documentation.rst

This is an example of the content for a module's .rst file in the documentation. It uses the literalinclude directive to embed content from another file.

```rest
.. automodule:: pennylane.module_name
   :members:
   :undoc-members:
   :show-inheritance:
```

--------------------------------

### Enable JAX-JIT First-Order Gradients

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/releases/changelog-0.28.0.md

Demonstrates enabling first-order gradient computation with JAX-JIT after calling `qml.enable_return()`. Requires JAX and PennyLane.

```python
import jax
from jax import numpy as jnp

jax.config.update("jax_enable_x64", True)

qml.enable_return()

dev = qml.device("lightning.qubit", wires=2)

@jax.jit
@qml.qnode(dev, interface="jax-jit", diff_method="parameter-shift")
def circuit(a, b):
    qml.RY(a, wires=0)
    qml.RX(b, wires=0)
    return qml.expval(qml.PauliZ(0)), qml.expval(qml.PauliZ(1))

a, b = jnp.array(1.0), jnp.array(2.0)
```

```pycon
>>> jax.jacobian(circuit, argnums=[0, 1])(a, b)
((Array(0.35017549, dtype=float64, weak_type=True),
Array(-0.4912955, dtype=float64, weak_type=True)),
(Array(5.55111512e-17, dtype=float64, weak_type=True),
Array(0., dtype=float64, weak_type=True)))
```

--------------------------------

### Install PennyLane in Development Mode

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/development/guide/installation.rst

Clone the PennyLane repository and install it in editable mode. This ensures that changes to the source code are immediately reflected when importing PennyLane in Python.

```bash
git clone https://github.com/PennyLaneAI/pennylane
cd pennylane
python -m pip install -e .
```

--------------------------------

### Draw Trotterized Circuit (Example)

Source: https://github.com/pennylaneai/pennylane/blob/main/doc/releases/changelog-0.40.0.md

Draw the quantum circuit generated by the TrotterizedQfunc or trotterize function. This example shows the circuit for one Trotter step with specific parameters.

```python
time = 0.1
theta, phi = (0.12, -3.45)
print(qml.draw(my_circuit, level="device")(time, theta, phi, num_trotter_steps=1))
```