deploy: 7451148

Author: SaashaJoshi

.buildinfo

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+# Sphinx build info version 1
+# This file records the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: 93c38a4049d0bbd55fbf3da93109d298
+tags: 645f666f9bcd5a90fca523b33c5a78b7

.doctrees/environment.pickle

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+Getting Started
+================
+
+Setup
+-----
+
+Begin by creating a new Python environment or activating an existing one for working with the `piQture` library. You set up a Python virtual environment `venv` or a Conda environment and use `pip` or `conda` to install the `piQture` package.
+
+Here's how you can create a conda environment and manage a Python environment:
+
+.. code:: sh
+
+    # Create a new conda environment
+    conda create -n piqture_env python=3.x
+
+    # Activate the conda environment
+    conda activate piqture_env
+
+Installation
+------------
+
+Once the Python environment is activated, the required `piQture` package can be installed using `pip`. You can install the latest version directly from PyPI.
+
+.. code:: sh
+
+    pip install piqture
+
+To create a development environment, and install `piQture` from source, you can refer to section :ref:`Install from Source <installation_from_source>`.
+
+.. _installation_from_source:
+
+Installation from Source
+------------------------
+
+To set up a development environment and install `piQture` from source, follow these steps:
+
+1. Start by cloning the `piQture` repository from GitHub.
+
+.. code:: sh
+
+    # Clone the GitHub repository.
+    git clone https://github.com/SaashaJoshi/piQture.git
+
+
+2. Activate the Python environment and navigate to the `piQture` repository directory. Then, inside the Python environment, install the required dependencies from the `requirements.txt` configuration file.
+
+.. code:: sh
+
+    # Install the required dependencies
+    pip install -r requirements.txt
+
+
+3. Install `piQture` in editable mode to make changes to the source code.
+
+.. code:: sh
+
+    # Install from source in editable mode
+    pip install -e .
+
+Your development environment is set up, and `piQture` is installed from source. You can now start making changes to the code, running tests, and contributing to the project as a developer.

.doctrees/getting_started.doctree

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+.. piQture documentation master file, created by
+   sphinx-quickstart on Wed Apr 24 19:35:42 2024.
+   You can adapt this file completely to your liking, but it should at least
+   contain the root `toctree` directive.
+
+Welcome to piQture's documentation!
+===================================
+
+**piQture** is a Python and Qiskit-based toolkit designed to simplify the development, execution, and training of Quantum Machine Learning (QML) models tailored for image processing tasks. This library seamlessly integrates with the Qiskit SDK, providing a convenient and user-friendly workflow for leveraging the potential of quantum computing for advanced image processing.
+
+
+Contribution Guidelines
+-----------------------
+
+We welcome contributions! Whether you're a quantum enthusiast or a Python developer, your input is valuable. Check out our Contribution Guidelines to get started.
+
+Authors and Citation
+--------------------
+
+Saasha Joshi
+
+Table of Contents
+------------------
+
+.. toctree::
+   :maxdepth: 2
+
+   getting_started
+   modules

.doctrees/index.doctree

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+.. toctree::
+   :maxdepth: 4
+
+   piqture

.doctrees/modules.doctree

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+Data Loader
+============================
+
+`piqture.data_loader.mnist_data_loader` module
+-----------------------------------------------
+
+This module provides a `load_mnist_dataset` function that simplifies loading the MNIST dataset for machine learning and deep learning experiments. It supports custom batch sizes, label selection, image resizing, and normalization options.
+
+.. automodule:: piqture.data_loader.mnist_data_loader
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Overview
+--------
+
+The `load_mnist_dataset` function in this module is designed to streamline the process of loading and preparing the MNIST dataset for image-based machine learning models, especially those involving quantum machine learning or custom image processing workflows.
+
+Features
+--------
+
+- Supports custom image resizing to specified dimensions.
+- Optionally filters specific labels from the MNIST dataset.
+- Integrates custom normalization using `MinMaxNormalization`.
+- Provides separate training and testing DataLoaders.
+
+.. note::
+
+   Make sure that the `torch` and `torchvision` libraries are installed, as these are used internally for dataset handling and transformations.
+
+Function Documentation
+----------------------
+
+**`load_mnist_dataset`**
+
+.. autofunction:: piqture.data_loader.mnist_data_loader.load_mnist_dataset
+
+Usage Example
+-------------
+
+Here's an example of how to use the `load_mnist_dataset` function to load the MNIST dataset and apply custom configurations:
+
+.. code-block:: python
+
+    from piqture.data_loader import mnist_data_loader
+
+    # Load MNIST dataset with custom configurations
+    train_loader, test_loader = mnist_data_loader.load_mnist_dataset(
+        img_size=(32, 32),          # Resize images to 32x32
+        batch_size=64,              # Set batch size to 64
+        labels=[0, 1, 2],           # Include only labels 0, 1, and 2
+        normalize_min=0.0,          # Normalize minimum value to 0.0
+        normalize_max=1.0           # Normalize maximum value to 1.0
+    )
+
+    # Print some batch information
+    for images, labels in train_loader:
+        print(f"Batch image shape: {images.shape}")
+        print(f"Batch labels: {labels}")
+        break
+
+Parameters
+----------
+
+- **`img_size`** (*int* or *tuple[int, int]*, optional):
+  - Size to which MNIST images will be resized.
+  - If an integer, images will be resized to a square of that size.
+  - If a tuple, it should specify `(height, width)` for the images.
+  - **Default:** `28` (images are resized to 28x28 pixels).
+
+- **`batch_size`** (*int*, optional):
+  - Specifies the number of samples per batch for training and testing DataLoaders.
+  - If not specified, the batch size defaults to `1`.
+
+- **`labels`** (*list[int]*, optional):
+  - A list of integers representing the labels to include in the dataset.
+  - For example, setting `labels=[0, 1]` will include images of digits 0 and 1 only.
+
+- **`normalize_min`** (*float*, optional):
+  - Minimum value for pixel normalization.
+  - **Default:** `None` (no normalization).
+
+- **`normalize_max`** (*float*, optional):
+  - Maximum value for pixel normalization.
+  - **Default:** `None` (no normalization).
+
+Returns
+-------
+
+- **`Tuple[torch.utils.data.DataLoader, torch.utils.data.DataLoader]`**:
+  - A tuple containing:
+    - **Training DataLoader**: A PyTorch DataLoader for training data.
+    - **Testing DataLoader**: A PyTorch DataLoader for testing data.
+
+Related Functions and Classes
+-----------------------------
+
+**`collate_fn`**
+
+The `collate_fn` function is used to filter and organize batches based on the specified `labels`. It only includes images that match the desired labels.
+
+.. autofunction:: piqture.data_loader.mnist_data_loader.collate_fn
+
+Dependencies
+------------
+
+- **torch**: Required for creating PyTorch DataLoaders.
+- **torchvision**: Required for dataset loading and transformations.
+- **piqture.transforms.MinMaxNormalization**: Custom normalization transform available in the `piqture.transforms` module.
+
+Handling Edge Cases
+-------------------
+
+The function performs type checking and validation to ensure that the input parameters are valid:
+
+- **`img_size`**: Raises a `TypeError` if the value is not of type `int` or `tuple[int, int]`.
+- **`batch_size`**: Raises a `TypeError` if the value is not an integer.
+- **`labels`**: Raises a `TypeError` if the value is not a list.
+
+Refer to the `source code <https://github.com/SaashaJoshi/piqture>`_ for additional implementation details and advanced configurations.
+
+.. seealso::
+
+   - `torchvision.datasets.MNIST <https://pytorch.org/vision/stable/datasets.html#mnist>`_
+   - `piqture.transforms.MinMaxNormalization <https://piqture.readthedocs.io/en/latest/transforms.html#piqture.transforms.MinMaxNormalization>`_

.doctrees/piqture.data_loader.doctree

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+Image Embeddings
+============================================
+
+piqture.embeddings.image\_embeddings.frqi module
+------------------------------------------------
+
+.. automodule:: piqture.embeddings.image_embeddings.frqi
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+piqture.embeddings.image\_embeddings.ineqr module
+-------------------------------------------------
+
+.. automodule:: piqture.embeddings.image_embeddings.ineqr
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+piqture.embeddings.image\_embeddings.mcrqi module
+-------------------------------------------------
+
+.. automodule:: piqture.embeddings.image_embeddings.mcrqi
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+piqture.embeddings.image\_embeddings.neqr module
+------------------------------------------------
+
+.. automodule:: piqture.embeddings.image_embeddings.neqr
+   :members:
+   :undoc-members:
+   :show-inheritance:

.doctrees/piqture.doctree

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+Embeddings
+==========================
+
+Submodules
+-----------
+.. toctree::
+   :maxdepth: 2
+
+   piqture.embeddings.image_embeddings
+
+piqture.embeddings.amplitude\_encoder module
+--------------------------------------------
+
+.. automodule:: piqture.embeddings.amplitude_encoder
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+piqture.embeddings.angle\_encoding module
+-----------------------------------------
+
+.. automodule:: piqture.embeddings.angle_encoding
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+
+piqture.embeddings.image\_embedding module
+------------------------------------------
+
+.. automodule:: piqture.embeddings.image_embedding
+   :members:
+   :undoc-members:
+   :show-inheritance:

.doctrees/piqture.embeddings.doctree

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+Gates
+=====================
+
+piqture.gates.two\_qubit\_unitary module
+----------------------------------------
+
+.. automodule:: piqture.gates.two_qubit_unitary
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+piqture.gates.unitary\_block module
+-----------------------------------
+
+.. automodule:: piqture.gates.unitary_block
+   :members:
+   :undoc-members:
+   :show-inheritance:

.doctrees/piqture.embeddings.image_embeddings.doctree

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+Mixin
+=====================
+
+piqture.mixin.image\_embedding\_mixin module
+--------------------------------------------
+
+.. automodule:: piqture.mixin.image_embedding_mixin
+   :members:
+   :undoc-members:
+   :show-inheritance:

.doctrees/piqture.gates.doctree

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+Neural Network Layers
+=======================================
+
+piqture.neural\_networks.layers.base\_layer module
+--------------------------------------------------
+
+.. automodule:: piqture.neural_networks.layers.base_layer
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+piqture.neural\_networks.layers.convolutional\_layer module
+-----------------------------------------------------------
+
+.. automodule:: piqture.neural_networks.layers.convolutional_layer
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+piqture.neural\_networks.layers.fully\_connected\_layer module
+--------------------------------------------------------------
+
+.. automodule:: piqture.neural_networks.layers.fully_connected_layer
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+piqture.neural\_networks.layers.pooling\_layer module
+-----------------------------------------------------
+
+.. automodule:: piqture.neural_networks.layers.pooling_layer
+   :members:
+   :undoc-members:
+   :show-inheritance:

.doctrees/piqture.mixin.doctree

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+Neural Networks
+================================
+
+
+Submodules
+-----------
+.. toctree::
+   :maxdepth: 2
+
+   piqture.neural_networks.layers
+
+piqture.neural\_networks.qcnn module
+------------------------------------
+
+.. automodule:: piqture.neural_networks.qcnn
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+piqture.neural\_networks.quantum\_autoencoder module
+----------------------------------------------------
+
+.. automodule:: piqture.neural_networks.quantum_autoencoder
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+piqture.neural\_networks.quantum\_neural\_network module
+--------------------------------------------------------
+
+.. automodule:: piqture.neural_networks.quantum_neural_network
+   :members:
+   :undoc-members:
+   :show-inheritance: