During my journey into the realm of quantum computing and machine learning, I have developed a Quantum Neural Network (QNN) project focused on classifying Iris flowers. This project is a fascinating intersection of traditional machine learning and the emerging field of quantum computing, utilizing the capabilities of PennyLane, a Python library dedicated to quantum machine learning.
The QNN is adeptly designed to categorize the Iris dataset into three species: setosa, versicolor, and virginica. It is a testament to the potential of quantum computing in handling complex datasets and tasks.
Key Features:
- Quantum Computing Integration: This project leverages PennyLane for implementing a QNN.
- Iris Dataset Classification: Utilizes a well-known dataset comprising 150 samples of Iris flowers, each with four distinct features.
- Innovative Quantum Circuit Architecture: The QNN features a two-layer structure with random rotations and entangling CNOT gates, a novel approach in data classification.
Technical Prerequisites:
- Libraries: The project uses PennyLane, pandas, numpy, and tqdm.
- Installation: All necessary libraries can be installed via pip commands.
Dataset:
- The Iris dataset, fundamental to this project, includes 150 samples with features like sepal length, width, petal length, and width.
Quantum Circuit Design:
- The QNN uses a state-of-the-art design with RX gates for input data preparation and Pauli operators for making predictions.
Running the Project:
- The code encompasses importing libraries, setting up qubits, embedding layers, and initializing the quantum device.
- It also includes defining state preparation functions, quantum circuits, and various essential functions like softmax, cross-entropy, and accuracy.
- The project involves loading the Iris dataset, splitting it into training and testing sets, initializing weights, and setting hyperparameters.
Training and Testing:
- The model undergoes a rigorous training loop with adjustable hyperparameters and is then tested on the Iris dataset.
Conclusion:
This project not only demonstrates the application of quantum computing in machine learning but also serves as an excellent introduction to quantum machine learning. Inspired by the tutorial at dvic.devinci.fr, it opens avenues for further adaptations and explorations into more complex quantum models and datasets. This Quantum Neural Network is a step towards the future of machine learning, blending traditional algorithms with the power of quantum computing.