Quantum Machine Learning (QML) AI in Python
This repository contains the QuaintQuanta (QQ.py) Python script demonstrating the application of Quantum Machine Learning (QML) to solve a classification problem using both quantum and classical machine learning methods.
The QQ.py script uses an advanced version of a Quantum Neural Network (QNN) and a classical Support Vector Classifier (SVC) to classify wine samples based on their chemical properties.
• Quantum machine learning with an advanced Quantum Neural Network (QNN)
• Classical machine learning with a Support Vector Classifier (SVC)
• Use of the Wine dataset from sklearn for multiclass classification
• Data preprocessing (scaling) to adjust for the quantum model
• Optimization with hyperparameter tuning
To run the QQ.py script, you need to have the following Python libraries installed:
• PennyLane
• numpy
• sklearn
If not installed, use pip to install them:
pip install pennylane numpy sklearn
To run the script, simply navigate to its directory and run the command:
python QQ.py
The script constructs a parameterized quantum circuit (a Quantum Neural Network) using PennyLane, a Python library for quantum machine learning. The quantum model is then trained using a hybrid quantum-classical approach.
The data for the model is obtained from sklearn’s Wine dataset, which is then split into training and testing sets.
The quantum AI model uses the concept of amplitude embedding to encode real-valued data into the state of a quantum system. It then uses the concept of entangling layers and StronglyEntanglingLayers from PennyLane for building up a more complex quantum circuit.
An optimization process is carried out to train the quantum model. The cost function used for training the quantum model is a function of the model’s accuracy on the training data. The optimization aims to minimize this cost function.
A classical SVC model is then trained on the same data, which is transformed by the quantum feature map, and its performance is tested.
Finally, the accuracy of the SVC model on the testing data is printed.
The script offers several opportunities for customization and further development:
• The quantum model structure can be modified or extended to create more complex QNNs.
• Other quantum feature maps and embeddings can be experimented with.
• Different optimization strategies and cost functions can be explored.
• You could also experiment with other quantum-compatible datasets, and perform different tasks such as regression or clustering.
• Additionally, you could also experiment with quantum computing hardware, using PennyLane’s ability to connect to different quantum computing backends.
While the script is a good example of the principles of Quantum Machine Learning, it should be noted that the field is still in its nascent stage and the script is in a research/experimental stage. Quantum Machine Learning is computationally intensive and may not be practical for larger datasets or more complex tasks with the current state of quantum computing technology. However, as quantum technology advances, it is expected to have significant potential for machine learning.
Also, keep in mind that running quantum simulations for large numbers of qubits might require substantial computational resources. This is one of the key challenges in scaling up quantum computations.
In addition, the performance of the model heavily depends on the dataset used and how it is preprocessed, so care should be taken when selecting and preprocessing the data.
Created by:
Adam Rivers https://abtzpro.github.io
Hello Security LLC https://hellosecurityllc.github.io
Datasets
The Wine dataset used in the script is a part of the sklearn.datasets module. Special thanks to the creators of the dataset for making it freely available for educational and research purposes.
Libraries and Functions
• PennyLane: A key component in this script, PennyLane is an open-source library for quantum machine learning. It allows us to design and train quantum circuits, create quantum/classical hybrid computational models, and optimize these models. Thank you to the developers of PennyLane for their great work in making quantum machine learning more accessible.
• NumPy: An essential library for numerical computation in Python. This script heavily relies on NumPy for data manipulation and mathematical operations. Thanks to the NumPy team for their robust and efficient library.
• scikit-learn (sklearn): The script uses scikit-learn for several tasks including data loading, data splitting, data preprocessing, and for the Support Vector Classifier model. Thanks to the scikit-learn developers for this comprehensive machine learning library.
Special Credits
• To the Quantum Machine Learning and Quantum Computing community for their research and work in the field, which was essential in the development of this script.
• To the OpenAI team for developing and training the GPT model, which helped in educating us on the foundational requirements for this script.
• Finally, thank you to all the developers and researchers in the field of quantum computing and machine learning for their continuous efforts in pushing the boundaries of what is possible.
This script would not be possible without all these contributions. Thank you!