Quantum Machine Learning
Quantum machine learning (QML) aims to leverage the unique properties of quantum computers to enhance machine learning algorithms, primarily focusing on improving speed, accuracy, and data efficiency. Current research emphasizes the development and application of quantum algorithms like variational quantum circuits (VQCs), quantum kernels, and quantum neural networks (QNNs), including variations such as quantum LSTMs and GANs, often in hybrid quantum-classical architectures. This field is significant because it explores the potential for quantum speedups in various machine learning tasks, with applications ranging from image classification and drug discovery to materials science and anomaly detection, although the extent of practical quantum advantage remains an active area of investigation. Challenges include mitigating noise in quantum hardware and understanding the generalization capabilities of QML models.
Papers
Shadows of quantum machine learning
Sofiene Jerbi, Casper Gyurik, Simon C. Marshall, Riccardo Molteni, Vedran Dunjko
Improved Financial Forecasting via Quantum Machine Learning
Sohum Thakkar, Skander Kazdaghli, Natansh Mathur, Iordanis Kerenidis, André J. Ferreira-Martins, Samurai Brito
Exploring the Vulnerabilities of Machine Learning and Quantum Machine Learning to Adversarial Attacks using a Malware Dataset: A Comparative Analysis
Mst Shapna Akter, Hossain Shahriar, Iysa Iqbal, MD Hossain, M. A. Karim, Victor Clincy, Razvan Voicu
Software Supply Chain Vulnerabilities Detection in Source Code: Performance Comparison between Traditional and Quantum Machine Learning Algorithms
Mst Shapna Akter, Md Jobair Hossain Faruk, Nafisa Anjum, Mohammad Masum, Hossain Shahriar, Akond Rahman, Fan Wu, Alfredo Cuzzocrea
On quantum backpropagation, information reuse, and cheating measurement collapse
Amira Abbas, Robbie King, Hsin-Yuan Huang, William J. Huggins, Ramis Movassagh, Dar Gilboa, Jarrod R. McClean
Quantum Text Classifier -- A Synchronistic Approach Towards Classical and Quantum Machine Learning
Dr. Prabhat Santi, Kamakhya Mishra, Sibabrata Mohanty