Quantum Machine Learning: Principles and Applications

Arshdeep, Konika Rani, Karanpreet Singh +3 more

Machine learning, a subset of AI is moving algorithms in the system which can be learned by data and not programmed explicitly. Various applications of AI across professionals like finance, health-care and automation. With the advancements in Machine Learning, Quantum Machine Learning (QML) or more concretely integrating quantum mechanics into classical machine learning tasks is emerging since QM provides some of the principles to increase efficiency from computational benchmarks. From the most used types of machine learning and algorithms to quantum its usage and applications in Quantum Machine Learning (QML) this paper covers everything. We then articulate some benefits and difficulties of merging quantum computing with machine learning, as well the real-life applications across industry backbones.

Md Shabbeer, Aarti, Swathi Mummadi

Quantum Machine Learning (QML) is an emerging interdisciplinary field which combines Quantum Computing and Machine Learning. With the help of Quantum principles, QML outperforms classical machine learning (CML) models. These principles make the QML models work exponentially faster than the CML models. A detailed analysis of various QML models, algorithms, and architectures with respect to healthcare applications is presented in this paper. The currently available quantum systems are associated with noise, which is why they are referred to as Noisy Intermediate-Scale Quantum Systems. The internal noise of a quantum system presents various challenges when experiments are implemented and run on Quantum systems and simulators. The current challenges in developing QML for healthcare applications, such as internal noise, hardware limitations, and high computational cost, are also detailed in this paper. The detailed analysis presented in this paper will help researchers to know the opportunities and future directions in this field.

Amani Lamine, Ameni Mejri, Bilel Charfi

Sajeeda Parveen Shaik

Quantum Machine Learning (QML), a confluence of quantum computing and classical machine learning, represents a revolutionary paradigm with transformative potential. This systematic review explores the landscape of QML by investigating its underlying principles, methodologies, diverse applications, challenges, and ethical considerations. Beginning with an examination of fundamental quantum computing principles, the review navigates through various QML methodologies, comparing them with classical counterparts. Real-world applications, ranging from quantum-enhanced optimization to drug discovery, are scrutinized, showcasing the practical implications of QML across industries. The paper systematically identifies challenges, including quantum hardware constraints and ethical considerations, while offering insights into current limitations and future research directions. A comparative analysis benchmarks QML against classical machine learning approaches, providing a nuanced understanding of its strengths and limitations. Ethical considerations underscore the importance of responsible AI practices in the integration of QML. The review concludes by identifying research gaps and suggesting future directions, emphasizing the need for continued exploration in this dynamic intersection of quantum computing and machine learning. This comprehensive exploration serves as a valuable resource for researchers, practitioners, and decision-makers seeking insights into the current state and transformative potential of Quantum Machine Learning.

Sumitha Valsalam, Tony Thomas