Machine Learning Quantum Emergence

Jacob Biamonte, Péter Wittek, Nicola Pancotti +3 more

Sachin Khurana, Manisha J. Nene

Machine Learning (ML) has been extensively utilized in various scientific and engineering domains. But the inherent constraints and computational complexity that arise in classical machine learning are particularly evident when dealing with large-scale, high-dimensional datasets or when attempting to solve intricate optimisation problems. Quantum systems exhibit unconventional patterns that are typically not effectively generated by traditional systems and can also tackle high dimensional and large size datasets efficiently. Therefore, it is believed that quantum computing systems may outperform traditional computing devices in machine learning tasks. Quantum Machine Learning (QML) has emerged as a rapidly developing field that combines the concepts of quantum mechanics and machine learning to solve complex problems across various disciplines. The domain of quantum machine learning investigates the development and execution of quantum software with the potential to facilitate machine learning at a much superior pace compared to traditional computers. However, the major challenges encountered are hardware limitations, noise, and development of quantum algorithms. The objective of this paper is to provide an insight on the foundational principles, essential algorithms, various associated platforms, potential applications and challenges being faced by QML. As data continues to grow in size and complexity, together with the emergence of nonlinear and high-dimensional real-world situations, the computing benefits offered by QML have the potential to facilitate significant advancements in the fields of healthcare, finance, robotics, logistics, communication, and cyber security. This paper presents the basis for future research work related to development of quantum algorithms in the domain of machine learning.

Pravin Ghosekar

The quantum computing paradigm is revolutionizing computation with the use of quantum mechanical principles such as superposition and entanglement. The machine learning (ML) method has also emerged as a cornerstone of modern artificial intelligence. Quantum Machine Learning (QML) combines quantum computing with machine learning to achieve unprecedented computational speed and efficiency. Quantum computing, machine learning, and its potential to transform computing technologies in the future are explored in this paper. Furthermore, it discusses key application areas, advantages, challenges, and future directions for research. QML can significantly improve data processing, optimization, and predictive modeling, paving the way for the next generation of intelligent systems, according to the study.

S. Karthikeyan, M Akila, D. Sumathi +1 more

Claudio Conti

Roozbeh Razavi‐Far, Mohammad Meymani, Erfan Mahmoudinia +6 more

Machine learning has revolutionized numerous industrial domains. Despite recent advances, machine learning models remain vulnerable to adversarial threats. Adversarial machine learning is a field that studies these vulnerabilities to build robust machine learning models. Quantum machine learning is an interdisciplinary field that bridges quantum computing and classical machine learning. While quantum machine learning shows potentials to outperform classical machine learning in complex tasks such as regression, classification, and generative modeling, it remains vulnerable to adversarial attacks. Given the recent advancements in quantum computing and machine learning, the quantum adversarial machine learning field has emerged to study the vulnerabilities of quantum machine learning, possible attacks, and novel quantum-enhanced defense strategies. In this survey, we provide a detailed overview on quantum adversarial machine learning and explore the existing attacks and countermeasures. We also review the theoretical underpinnings of this area, emerging trends, and critical challenges.