Name: Quantum Robustness in Artificial Intelligence: Principles and Applications (en Inglés)
SKU: 66917666
Price: 3019.70 MXN
Availability: InStock
Author: Usman, Muhammad
ISBN: 9783032111524

portada Quantum Robustness in Artificial Intelligence: Principles and Applications (en Inglés)

Quantum Robustness in Artificial Intelligence: Principles and Applications (en Inglés)

Formato

Libro Físico

Editorial

Springer

Autor

Usman, Muhammad

Idioma

Inglés

N° páginas

453

Encuadernación

Tapa Dura

Dimensiones

23.5x15.5 cm

ISBN13

9783032111524

Quantum Robustness in Artificial Intelligence: Principles and Applications (en Inglés)

Usman, Muhammad (Autor) · Springer · Tapa Dura

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Reseña del libro "Quantum Robustness in Artificial Intelligence: Principles and Applications (en Inglés)"

This book surveys state-of-the-art research on adversarial robustness of quantum machine learning algorithms. Despite their high efficiency and accuracy, classical ML and AI algorithms can be easily fooled by an adversary through manipulation or spoofing of data (also known as adversarial attacks), which poses serious security ramifications. On the other hand, the integration of quantum computing in ML and AI is progressing rapidly to create new quantum ML/AI models which are designed to fundamentally exploit quantum mechanical properties to gain advantages in aspects such as training speed or feature extraction accuracy. This raises the important question of whether quantum AI algorithms are as vulnerable as classical AI models. Recent work has shown that quantum AI algorithms are remarkably robust against adversarial attacks. This offers a unique opportunity to leverage quantum computing, specifically its unique properties like superposition and entanglement, to develop highly resistant quantum AI systems. This shift is crucial for enhancing the safety and reliability of AI in security-sensitive applications. This book provides a comprehensive overview of the research in the emerging field of quantum adversarial AI, presenting seminal work from world-leading quantum AI experts on quantum AI and its benchmarking against adversarial attacks. It provides an essential reference for graduate students and industry experts who are interested in quantum AI for security-sensitive autonomous systems.