Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Quantum Robustness in Artificial Intelligence: Principles and Applications

Quantum Robustness in Artificial Intelligence: Principles and Applications

Usman, Muhammad

Hardcover

Series: Quantum Science and Technology

General ComputersProbability & Statistics

PREORDER - Expected ship date February 22, 2026

ISBN10: 3032111528
ISBN13: 9783032111524
Publisher: Springer
Published: Feb 22 2026
Pages: 450
Language: English
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.

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