Quantum Robustness in Artificial Intelligence

Jaydip Sen

Siva Sai, Rajkumar Buyya

Mission critical (MC) applications such as defense operations, energy management, cybersecurity, and aerospace control require reliable, deterministic, and low-latency decision making under uncertainty. Although the classical Artificial Intelligence (AI) approaches are effective, they often struggle to meet the stringent constraints of robustness, timing, explainability, and safety in the MC domains. Quantum Artificial Intelligence (QAI), the fusion of artificial intelligence and quantum computing (QC), can potentially provide transformative solutions to the challenges faced by classical ML models. QAI is a broader umbrella than Quantum Machine Learning (QML) and additionally includes quantum optimization, search, and reasoning; we use QAI throughout the paper for the field at large, and QML only for learning-specific subroutines. The principal contributions of this work are: (i) a systematic survey of QAI methods analyzed through the lens of MC requirements like certification, robustness, and timing; (ii) a conceptual quantum cloud resource management and scheduling framework with deployment assumptions, complexity analysis, and failure-mode discussion; and (iii) an identification of the gaps between current QAI capabilities and MC systems requirements. We also propose a conceptual model for management of quantum resources and scheduling of applications driven by timeliness constraints. We discuss multiple challenges, including trainability limits, data access, and loading bottlenecks, verification of quantum components, and adversarial QAI. Finally, we outline future research directions toward achieving interpretable, scalable, and hardware-feasible QAI models for MC application deployment.

Sumin Wang, Ning Wang, Tongyu Ji +2 more

The rapid accumulation of big data in the Internet era has gradually decelerated the progress of Artificial Intelligence (AI). As Moore's Law approaches its limit, it is imperative to break the constraints that are holding back artificial intelligence. Quantum computing and artificial intelligence have been advancing along the highway of human civilization for many years, emerging as new engines driving economic and social development. This article delves into the integration of quantum computing and artificial intelligence in both research and application. It introduces the capabilities of both universal quantum computers and special-purpose quantum computers that leverage quantum effects. The discussion further explores how quantum computing enhances classical artificial intelligence from four perspectives: quantum supervised learning, quantum unsupervised learning, quantum reinforcement learning, and quantum deep learning. In an effort to address the limitations of smart cities, this article explores the formidable potential of quantum artificial intelligence in the realm of smart cities. It does so by examining aspects such as intelligent transportation, urban operation assurance, urban planning, and information communication, showcasing a plethora of practical achievements in the process. In the foreseeable future, Quantum Artificial Intelligence (QAI) is poised to bring about revolutionary development to smart cities. The urgency lies in developing quantum artificial intelligence algorithms that are compatible with quantum computers, constructing an efficient, stable, and adaptive hybrid computing architecture that integrates quantum and classical computing, preparing quantum data as needed, and advancing controllable qubit hardware equipment to meet actual demands. The ultimate goal is to shape the next generation of artificial intelligence that possesses common sense cognitive abilities, robustness, excellent generalization capabilities, and interpretability.

Emma Andrews, Prabhat Mishra

Deepika Ghai, Suman Lata Tripathi, Ramandeep Sandhu +1 more

Jaydip Sen

Artificial Intelligence (AI) has achieved remarkable success across diverse application domains; however, its vulnerability to adversarial attacks poses significant challenges to reliability, security, and trustworthiness. Adversarial machine learning demonstrates that even highly accurate models can be manipulated through carefully crafted perturbations, raising serious concerns in safety-critical systems such as healthcare, finance, and autonomous technologies. In parallel, quantum computing has emerged as a transformative paradigm capable of addressing complex computational problems through principles such as superposition, entanglement, and quantum interference. The convergence of these fields has led to the emergence of Quantum Artificial Intelligence (QAI), which explores how quantum techniques can enhance learning efficiency, scalability, and robustness. This chapter provides a comprehensive overview of adversarial machine learning and existing defense strategies, followed by an accessible introduction to quantum computing and quantum machine learning models.