Future Perspectives of Quantum Applications Using AI

Kalyani Guddanti

Artificial Intelligence (AI) continues to drive innovation across industries, but faces mounting challenges in computational scalability, optimization, and interpretability as models grow increasingly complex. Quantum Artificial Intelligence (QAI) offers a promising path forward by leveraging quantum phenomena such as superposition and entanglement to augment learning processes beyond classical limits. This review synthesizes the recent progress in QAI, emphasizing a comparative perspective framed through its advantages and limitations. It examines how hybrid quantum-classical architectures and variational models seek to address constraints in the Noisy Intermediate-Scale Quantum (NISQ) era, and how applications in healthcare, finance, and cybersecurity illustrate both the potential and the barriers of the field. While several studies have broadly mapped the growth of QAI in algorithms and applications, this paper adopts a different perspective: emphasizing comparative analysis through advantages and limitations. The focus is on where QAI offers unique opportunities, where it encounters obstacles distinct from classical AI and quantum computing, and what research directions may transform these challenges into breakthroughs. By situating QAI within this comparative framework, the paper provides a realistic yet forward-looking assessment of its trajectory as a paradigm poised to reshape computational intelligence.

Zhuo Chen, Di Luo

The intersection of artificial intelligence (AI) and quantum physics has sparked significant advancements in both fields. This paper first reviews the application of AI techniques, particularly neural networks, to quantum simulation. We discuss key developments in representing quantum states, efficient sampling methods, and optimization techniques for ground states and dynamics. The review also explores how quantum principles enhances AI algorithms, including quantum-inspired neural architectures and physics-based optimization methods. We highlight challenges and open questions in this rapidly evolving interdisciplinary field, emphasizing the potential for future innovations that combine AI and quantum approaches to address complex problems in both domains.

Satchidananda Dehuri, Monalisa Jena, Sarat Chandra Nayak +2 more

Scott A. Humr, M. Canan, Mustafa Demir

Artificial intelligence is set to incorporate additional decision space that has traditionally been the purview of humans. However, AI systems that support decision making also entail the rationalization of AI outputs by humans. Yet, incongruencies between AI and human rationalization processes may introduce uncertainties in human decision making, which require new conceptualizations to improve the predictability of these interactions. The application of quantum probability theory (QPT) to human cognition is on the ascent and warrants potential consideration to human-AI decision making to improve these outcomes. This perspective paper explores how QPT may be applied to human-AI interactions and contributes by integrating these concepts into human-in-the-loop decision making. To capture this and offer a more comprehensive conceptualization, we use human-in-the-loop constructs to explicate how recent applications of QPT can ameliorate the models of interaction by providing a novel way to capture these behaviors. Followed by a summary of the challenges posed by human-in-the-loop systems, we discuss newer theories that advance models of the cognitive system by using quantum probability formalisms. We conclude by outlining areas of promising future research in human-AI decision making in which the proposed methods may apply.

Fatma Foad Ashrif, Rami Ahmad, Faiza A. M. Mansour