Quantum Artificial Intelligence Scalability in the NISQ Era: Pathways to Quantum Utility

作者：G. Moreau, Lorenzo Pisani, Manuela Profir, Carlo Podda, Lidia Leoni, Giacomo Cao · 发表于：Advanced Quantum Technologies · 年份：2025 · DOI：10.1002/qute.202400716 · 被引用次数：9 · 研究领域：Quantum Computing Algorithms and Architecture、Quantum Information and Cryptography、Computability, Logic, AI Algorithms

Abstract Quantum computing has immense potential to advance the field of Artificial Intelligence (AI), promising faster processing, better optimization, and the ability to handle complex data structures more effectively. However, several challenges need to be addressed, including hardware limitations, algorithm development, and integration with existing AI workflows. This work provides a comprehensive overview of the current state of quantum AI research, covering key areas such as quantum machine learning, quantum deep learning, quantum natural language processing, quantum problem solving, quantum fuzzy logic, quantum evolutionary computation, and quantum decision making. It also presents a collection of interesting use cases targeted at different vertical sectors. The research methodology involves a thorough literature review to capture the most impactful developments from the standpoint of algorithm scalability in the 2020–2024 timeframe. The review highlights the benefits of quantum approaches, such as improved learning capacity, robustness to overfitting and, in some specific cases, quantum speedups for solving various problems. The interdisciplinary research landscape demonstrates a discernible trajectory toward the implementation of practical, large‐scale quantum computational intelligence. This progression is contingent upon the adoption of scalable algorithms that can deliver a quantum advantage, diverging from the replication of classical AI workflows.