Statistics

Artificial Intelligence and Quantum Computing: Statistical Methods, Theory, and Applications

Motivation and Scope

AI and Quantum computing are reshaping the landscape of computational science. AI has become a central technology across science, engineering, finance, healthcare, and many other domains. At the same time, advances in quantum computing are creating entirely new paradigms for computation and learning. Increasingly, these developments converge in the emerging field of Quantum AI, where quantum algorithms are developed for machine learning, AI techniques support quantum systems, and hybrid quantum-classical methods offer new approaches to challenging computational problems.

As these technologies mature, rigorous statistical thinking becomes increasingly important. Statistical methodology provides the theoretical foundations for learning from data, quantifying uncertainty, validating empirical findings, designing experiments, and evaluating increasingly complex computational systems. At the same time, Quantum AI raises new statistical challenges related to probabilistic modeling, inference, high-dimensional learning, noisy quantum hardware, uncertainty quantification, and the reliable assessment of quantum advantage.

This Special Issue aims to bring together contributions at the intersection of Artificial Intelligence, Quantum Computing, and Statistics. It welcomes methodological, theoretical, and application-oriented research that advances our understanding of these rapidly evolving fields and their interactions. While the primary focus lies on statistical methodology and its role in Quantum AI, contributions from mathematics, machine learning, quantum information science, and related disciplines are equally welcome when they offer clear methodological or theoretical insights relevant to the readership of Statistics.

The Special Issue also encourages contributions demonstrating the impact of Quantum AI in application domains where statistical methodology plays a central role, including quantitative finance, economics, engineering, healthcare, and the natural sciences.

Topics of Interest

Topics of interest include, but are not limited to:

•        Statistical methods for quantum computing and quantum machine learning

•        Statistical learning theory for Quantum AI

•        Quantum algorithms for machine learning and data analysis

•        AI methods for quantum systems, quantum control, and quantum optimization

•        Uncertainty quantification, inference, and probabilistic modeling in Quantum AI

•        Benchmarking, validation, and performance evaluation of quantum and hybrid algorithms

•        Hybrid quantum-classical learning methods

•        High-dimensional statistical methods for quantum data

•        Explainability, robustness, and trustworthy Quantum AI

•        Applications of Quantum AI in finance, economics, engineering, healthcare, and other scientific domains

Association with IntelligenceX 2026

This Special Issue is organized in association with IntelligenceX 2026: The Global Quantum×AI Frontier (Singapore, 24–26 September 2026), an international conference that brings together researchers from statistics, artificial intelligence, machine learning, quantum computing, mathematics, and related disciplines.

Authors presenting their work at the conference will be invited to submit substantially extended versions of their research for consideration in this Special Issue. Submissions are not restricted to conference participants, and all manuscripts will undergo the journal’s standard peer-review process in accordance with the editorial policies of Statistics.