Journal title Journal of Quantum Artificial Intelligence
Initials JQAI
Abbreviation J. Quantum Artif. Intell.
Online ISSN xxxx-xxxx
Frequency 4 issues per year
DOI doi.org/10.63913/jqai
Editor-in-chief

Dr. Shofa Shofiah, H. M.Kom (Department of Informatics Management, AMIK YPAT Purwakarta, Indonesia)

   
Publisher LPPM AMIK YPAT Purwakarta
Citation Analysis Scopus | Web of Science | Google Scholar
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The Journal of Quantum Artificial Intelligence (JQAI) is an international, peer-reviewed scholarly journal that focuses on pioneering research at the intersection of quantum computing and artificial intelligence (AI). The journal serves as a global forum for scientists, researchers, and practitioners to share novel ideas, groundbreaking findings, and transformative innovations that explore how quantum principles can enhance the capabilities of intelligent systems.

In an era where computational paradigms are rapidly evolving, JQAI stands as a bridge between quantum theory and artificial intelligence, providing a dedicated platform for studies that aim to redefine the foundations of intelligent computation. The journal promotes interdisciplinary collaboration, integrating concepts from computer science, quantum physics, mathematics, information theory, and cognitive systems.

Through its publications, JQAI seeks to advance both theoretical frameworks and practical implementations, encouraging research that contributes to the development of next-generation technologies capable of leveraging quantum mechanics to transform learning processes, reasoning structures, and decision-making mechanisms within intelligent environments.

Topics covered include:

Quantum Computing Models and Algorithms for Artificial Intelligence; Quantum Machine Learning and Hybrid Quantum-Classical Systems; Quantum Neural Networks and Optimization Techniques; Explainable and Interpretable Artificial Intelligence in Quantum Systems; Quantum Cryptography, Security, and Information Theory for Intelligent Environments; Quantum-Enhanced Decision Support, Data Analysis, and Simulation Frameworks.

JQAI aims to foster interdisciplinary dialogue and collaboration, contributing to the body of knowledge that drives the evolution of next-generation intelligent systems powered by quantum technologies. Papers published in JQAI are grounded in rigorous research methodologies and are expected to clearly articulate their theoretical contributions as well as practical implications. Authors are encouraged to explicitly state their novelty and advancement to the state of the art, while also considering the broader societal, ethical, and technological impact of their work. In alignment with global research priorities, JQAI welcomes studies that contribute to sustainability and support the achievement of the United Nations 2030 Sustainable Development Goals (SDGs).

Subject Area and Category:

The Journal of Quantum Artificial Intelligence (JQAI) focuses on the design, development, and evaluation of quantum-enhanced artificial intelligence systems. The journal covers research on Quantum Computing Models and Algorithms for AI; Quantum Machine Learning and Neural Systems; Explainable and Trustworthy Quantum AI; Quantum Cryptography and Intelligent Security Systems; and Quantum-Driven Decision Support and Simulation. It also addresses interdisciplinary aspects, including mathematical foundations, information theory, cognitive modeling, and real-world applications in domains such as healthcare, finance, cybersecurity, smart systems, and digital society.

Starting publishing date: 2026

Frequency: Quarterly (February, May, August, and November)

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Vol. 1 No. 1 (2026): Regular Issue May 2026

This regular May 2026 issue comprises four original research articles authored and co-authored by 9 contributors representing 2 countries: Indonesia and Turkey.

  Indonesia  

Published: 2026-05-01

Diffusion-Driven Synthesis of Parameterized Quantum Circuits for Efficient Hybrid Deep Learning Pipelines

👥 Ariel Christopher Wawolangi, Zeva Aji Satrio Nugroho
📈 Abstract: 4
📄 1-18
🗎 PDF: 0

Noise-Resilient Variational Quantum Transformers for Sequence Modeling in the NISQ Era

👥 Bambang Harimanto, Budhy Susanto
📈 Abstract: 5
📄 19-35
🗎 PDF: 0

Quantum Kernel-Aligned Regression for Data-Efficient Predictive Modeling in Semiconductor Manufacturing

👥 M Itmamul Wafa, Aulia Al-JIhad Safhadi
📈 Abstract: 4
📄 36-51
🗎 PDF: 0

Hybrid Quantum–Classical Convolutional Networks for Robust Multimodal Pattern Recognition

👥 Li Qingmei, Siti Zayyana Ulfah
📈 Abstract: 5
📄 52-68
🗎 PDF: 0
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