Quantum neural models and reinforcement learning are rapidly converging into a new research frontier-one that unites quantum computation, sequential decision-making, and expressive neural architectures. The QNRL Workshop invites contributions that push this boundary: quantum RL algorithms, quantum-enhanced neural representations, hybrid classical–quantum agents, and RL-based optimization of quantum systems and circuits. Our goal is to catalyze a global research community that accelerates the development of scalable, noise-resilient, and practically deployable quantum AI.

Submission Guidelines

All papers must be original and not simultaneously submitted to another journal or conference. The following paper categories are welcome:

• Full papers (12 - 15+ pages) Full papers should present original, high-quality, and previously unpublished research contributions in quantum reinforcement learning and related areas. Submissions are expected to provide substantial theoretical insights, methodological innovations, or comprehensive experimental evaluations. Full papers will undergo a rigorous peer-review process and, if accepted, will be included in the conference proceedings.
• Short papers (6-11 pages) Short papers are intended for reporting novel ideas, preliminary results, emerging research directions, or innovative applications that may not yet be fully mature but demonstrate significant potential. Short papers will also be peer-reviewed and published in the conference proceedings.

Please use Springer conference proceeding templates as shown here: 

https://www.springer.com/gp/computer-science/lncs/conference-proceedings-guidelines

List of Topics

• Quantum neural architectures (variational, recurrent, convolutional); hybrid quantum--classical models; embedding/encoding strategies; optimization and trainability.
• Quantum reinforcement learning: on-policy/off-policy learning; model-based QRL; hierarchical and meta-QRL; multi-agent and federated/distributed QRL.
• Trustworthy QN&RL: robustness, adversarial resilience, safety, privacy, and interpretability.
• RL for quantum systems: quantum control, error correction/mitigation, compilation/scheduling, architecture/program synthesis.
• Systems and benchmarks: simulators, hardware-aware training, realistic noise; datasets, protocols, and metrics for fair comparison.
• Applications in science/engineering, communications/6G, finance, robotics, healthcare, and complex systems/critical infrastructures.

Committees

Organizing committee

• Samuel Yen-Chi Chen, Wells Fargo Bank, USA
• Joongheon Kim, Korea University, Korea
• Huan-Hsin Tseng, Brookhaven National Laboratory, USA
• Fan Chen, Indiana University Bloomington, USA
• Prayag Tiwari, Halmstad University, Sweden
• Alberto Marchisio, New York University Abu Dhabi, UAE
• Kuan-Cheng Chen, Imperial College London/Jij Europe, UK
• Kumar Venayagamoorthy, Clemson University, USA

Invited Speakers

• Prof. Dr. Muhammad Shafique, New York University Abu Dhabi, UAE
• Prof. Oleksandr Kyriienko, University of Sheffield, UK

Publication

QNRL@WCCI2026 proceedings will be published by Springer Lecture Notes in Computer Science (LNCS).

Venue

The conference will be held in Maastricht Exhibition & Congress Center (MECC), Maastricht, The Netherlands

Contact

All questions about submissions should be emailed to Dr. Samuel Yen-Chi Chen (ycchen1989 (at) ieee.org)