IEEE Quantum Week Workshop on

International Workshop on

Quantum Computing and

Reinforcement Learning

(QCRL)

2025

co-located with IEEE Quantum Week 2025

QCRL @ IEEE Quantum Week 2025, ABQ Convention Center, Albuquerque, New Mexico, USA

Introduction

The integration of quantum computing (QC) and reinforcement learning (RL) stands at the forefront of scientific exploration, promising transformative advancements with far-reaching implications across numerous domains. This workshop brings together experts from diverse fields—including computer science, artificial intelligence/machine learning (AI/ML), and quantum information science, to explore the rich intersection of QC and RL. Recent breakthroughs in both quantum technologies and AI/ML have highlighted the potential for synergistic collaboration, with RL demonstrating remarkable success in sequential decision-making tasks and QC introducing novel computational paradigms. Our workshop aims to provide a comprehensive overview of the current state-of-the-art in quantum reinforcement learning, as well as explore how classical RL techniques can be leveraged to address challenges in quantum computing. By fostering interdisciplinary dialogue and facilitating knowledge exchange, we seek to identify immediate research opportunities and promote collaboration among researchers and practitioners from academia and industry alike. Looking ahead, our long-term vision is to cultivate lasting partnerships that accelerate innovation at the convergence of QC and RL. Through these collaborations, we aim to drive the development of quantum-enhanced decision-making algorithms and unlock new frontiers in quantum computing applications. We invite you to join us in this endeavor to harness the combined potential of QC and RL for the advancement of science, technology, and society.

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Call For Papers

Topics

In this workshop, we invite the research community in quantum information science and reinforcement learning/artificial intelligence to submit works related to the proposed integration of quantum computing and reinforcement learning/ artificial intelligence, revolving around the following topic areas:

• Theoretical studies of quantum reinforcement learning algorithms
• Challenges in quantum reinforcement learning in the NISQ era
• Quantum reinforcement learning under the influences of quantum devices noise
• Quantum reinforcement learning in the context of trustworthy ML (e.g. adversarial attacks, federated learning)
• Quantum reinforcement learning for scientific discovery, commercial and industrial applications
• Multi-Agent quantum reinforcement learning
• Quantum reinforcement learning for reasoning
• Benchmarking QRL algorithms
• Reinforcement learning for quantum control, quantum error correction, quantum error mitigation
• Reinforcement learning for quantum architecture search, optimization, and program synthesis
• Hybrid RL-QC systems design

The list above is by no means exhaustive, as the aim is to foster the debate around all aspects of the suggested integration

Submission

Guidelines

Papers must be formatted according to the IEEE Transactions format and limited to six (6) pages, including references. We welcome submissions across the full spectrum of theoretical and practical work, including research ideas, methods, tools, simulations, applications or demos, practical evaluations, and surveys. All papers will undergo a double-blind peer-review process and will be evaluated based on novelty, technical quality, potential impact, clarity, and reproducibility (where applicable). Submissions will be managed via EasyChair.

Important Dates

Be mindful of the following dates:

• Paper Submission Due Date: June 2, 2025
• Notification of Paper Acceptance: July 23, 2025
• Camera-ready Papers Due: TBA
• Conference Date: Aug 31- Sep 5, 2025

Proceedings

The accepted papers will appear on the workshop website and are included in the IEEE Quantum Week conference proceedings.

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Workshop Program

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Organization

Organizing Committee

• Samuel Yen-Chi Chen, Wells Fargo Bank, USA
• Joongheon Kim, Korea University, Korea
• Soohyun Park, Sookmyung Women’s University, Korea
• Huan-Hsin Tseng, Brookhaven National Laboratory, USA
• Fan Chen, Indiana University Bloomington, USA
• Qiang Guan, Kent State University, USA
• Ying Mao, Fordham University, USA
• Weiwan Jiang, George Mason University, USA
• Muhammad Ismail, Tennessee Technological University, USA
• Khoa Luu, University of Arkansas, Fayetteville, USA
• Zhiding Liang, Rensselaer Polytechnic Institute, USA
• Prayag Tiwari, Halmstad University, Sweden
• Muhammad Usman, CSIRO's Data61, Melbourne, Australia
• Jun Zhuang, Boise State University, USA
• Mahdi Chehimi, American University of Beirut, Lebanon

Program Committee

• Shinjae Yoo, Brookhaven National Laboratory, USA
• Jun Qi, Hong Kong Baptist University, Hong Kong
• Chao-Han Huck Yang, NVIDIA, Taiwan
• Pin-Yu Chen, IBM Research, USA
• Andrea Skolik, QuiX Quantum GmbH, Germany
• Su Fong Chien, MIMOS Berhad, Malaysia
• Nico Meyer, Fraunhofer IIS, Germany
• Kuan-Cheng Chen, Imperial College London, UK
• Chen-Yu Liu, National Taiwan University, Taiwan
• Yen-Jui Chang, Chung Yuan Christian University, Taiwan

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Venue

How to Reach the Venue

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