International Workshop on Quantum Computing and Reinforcement Learning (QCRL-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 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 30, 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

Session 1:

Time	Speaker(s)	Title
10:00 - 10:10	Samuel Yen-Chi Chen	Welcome and Introduction
10:10 - 10:30	Hsin-Yi Lin et al.	Quantum Reinforcement Learning by Adaptive Non-local Observables
10:30 - 10:50	Duy Do et al.	RELIC: Reinforcement Learning Based Ising Optimization via Graph Compression
10:50 - 11:10	Seok Bin Son et al.	Quantum Circuit Structure Optimization for Quantum Reinforcement Learning
11:10 - 11:30	Abdul Basit et al.	PennyCoder: Efficient Domain-Specific LLMs for PennyLane-Based Quantum Code Generation
11:30 - 12:00	Authors	Mini Panel Discussion

Session 2:

Time	Speaker(s)	Title
13:00 - 13:20	Chi-Sheng Chen et al.	Quantum Reinforcement Learning Trading Agent for Sector Rotation in the Taiwan Stock Market
13:20 - 13:40	Yen-Ku Liu et al.	Quantum-Enhanced Reinforcement Learning with LSTM Forecasting Signals for Optimizing Fintech Trading Decisions
13:40 - 14:00	Howard Su et al.	On Quantum BSDE Solver for High-Dimensional Parabolic PDEs
14:00 - 14:30	Authors	Mini Panel Discussion

Session 3:

Time	Speaker(s)	Title
15:00 - 15:20	Ding Lin et al.	A Sample-Efficient Quantum-Classical Reinforcement Learning Framework for Transmission Switching
15:20 - 15:40	Jesus Lopez et al.	Towards Quantum Machine Learning for Malicious Code Analysis
15:40 - 16:00	Hoang-Quan Nguyen et al.	QMoE: A Quantum Mixture of Experts Framework for Scalable Quantum Neural Networks
16:00 - 16:25	Authors	Mini Panel Discussion
16:25 - 16:30	Samuel Yen-Chi Chen	Closing

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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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