International Workshop on Quantum Computing and Reinforcement Learning (QCRL-2026)

QCRL @ IEEE Quantum Week 2026, Metro Toronto Convention Centre Toronto, Ontario, Canada

Introduction

As quantum computing enters a new phase of hardware-aware algorithm design and AI-driven automation, reinforcement learning is emerging as a key paradigm for both quantum-enhanced decision-making and the control of quantum systems themselves. QCRL 2026 will convene researchers from quantum computing, artificial intelligence, machine learning, and quantum engineering to explore this two-way frontier. Topics of interest include quantum reinforcement learning, hybrid quantum-classical agents, reinforcement learning for quantum control, calibration, compilation, mapping, qubit reuse, error correction and mitigation, RL-driven quantum architecture search, benchmark design, reproducibility, and application-driven studies in areas such as scientific discovery, critical infrastructure, cybersecurity, and finance. The workshop will provide a timely forum for aligning algorithmic advances with the realities of near-term quantum hardware and experimental constraints. The goal of the workshop is not only to showcase recent advances, but also to define the next generation of research challenges at the intersection of learning, quantum hardware, and scalable system design. By fostering interdisciplinary exchange across theory, software, and experimentation, QCRL 2026 seeks to accelerate the development of robust, testable, and practically relevant intelligent quantum technologies.

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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 foundations and algorithmic advances in quantum reinforcement learning
Hybrid quantum-classical reinforcement learning agents and systems
Quantum reinforcement learning in noisy, dynamic, and resource-constrained settings
Benchmarking, reproducibility, and evaluation of QRL algorithms
Quantum reinforcement learning for reasoning, planning, and sequential decision-making
Multi-Agent quantum reinforcement learning
Quantum reinforcement learning for scientific discovery and real-world applications
Reinforcement learning for quantum control, calibration, and feedback
Reinforcement learning for quantum error correction and quantum error mitigation
Reinforcement learning for quantum circuit compilation, mapping, transpilation, and qubit reuse
Reinforcement learning for quantum architecture search, circuit optimization, and program synthesis
Quantum or classical reinforcement learning for quantum sensing and quantum communication
Hardware-aware learning methods for near-term and experimental quantum platforms
Trustworthy, robust, and secure QRL, including adversarial settings and distributed/federated scenarios
RL-enabled intelligent quantum system design across the quantum computing stack

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 4 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 single-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:

Workshop paper abstract due: June 22, 2026
Full workshop paper due: June 29, 2026
Workshop paper acceptance notification: July 20, 2026
Workshop paper author registration: July 27, 2026
Final workshop paper for proceedings due: July 27, 2026
Workshop day: Sept 18, 2026

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

All times are in Eastern Daylight Time (EDT).

Each contributed presentation is allocated 12 minutes, consisting of a 10-minute presentation followed by 2 minutes of questions. The invited talk is allocated 35 minutes, including questions.

Session I: QRL Foundations, Feedback Control, and Autonomous Decision-Making

Time	Speaker(s) / Author(s)	Presentation
10:00–10:05 AM	Samuel Yen-Chi Chen	Opening Remarks
10:05–10:40 AM	Invited Speaker: Junghoon Justin Park (Seoul National University)	Invited Talk: Quantum Reinforcement Learning in Behavioral Cognitive Science
10:40–10:52 AM	Priyanshi Singh and Krishna Bhatia	Continuous Quantum Feedback Control via Kraus-Parameterized Belief Reinforcement Learning
10:52–11:04 AM	Hoyeong Lee and Joongheon Kim	Frequency-Domain Representation for Quantum Reinforcement Learning in Control Tasks
11:04–11:16 AM	Muhammad Mahad Khaliq, Srikar Alla, Ali Shiri Sichani, Mert Korkali, Hadi Akbarpour, Chi-Ren Shyu, and Vashisth Kumar P. Pulluri	Offensive Quantum Reinforcement Learning for Stealthy False Data Injection Attack Generation in Power Grids
11:16–11:28 AM	Emily Jimin Roh, Hyojun Ahn, Soohyun Park, and Joongheon Kim	LUCID-Q: Language-Guided Uncertainty-Aware Control with Integrated Decision-Making using Quantum Reinforcement Learning for Autonomous Mobility
11:28–11:30 AM	—	Transition and Contingency Buffer

Session II: Reinforcement Learning for Quantum Codes, Design Automation, and Simulation

Time	Speaker(s) / Author(s)	Presentation
1:00–1:12 PM	Vahid Nourozi, David Mitchell, and Toshiaki Koike-Akino	Q-Learning Search over Voltage-Labeled Covers for Weight-Six Bivariate-Bicycle Quantum LDPC Codes
1:12–1:24 PM	Mohsen Moradi, Vahid Nourozi, Taejoon Kim, Remi Chou, and David Mitchell	High-Performance Reinforcement-Learned Belief-Propagation Decoding of Quantum LDPC Codes
1:24–1:36 PM	Vahid Nourozi, David Mitchell, and Toshiaki Koike-Akino	Reinforcement-Learning-Guided Multi-Branch Decoding of Quantum LDPC Codes
1:36–1:48 PM	Mohsen Moradi, Taejoon Kim, and Remi Chou	Conflict-Free Color-Clustered Sequential Belief-Propagation Decoding of Quantum LDPC Codes via Reinforcement Learning
1:48–2:00 PM	Owen Friedewald, Ali Shiri Sichani, and Chi-Ren Shyu	Shielded RL for Route-Charged Parity-Term Ordering in QEDA Phase Components
2:00–2:12 PM	Vinitha Balachandran, Liwei Yang, Kai Yong Andy Tan, Zhehui Wang, Nitin Shivaraman, and Tao Luo	Learning Transferable Tensor Network Contraction Policies for Quantum Circuit Simulation
2:12–2:25 PM	Session Speakers	Moderated Discussion: RL for Quantum-System Engineering
2:25–2:30 PM	—	Contingency Buffer

Session III: Adaptive Quantum Learning Architectures and Applications

Time	Speaker(s) / Author(s)	Presentation
3:00–3:12 PM	Nicholas Jeon, Paul Baity, Anuj Nayak, Lav Varshney, Peter Love, Kristofer Reyes, Huan-Hsin Tseng, Adolfy Hoisie, and Byung-Jun Yoon	Bayesian Active Learning for Sample-Efficient and Symmetry-Aware Qubit Layout Optimization
3:12–3:24 PM	Yash Tomar and Dheeraj Peddireddy	SymQNet: Amortized Acquisition for Low-Latency Adaptive Hamiltonian Learning
3:24–3:36 PM	Seojin Yoon and Soohyun Park	Selective Aggregation for Quantum Federated Learning with Dynamic QNN Architecture
3:36–3:48 PM	Shih-Lung Yu, Ming-Kang Ho, Tai-Yue Li, and Sheng Yun Wu	Scaling Adaptive Non-Local Observable Quantum Super-Resolution via Matrix Product States
3:48–4:00 PM	Ming-Kai Hung, Jun-Hao Chen, Yun-Cheng Tsai, and Samuel Yen-Chi Chen	Titans-QFWP: A Regime-Aware Hybrid Quantum Fast Weight Programmer for Portfolio Optimization
4:00–4:12 PM	Howard Su, Huan-Hsin Tseng, Chi-Sheng Chen, and Lance Bai	Quantum Transformer BSDE Solver via Multi-Layer Fully-Connected Variational Quantum Circuits
4:12–4:25 PM	Session Speakers	Moderated Discussion: Adaptive and Sequential Quantum Learning
4:25–4:30 PM	Samuel Yen-Chi Chen	Closing Remarks

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Organization

Steering Committee

Shinjae Yoo, Brookhaven National Laboratory, USA
Saif Al-Kuwari, Hamd Bin Khalifa University, Qatar

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, The Chinese University of Hong Kong, Hong Kong
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
Alberto Marchisio, New York University Abu Dhabi, UAE

Program Committee

Jun Qi, Hong Kong Baptist University, Hong Kong
Chao-Han Huck Yang, NVIDIA, Taiwan
Pin-Yu Chen, IBM Research, USA
Su Fong Chien, MIMOS Berhad, Malaysia
Nico Meyer, Fraunhofer IIS, Germany
Kuan-Cheng Chen, Hamd Bin Khalifa University, Qatar
Chen-Yu Liu, National Taiwan University, Taiwan
Yen-Jui Chang, Chung Yuan Christian University, Taiwan
Yun-Cheng Tsai, National Taiwan Normal University, Taiwan
Junghoon Justin Park, Seoul National University, Korea
Yifeng Peng, Stevens Institute of Technology, USA

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Venue

How to Reach the Venue

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