Program | CFP | Dates | Organization | Venue |
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.
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:
The list above is by no means exhaustive, as the aim is to foster the debate around all aspects of the suggested integration
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.
Be mindful of the following dates:
The accepted papers will appear on the workshop website and are included in the IEEE Quantum Week conference proceedings.
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 |
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 |
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 |