| Program | CFP | Dates | Organization | Venue |
The integration of quantum computing (QC) and reinforcement learning (RL) represents a frontier of exploration in both fields, promising transformative advancements with profound implications across diverse domains. This workshop convenes experts from various backgrounds, including computer science, artificial intelligence/machine learning (AI/ML), and quantum information science, to delve into the intersection of QC and RL. Recent breakthroughs in QC and AI/ML have underscored the potential for synergistic collaboration, with RL showcasing remarkable success in solving sequential decision-making problems and QC offering novel computational paradigms. Our workshop aims to elucidate the current state-ofthe-art in quantum reinforcement learning and the application of classical RL techniques in addressing quantum computing challenges. By fostering interdisciplinary dialogue and knowledge exchange, we seek to identify immediate research opportunities and facilitate collaboration among researchers and practitioners from academia and industry. Our long-term vision is to establish enduring partnerships that accelerate innovation and exploration at the interface of QC and RL, driving forward the development of quantum-enhanced decision-making algorithms and unlocking new frontiers in quantum computing applications. Join us as we embark on this journey to harness the potential of combining 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 should be formatted according to the IEEE transactions format, 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 be peer-reviewed in a double-blind process and assessed based on their novelty, technical quality, potential impact, clarity, and reproducibility (when applicable). The workshop submissions will be handled by EasyChair (the submission link will be updated).
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 - 10:15 | Samuel Yen-Chi Chen | Welcome and Introduction |
| 10:15 - 10:40 | Michael Kölle et al. | Optimizing Variational Quantum Circuits Using Metaheuristic Strategies in Reinforcement Learning |
| 10:40 - 11:05 | Daniel Kent et al. | Using Quantum Solved Deep Boltzmann Machines to Increase the Data Efficiency of RL Agents |
| 11:05 - 11:30 | Siddhant Dutta et al. | QADQN: Quantum Attention Deep Q-Networks For Financial Market Prediction and Trading |
| Time | Speaker(s) | Title |
|---|---|---|
| 13:00 - 13:25 | André Sequeira et al. | Trainability Issues in Quantum Policy Gradients with Softmax Activations |
| 13:25 - 13:50 | Chen-Yu Liu et al. | QTRL: Toward Practical Quantum Reinforcement Learning via Quantum-Train |
| 13:50 - 14:15 | Joaquim M. Gaspar et al. | Entanglement-enhanced Quantum Reinforcement Learning: An Application Using Single-Photons |
| Time | Speaker(s) | Title |
|---|---|---|
| 15:00 - 15:25 | Mohammad Walid Charrwi et al. | Quantum Circuit Partitioning for Scalable Noise-aware Quantum Circuit Re-Synthesis |
| 15:25 - 15:50 | James Holliday et al. | Hybrid Quantum Tabu Search for Solving the Vehicle Routing Problem |
| 15:50 - 16:15 | Xuan Bac Nguyen et al. | QClusformer: A Quantum Transformer-based Framework for Unsupervised Visual Clustering |
| 16:15 - 16:30 | Samuel Yen-Chi Chen | Closing |