As world-renowned experts in quantum computing and artificial intelligence (AI) technologies, faculty from Rice University’s George R. Brown School of Engineering and Computing are leading the way in shaping the future of these technologies. They recently organized two workshops to explore recent advancements and future opportunities and applications in quantum computing, machine learning, and AI.
The first event, the Quantum Information Processing Systems (QuantIPS 2025) summit, took place on April 28-29 at Rice University’s Ralph S. O’Connor Building for Engineering and Science. It was organized by Rice engineering professors—Anastasios Kyrillidis, Nai-Hui Chia, Shengxi Huang, and Tirthak Patel—who form the core of QuanTAS, a Ken Kennedy Institute research group pushing the boundaries of quantum optimization, algorithms, and systems.
The two-day event brought together computer scientists, physicists, chemists, and electrical engineers from esteemed universities and research institutions across the US. A core tenet of QuantIPS is to bridge the often-perceived gap between abstract developments in theoretical quantum physics, novel experimental insights, and practical engineering designs. The goal is to enable efficient, powerful, and secure quantum computing systems that can transform fields such as machine learning, materials science, and drug discovery. In keeping with this mission, the attendees discussed the latest breakthroughs in novel quantum algorithmic designs and applications. They also identified emergent opportunities and collaboratively strategized to address some of the most urgent computational challenges in quantum information science and computing.
Key takeaways from QuantIPS 2025:
Identifying Promising New Avenues: Discussions pinpointed several high-potential directions for quantum algorithm development, particularly in optimization, quantum systems design, simulation, and other practices related to quantum computing.
Emphasis on Co-Design: A strong theme emerged around the necessity of co-designing quantum hardware and software to maximize near-term quantum advantage.
Future Vision: In the past two years, QuantIPS has laid a strong foundation and provided an important platform for vital dialogues on the future vision for emerging quantum technologies and applications.
“QuantIPS 2025 was a critical confluence of minds,” said Kyrillidis, associate professor of computer science and a lead organizer. “The field of quantum information processing is experiencing a surge of innovation, from foundational theory to experimental realization. Bringing together this diverse cohort of experts here at Rice allows us to synergize efforts, bridge theoretical advancements with practical applications, and collectively accelerate research towards practical quantum computing. Rice is exceptionally proud to be a central hub for these pivotal discussions, fostering the collaborations that will define the future of quantum technology and AI.”
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The second event, the Texas Colloquium on Distributed Learning (TL;DR 2025), took place on May 1-2, at the Ralph S. O’Connor Building for Engineering and Science. This two-day colloquium brought together roughly 60 national and international computer scientists and machine learning researchers from industry and academia. The colloquium delved deep into the latest advances in efficient distributed learning algorithms, charting the future of training and deploying massive AI models on cost-effective and specialized computing infrastructure.
This event was organized by Rice engineering professors—Anastasios Kyrillidis, César A. Uribe, Sebastian Perez-Salazar, Santiago Segarra, and Arlei Silva. Together, they form the core team of the Ken Kennedy Institute’s AI-OWLS (AI, Optimization, Wireless, and Learning Systems) group, an academic hub known for developing state-of-the-art open-source algorithms for machine learning and AI. Their work is driven by innovative optimization, efficient model engineering, and robust distributed systems design.
Presentations underscored recent breakthroughs in both the theory and application of AI agents, with discussions revolving around cutting-edge optimization techniques, privacy-preserving methodologies, federated learning, and the importance of building trustworthy, reliable, and robust AI systems.
Key takeaways and outcomes from TL;DR 2025:
Spotlight on Novel Algorithms: The colloquium highlighted several novel algorithms and frameworks that promise greater efficiency, scalability, and resource-consciousness for training large-scale models.
Focus on Foundation Models: Significant attention was devoted to the challenges and opportunities in training and fine-tuning foundation models in distributed settings, including issues of data heterogeneity and communication bottlenecks.
Open-Source and Collaboration: There was a strong consensus on the critical role of open-source tools, standardized benchmarks, and collaborative platforms in accelerating progress and fostering reproducibility in distributed learning.
Building Momentum: The exciting ideas and insights exchanged at this conference have set a vibrant agenda for future research collaborations and community engagement.
“TL;DR 2025 was designed to address the challenge and opportunity of scaling artificial intelligence through techniques like optimization, distributed training and use of AI agents,” said Kyrillidis. “As AI models, particularly foundational models, grow exponentially in size and complexity, distributed learning is no longer a niche technique but an absolute necessity. This colloquium provided a platform for leading experts to share insights into algorithmic efficiency, innovative system design, and the crucial aspects of privacy, security, and trustworthiness in distributed AI.”
Rice engineering graduate students—David Quiroga, Ria Stevens, Jasper Liao, Carlos Taveras, Jhojan Rodriguez, and Yamin Zhou—assisted the faculty in organizing the workshops.
Both workshops were supported by the Ken Kennedy Institute and generously funded by Rice University through the Conference and Workshop Development Fund. TL;DR 2025 also received valued additional funding from the Rice Engineering Innovation Conference and Workshop Fund.