Abstract

More than eight years back we have made quantum computing available through the cloud for the first time. Since then, a continuous stream of technical breakthroughs and improvements on the hardware as well as on the software and algorithm level has been demonstrated building up a full stack quantum computer. Currently, they are conquering a place in the data and high-performance computing centers. Although the focus is typically on the scale, quality, and speed of the quantum hardware, it is imperative that the classical computing infrastructure underpinning quantum computing architectures be performant and reliable, in lockstep with quantum hardware improvements. Modularity will be key for scaling quantum hardware to eventually build an error-corrected quantum platform.

We focus on creating the tools and capabilities needed for the tight integration between scalable quantum and classical computing resources necessary to enable Quantum Centric Supercomputing and performant software capabilities. Moreover, as quantum platforms continue to mature, software needs to support the continued abstraction away from quantum circuits and operators, which is necessary for the widespread adoption of quantum computing as a computational resource.

In this presentation I will give an overview of what we have achieved across the quantum computing stack and share our extended roadmap to further advance the Era of Quantum Utility. We will discuss how the targeted technological improvements not only create new opportunities for large-scale applications with error mitigation, but also pave the way for future error-corrected systems.

Bio

Dr. Heike Riel is IBM Fellow, Head of Science & Technology and Lead of IBM Research Quantum Europe & Africa. She leads the research area of Science of Quantum & InformationTechnologies aiming to create scientific and technological breakthroughs in Quantum Computing and Technologies, Physics of Artificial Intelligence, Nanoscience and Nanotechnology and to explore new directions to computing.

She received the master’s in physics from the Friedrich-Alexander University of Erlangen-Nürnberg and the PhD in physics from University of Bayreuth and an MBA from Henley Business College. She has authored more than 155 peer-reviewed publications and filed more than 50 patents She has received several prestigious honors, e.g., elected member of the Leopoldina – German National Academy of Sciences, and the Swiss Academy of Engineering Sciences; she was awarded the APS David Adler Lectureship Award in the Field of Materials Physics, the Applied Physics Award of the Swiss Physical Society, and the 2022 IEEE Andrew S. Grove Award. She was honored as Fellow of the American Physical Society, and with an honorary doctorate by Lund University. In February 2022 she was elected to the National Academy of Engineering and in 2023 to Acatech.

Abstract

High-Performance Computing (HPC) has been at the heart of transformative technological progress, propelling innovations across scientific and industrial landscapes. This exponential growth of HPC—characterized by rapid increases in computational power, efficiency, and accessibility—has laid a powerful foundation for the advancement of Artificial Intelligence (AI) systems. Today, the convergence of HPC and AI is set to redefine possibilities, enabling AI models to not only process vast datasets with unprecedented speed but also achieve levels of precision and adaptability previously thought unreachable. As AI algorithms grow in complexity and data demands continue to surge, the scalability and computational intensity offered by HPC will be instrumental in addressing these needs. The future of AI-driven by HPC promises breakthroughs in diverse fields such as healthcare, climate science, finance, and beyond, where data-driven insights can lead to robust predictive models and real-time decision-making frameworks. This presentation will discuss how ADIA Lab can leverage this growth in HPC to pioneer innovative AI solutions, examining the evolving HPC landscape, its impact on AI research, and how these developments are poised to fuel a new era of exponential technological growth.

Bio

Dr. Horst Simon is an internationally recognized expert in the development of parallel computational methods for large-scale scientific challenges. Since 2023, he has been the director of ADIA Lab, where he leads initiatives that leverage artificial intelligence and computational science to address complex global issues. His extensive research background includes pioneering work in sparse matrix algorithms, large- scale eigenvalue problems, and domain decomposition algorithms. Dr. Simon’s recursive spectral bisection algorithm is regarded as a landmark achievement in parallel algorithms. With 40 years of experience across high-performance computing, numerical algorithms, and AI, Dr. Simon has contributed significantly to advancements in industry (Boeing, SGI), research labs (NASA Ames, Berkeley Lab), and academia (Stony Brook University, UC Berkeley).

Abstract

The digitalization of the economy coupled with the rapid advancement of AI, fueled by the success of large language models like ChatGPT, has dramatically increased energy consumption in data centers, leading to a surge in carbon emissions. This talk discusses a potential solution: converting data centers into flexible computing platforms that enable integration into power grid programs such as demand response or with behind-the-meter generation. This flexibility augments power usage without requiring new fossil-fuel infrastructure and facilitates more ambitious renewable deployment. However, the unique scale, operational constraints, and future projections of data centers present distinct challenges for implementing this vision. On the other hand, data centers, in contrast to other electricity consumers, already offer greater flexibility in power control and the potential for coordinated optimization with the grid. This intersection of challenges and capabilities opens avenues for designing intelligent solutions that dynamically adjust data center power usage in response to grid or on-site generation requirements while meeting user performance demands.

This talk discusses the opportunities as well as the myths inherent in this perspective on improving data center sustainability. Recent success stories have emerged in the development of intelligent solutions that can be swiftly implemented in data centers to accelerate participation in demand response. These solutions include power capping, load scheduling, load forecasting, market bidding, and collaborative optimization. This talk, by discussing the landscape of such solutions, offers insights into the journey towards making sustainable computing in data centers a reality.

Bio

Prof. Ayse K. Coskun  is a full professor at Boston University (BU) at the Electrical and Computer Engineering Department, where she leads the Performance and Energy Aware Computing Laboratory (PeacLab) to solve problems towards making computer systems more intelligent and energy-efficient. Coskun is also the Director of the Center for Information and Systems Engineering (CISE) at BU, a research center themed on intelligent systems. Coskun’s research interests intersect design automation, large-scale computer systems, and applied machine learning. Her research outcomes are culminated in several technical awards, including the IEEE CEDA Ernest Kuh Early Career Award, an IBM Faculty Award, and an IEEE TCAD Donald O. Pederson Best Paper Award. Coskun’s team has continuously collaborated with industry and government labs, and open-sourced impactful software tools to the community. Coskun currently serves as the Deputy Editor-in-Chief of the IEEE Transactions on Computer Aided Design. Coskun received her PhD degree in Computer Engineering from University of California San Diego and her BS degree in Microelectronics Engineering from Sabanci University.