The MIT-IBM Computing Research Lab launches to shape the future of AI and quantum computing

Published: (April 29, 2026 at 06:00 AM EDT)
6 min read

Source: MIT News - AI

The following is a joint announcement by the MIT Schwarzman College of Computing and IBM.

IBM and MIT today announced the launch of the MIT‑IBM Computing Research Lab, advancing their long‑standing collaboration to shape the next era of computing. The new lab expands its scope to include quantum computing, alongside foundational artificial‑intelligence research, with the goal of unlocking new computational approaches that go beyond the limits of today’s classical systems.

The MIT‑IBM Computing Research Lab builds on a distinguished history of scientific excellence at the intersection of research and academia. Evolving from the MIT‑IBM Watson AI Lab, which originated in 2017 on MIT’s campus, the new lab reflects a transformed technology landscape—one in which AI has entered mainstream deployment and quantum computing is rapidly advancing toward practical impact. Together, MIT and IBM aim to help lead research in AI and quantum and to redefine mathematical foundations across both domains.

“We expect the MIT‑IBM Computing Research Lab to emerge as one of the world’s premier academic and industrial hubs accelerating the future of computing,” says Jay Gambetta, director of IBM Research, IBM Fellow, and IBM chair of the MIT‑IBM Computing Research Lab. “Together, the brightest minds at MIT and IBM will rethink how models, algorithms, and systems are designed for an era that will be defined by the sum of what’s possible when AI and quantum computing come together.”

“For a decade, the collaboration between MIT and IBM has produced leading‑edge research and innovation, and provided mentorship and supported the professional growth of researchers both at MIT and IBM,” says Anantha Chandrakasan, MIT’s provost, who, as then‑dean of the School of Engineering, spearheaded the creation of the MIT‑IBM Watson AI Lab and will continue as MIT chair of the lab. “The incredible technical achievements set the bar high for our work together over the next 10 years. I look forward to another decade of impact.”

Addressing the next frontiers in computation

The MIT‑IBM Computing Research Lab will serve as a focal point for joint research between MIT and IBM in AI, algorithms, and quantum computing, as well as the integration of these technologies into hybrid computing systems. The lab is designed to accelerate progress toward powerful new computational approaches that take advantage of rapid advances in AI and quantum‑centric supercomputing, including those that combine maturing quantum hardware with classical systems and advanced AI methods.

Key research themes

  • AI‑centric advances – improving capabilities and integrating AI with traditional computing; developing small, efficient, modular language‑model architectures; exploring novel AI computing paradigms; and building enterprise‑focused AI systems for real‑world deployment where reliability, transparency, and trust are essential.
  • Quantum‑centric breakthroughs – rethinking the mathematical and algorithmic foundations that underpin the next era of computing; accelerating the development of novel quantum algorithms for complex problems with impacts in materials science, chemistry, and biology.
  • Cross‑disciplinary foundations – investigating the mathematical and algorithmic bases of machine learning, optimization, Hamiltonian simulations, and partial differential equations used to approximate dynamical‑system behaviors that currently stump classical systems. Potential implications span more accurate weather and turbulence prediction, better financial‑market forecasts, lower‑risk finance, protein‑structure prediction for targeted medicine, and streamlined global supply chains.

The lab will complement and enhance two of MIT’s strategic initiatives—the MIT Generative AI Impact Consortium and the MIT Quantum Initiative—both launched by President Sally Kornbluth to broaden MIT’s impact on serious global challenges. It will also leverage IBM’s longtime leadership in quantum computing; IBM has outlined a roadmap to deliver the world’s first fault‑tolerant quantum computer by 2029 and is integrating quantum hardware with high‑performance computing and AI accelerators to solve the world’s toughest problems.

Deep integration with scientific domains

The MIT‑IBM Computing Research Lab will continue to serve as a foundation for training the next generation of computational scientists and innovators by engaging faculty and students across MIT departments, enabling new computational approaches to accelerate discoveries in the physical and life sciences.

Leadership

  • Co‑directors

    • Aude Oliva, senior research scientist, MIT Computer Science and Artificial Intelligence Laboratory (CSAIL)
    • David Cox, vice president of AI Foundations, IBM Research
  • Focus‑area leads

    AI

    • Jacob Andreas, associate professor, Department of Electrical Engineering and Computer Science (EECS)
    • Kenney Ng, principal research scientist, IBM Research; MIT‑IBM science program manager

    Algorithms

    • Vinod Vaikuntanathan, Ford Foundation Professor of Engineering, EECS
    • Vasileios Kalantzis, senior research scientist, IBM Research

    Quantum

    • Aram Harrow, professor of physics, MIT
    • Hanhee Paik, IBM director, Quantum Algorithm Centers

These leaders will guide the lab’s three focus areas—AI, algorithms, and quantum—ensuring a cohesive, interdisciplinary effort that pushes the boundaries of computation.

“T‑IBM Computing Research Lab reflects an important expansion of the collaboration between MIT and IBM and the increasing connections across AI, algorithms, and quantum. This deepened focus also underscores a strong alignment with the MIT Schwarzman College of Computing’s mission to advance the forefront of computing and its integration across disciplines,” says Dan Huttenlocher, dean of the MIT Schwarzman College of Computing and MIT co‑chair of the lab. “I’m excited about what this next chapter will enable in these three areas, and their impact broadly.”

Building on nearly a decade of collaboration

The MIT‑IBM Watson AI Lab helped pioneer a model for academic‑industry research collaboration, aligning long‑term scientific inquiry with real‑world impact. Since its inception, the lab has:

  • Funded over 210 research projects involving more than 150 MIT faculty members and over 200 IBM researchers.
  • Produced more than 1,500 peer‑reviewed articles.
  • Supported the career growth of a large number of MIT students and junior researchers, funding more than 500 students and postdocs.

“The true measure of this lab is not just innovation, but transformation of a field. Hundreds of students have contributed to thousands of publications in top conferences and journals, demonstrating their capabilities to address meaningful problems,” says Oliva.
“The MIT‑IBM Computing Research Lab builds on an extraordinary legacy of impact to advance a trusted collaboration that will redefine the future of AI and quantum computing in a way never seen before.”

“By coupling academic rigor with industrial scale, the lab aims to define the computational foundations that will power the next generation of AI, quantum, and scientific breakthroughs,” says Cox.
“By bringing together advances in AI, algorithms, and quantum computing under one integrated research effort, we’re creating the conditions to rethink the mathematical and computational foundations of science and engineering.”

The MIT‑IBM Computing Research Lab will capitalize on this foundation, expanding both the scientific scope and the ecosystem of collaborators across the Cambridge‑Boston region and beyond.

0 views
Back to Blog

Related posts

Read more »

Cybersecurity in the Intelligence Age

Action Plan Overview Artificial intelligence is reshaping cybersecurity. The same capabilities that help defenders identify vulnerabilities, automate remediati...

[Paper] Recursive Multi-Agent Systems

Recursive or looped language models have recently emerged as a new scaling axis by iteratively refining the same model computation over latent states to deepen ...