Artificial Intelligence, Partnerships

IBM and Indian Institutes boost AI and quantum research

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October 3, 2026 by Ivy Delaney

IBM is intensifying its research partnerships with the Indian Institute of Technology Bombay and the Indian Institute of Science to focus on advancements in artificial intelligence and quantum computing. Building on a collaboration that began in 2018, IBM researchers are now concentrating on “sovereign and Indic language model adaption” with IIT Bombay, aiming to improve AI capabilities for Indian languages.

Simultaneously, a collaboration initiated with IISc in 2021 is specifically targeting autonomous AI agents designed to manage complex tasks. “The next wave of computing will be shaped by advances in agentic AI, sovereign AI, and quantum computing,” says Dr. Amith Singhee, Director, IBM Research India and CTO, IBM India and South Asia, as the companies work to create practical technologies benefiting enterprises and society.

IIT Bombay Focus: Sovereign AI and Multimodal Systems

The IBM-IIT Bombay collaboration is actively refining techniques to improve the efficiency of adapting AI models for Indian languages. This work centers on “sovereign and Indic language model adaptation,” a critical area for ensuring AI technologies are accessible and effective across diverse linguistic landscapes. Researchers are not simply translating existing models. They are optimizing them to better understand the nuances of Indian languages, addressing a key challenge in global AI development.

The goal is to create AI systems that can accurately process and generate text in multiple Indian languages, encouraging inclusivity and broader adoption. Beyond language adaptation, the partnership is heavily invested in developing multimodal AI systems with applications in software programming education and intelligent operations. These systems aim to integrate information from various sources, text, images, audio, to create a more comprehensive and intuitive user experience.

This focus extends to hybrid cloud environments, suggesting a strategy for deploying AI capabilities across distributed computing infrastructure. The collaborative effort intends to strengthen human-AI collaboration, allowing for more effective teamwork between people and intelligent systems. A core component of the IIT Bombay research involves enhancing intelligent knowledge retrieval through optimized AI runtimes and distributed inference. This technical focus aims to improve the speed and scalability of large language models, enabling faster and more accurate access to enterprise knowledge.

Optimizing AI runtimes helps reduce computational costs and improve the responsiveness of AI applications. “The power of AI is already being demonstrated in systems that can seamlessly understand and reason across languages, modalities and domains,” said Ganesh Ramakrishnan, Institute Chair Professor Science and Engineering, IIT Bombay.

“Our joint efforts with IBM Research are laying the foundation for such intelligent systems through advances in multimodal AI models, knowledge retrieval, optimization techniques, agentic AI workflows, AI-assisted software engineering and education.” The collaboration’s scope extends beyond immediate performance gains, with a long-term vision of creating trustworthy, inclusive and scalable AI technologies. By combining expertise in AI, language, knowledge and intelligent computing, the researchers hope to transform how people learn, work, and access information, not only in India but globally.

This ambitious goal emphasises the potential of collaborative research to address complex challenges and drive innovation in the field of artificial intelligence, building AI systems that are both powerful and responsible and ensuring that the benefits of this technology are shared by all.

The next wave of computing will be shaped by advances in agentic AI, sovereign AI, and quantum computing.

Dr. Amith Singhee, Director, IBM Research India and CTO, IBM India and South Asia
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Ivy Delaney

Ivy Delaney has been working with neural networks and machine learning since the mid-nineties, back when a couple of hidden layers and a long afternoon of training counted as ambitious. She has watched the field go from academic curiosity to the thing quietly running underneath everything, and she brings that long view to quantum computing. For Quantum Zeitgeist she covers the ground where the two fields meet. That means quantum machine learning and the variational algorithms it leans on, and it also means the less glamorous but more interesting story of classical machine learning already doing real work inside quantum machines, decoding error-correcting codes, calibrating noisy hardware and learning the error models that simulators depend on. She writes about the hardware those algorithms have to run on too, and about the post-quantum cryptography scramble that the same hardware has set off. Her stories typically start with the paper, whether that is peer-reviewed work, conference proceedings or an arXiv preprint, with the source linked so you can hold a claim up against the research it came from. She is unimpressed by benchmarks that will not say what they beat, and by demonstrations that only work in the press release.

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