At the GTC 2025 conference, Nvidia announced its plans for a new, Boston-based Nvidia Accelerated Quantum Research Center or NVAQC, designed to integrate quantum hardware with AI supercomputers. Expected to begin operations later this year, it will focus on accelerating the transition from experimental to practical quantum computing.

“We view this as a long-term opportunity,” says Tim Costa, Senior Director of Computer-Aided Engineering, Quantum and CUDA-X at Nvidia. “Our vision is that there will come a time when adding a quantum computing element into the complex heterogeneous supercomputers that we already have would allow those systems to solve important problems that can't be solved today.”

Quantum computing, like AI (i.e., deep learning) a decade ago, is yet another emerging technology with an exceptional affinity with Nvidia’s core product, the GPU. It is another milestone in Nvidia’s successful ride on top of the technological shift re-engineering the computer industry, the massive move from serial data processing (executing instructions one at a time, in a specific order) to parallel data processing (executing multiple operations simultaneously).

Over the last twenty years, says Costa, there were several applications where “the world was sure it was serial and not parallel, and it didn't fit GPUs. And then, a few years later, rethinking the algorithms has allowed it to move on to GPUs.” Nvidia’s ability to “diversify” from its early focus on graphics processing (initially to speed up the rendering of three-dimensional video games) is due to the development in the mid-2000s of its software, the Compute Unified Device Architecture or CUDA. This parallel processing programming language allows developers to leverage the power of GPUs for general-purpose computing.

The key to CUDA’s rapid adoption by developers and users of a wide variety of scientific and commercial applications was a decision by CEO Jensen Huang to apply CUDA to the entire range of Nvidia’s GPUs, not just the high-end ones, thus ensuring its popularity. This decision—and the required investment—caused Nvidia’s gross margin to fall from 45.6% in the 2008 fiscal year to 35.4% in the 2010 fiscal year.

“We were convinced that accelerated computing would solve problems that normal computers couldn’t. We had to make that sacrifice. I had a deep belief in [CUDA’s] potential,” Huang told Tae Kim, author of the recently published The Nvidia Way.

This belief continues to drive Nvidia’s search for opportunities where “we can do lots of work at once,” says Costa. “Accelerated computing is synonymous with massively parallel computing. We think accelerated computing will ultimately become the default mode of computing and accelerate all industries. That is the CUDA-X strategy.”

Costa has been working on this strategy for the last six years, introducing the CUDA software to new areas of science and engineering. This has included quantum computing, helping developers of quantum computers and their users simulate quantum algorithms. Now, Nvidia is investing further in applying its AI mastery to quantum computing.