TOPSHOT – Nvidia CEO Jensen Huang delivers a keynote address at the Consumer Electronics Show (CES) … More in Las Vegas, Nevada on January 6, 2025. (Photo by Patrick T. Fallon \/ AFP) (Photo by PATRICK T. FALLON\/AFP via Getty Images)<\/p>\n
AFP via Getty Images<\/p>\n
At the GTC 2025 conference, Nvidia announced<\/a> 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.<\/p>\n \u201cWe view this as a long-term opportunity,\u201d says Tim Costa, Senior Director of Computer-Aided Engineering, Quantum and CUDA-X at Nvidia. \u201cOur 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.\u201d<\/p>\n Quantum computing, like AI (i.e., deep learning) a decade ago, is yet another emerging technology with an exceptional affinity with Nvidia\u2019s core product, the GPU. It is another milestone in Nvidia\u2019s 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).<\/p>\n Over the last twenty years, says Costa, there were several applications where \u201cthe 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.\u201d Nvidia\u2019s ability to \u201cdiversify\u201d 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.<\/p>\n The key to CUDA\u2019s 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\u2019s GPUs, not just the high-end ones, thus ensuring its popularity. This decision\u2014and the required investment\u2014caused Nvidia\u2019s gross margin to fall from 45.6% in the 2008 fiscal year to 35.4% in the 2010 fiscal year.<\/p>\n \u201cWe were convinced that accelerated computing would solve problems that normal computers couldn\u2019t. We had to make that sacrifice. I had a deep belief in [CUDA\u2019s] potential,\u201d Huang told Tae Kim, author of the recently published The Nvidia Way.<\/p>\n This belief continues to drive Nvidia\u2019s search for opportunities where \u201cwe can do lots of work at once,\u201d says Costa. \u201cAccelerated 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.\u201d<\/p>\n 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<\/a> in applying its AI mastery to quantum computing.<\/p>\n Nvidia became one of the world\u2019s most valuable companies because the performance of the artificial neural networks at the heart of today\u2019s AI depends on the parallelism of the hardware they are running on, specifically the GPU\u2019s ability to process many linear algebra multiplications simultaneously. Similarly, the basic units of information in quantum computing, qubits, interact with other qubits, allowing for many different calculations to run simultaneously.<\/p>\n Combining quantum computing and AI<\/a> promises to improve AI processes and practices and, at the same time, escalate the development of practical applications of quantum computing. The focus of the new Boston research center is on \u201cusing AI to make quantum computers more useful and more capable,\u201d says Costa. \u201cToday’s quantum computers are fifty to a hundred qubits. It’s generally accepted now that truly useful quantum computing will come with a million qubits or more that are error corrected down to tens to hundreds of thousands of error-free or logical qubits. That process of error correction is a big compute problem that has to be done in real time. We believe that the methods that will make that successful at scale will be AI methods.\u201d<\/p>\n Quantum computing is a delicate process, subject to interference from \u201cnoise\u201d in its environment, resulting in at least one failure in every thousand operations. Increasing the number of qubits introduces more opportunities for errors. When Google announced Willow<\/a> last December, it called it \u201cthe first quantum processor where error-corrected qubits get exponentially better as they get bigger.\u201d Its error correction software includes AI methods such as machine learning, reinforcement learning, and graph-based algorithms, helping identify and correct errors accurately, \u201cthe key element to unlocking large-scale quantum applications,\u201d according to Google.<\/p>\n