NVIDIA bets on quantum computing as it expands its AI hardware footprint

NVIDIA has quietly expanded its footprint in quantum computing by backing three startups—Quantinuum, QuEra and PsiQuantum—through its venture-capital arm and other investors. The rounds collectively value the quantum firms at more than $17 billion, a sign of a shift in tone for NVIDIA chief executive Jensen Huang, who had previously described useful quantum computers as a 15 to 20 year prospect. Huang later acknowledged that the field could reach an inflection point in coming years. In response to the rounds, NVIDIA declined to comment on the investments.

Traditionally the company sits at the center of the AI hardware stack, designing GPUs, developing CUDA software and packaging systems that power the world’s largest AI workloads. The new bets into quantum hardware come as the industry debates how the two technologies relate. Some observers note that quantum computing and AI address different kinds of problems; quantum aims to solve a handful of highly valuable calculations with high precision, while GPUs execute vast numbers of simple operations in parallel. The distinction matters because the timing and scale of practical quantum advantages remain uncertain. A senior quantum executive described the dynamic as a shift in tone for NVIDIA, given the breadth of its footprint across data centers and the software that runs its chips.

Quantinuum uses ions to implement qubits, PsiQuantum builds its processors with photons, and QuEra pursues neutral-atom qubits. The three approaches illustrate the field’s diversity as qubit technologies race to demonstrate scale and reliability. Proponents say quantum computing could usher in a new computing paradigm: rather than performing many simple calculations in parallel, a quantum computer could solve a few extremely valuable equations that are intractable for classical machines. In the quantum ecosystem, a growing number of research groups and industry collaborations emphasize potential gains in chemistry, materials science and optimization. Google Quantum AI's Hsin-Yuan Huang has pointed to the fundamental advantage of simulating quantum mechanical systems, a task that remains challenging for classical hardware.

The practical utility, however, remains a moving target. For example, research into industrially relevant problems such as greener ammonia production or more efficient battery design hinges on building sufficiently large and reliable quantum systems. PsiQuantum has pursued partnerships to explore such applications: it is working with Mercedes-Benz to simulate lithium-ion battery electrolytes and with Boehringer Ingelheim to study enzymes involved in drug metabolism. Yet a representative from Oxford University cautioned that the field has yet to demonstrate a clear, replicable use case, and that the timeline for such breakthroughs is uncertain. While some researchers expect meaningful demonstrations in the coming years, others emphasize that much of the early promise depends on achieving scalable hardware and robust error correction.

PsiQuantum says it has begun work on sites in Australia and Illinois and is testing the cooling infrastructure necessary for its quantum devices. The company has said it expects a quantum computer large enough to be useful by 2027, a projection that shifted from earlier plans announced in 2021. Timelines in quantum computing have long been fluid, and industry observers note that progress toward practical machines will likely be incremental rather than spectacular.

In the meantime, NVIDIA’s role in the quantum ecosystem is evolving. Industry participants say the company’s hardware is already central to preparing quantum computations, running error correction, and analyzing readouts. In 2022, the firm rolled out CUDA-Q to enable quantum-classical communication, and this year it opened the NVIDIA Accelerated Quantum Computing Research Center in Boston, a facility focused on shortening the timeline to useful quantum computing. Analysts emphasize that even if quantum hardware remains years away from wide adoption, the push to understand which platforms scale best gives NVIDIA valuable foresight into the market’s direction.

As the field moves forward, some observers expect NVIDIA to consolidate its position through acquisitions should a quantum startup prove core to a scalable ecosystem. The recent funding rounds give the company advance notice about platform performance and reliability across rival approaches, and a number of investors say that, given enough time, NVIDIA could end up buying one or more quantum companies to accelerate its ambitions in this frontier.”