The quantum computing industry is sounding off after Nvidia (NVDA) CEO Jensen Huang made some bold predictions at the annual Consumer Electronics Show (CES) this week.
On Monday, Huang appeared at a Wall Street analyst Q&A following Nvidia’s keynote address at CES 2025.
He shared his take on the future of quantum computing, stating he believes that mainstream quantum computing technology is likely 20 years away.
As Huang sees it, a 15-year window for what he describes as “very useful quantum computers” is likely too early, but a 30-year window is likely too late. He hypothesizes that a 20-year window for technology makes the most sense, adding that he believes many tech leaders would agree.
Not everyone is in sync with Huang’s predictions, though. The CEO of a prominent quantum computing company offered his own take, making it clear that he sees a much different timeline for the technology’s evolution.
Nvidia CEO Jensen Huang recently issued some predictions on the future of quantum computing and some experts do not agree.. (Photo by Justin Sullivan/Getty Images)Justin Sullivan/Getty Images
D-Wave Systems' CEO challenges Huang’s take on his industry
After Huang’s comments, many quantum computing stocks that had previously been performing well plunged as speculation rose based on his predictions.
One industry leader whose company has helped usher in the new phase of quantum computing quickly stated that Huang is ‘dead wrong’ about the technology.
Dr. Alan Baratz is president and CEO of D-Wave Systems, a role he has held since 2020.
Following Huang’s comments, Baratz spoke to TheStreet about why Huang is incorrect on the quantum computing timeline. He also shared his opinion on what the future of quantum computing will look like.
Baratz retort to Huang is simple: D-Wave Systems already has commercial quantum computers that customers use to run their business operations today.
Baratz says there are many approaches to quantum computing, and he speculates Huang is focusing on just one of them.
“I think the problem we have is that Jensen is only really familiar with one approach, and his comments, while maybe on a longer timeline than others might say, isn't totally wrong for the approach he has in mind,” Baratz notes.
Unlike the approach Huang is referencing, Baratz says D-Wave took a different route. As a result, companies are already using his company's quantum computing technology.
D-Wave Systems approach to quantum computing
The approach to which Huang seems to be referring requires error correction, described as “a set of techniques used to protect the information stored in qubits from errors and decoherence caused by noise.”
Baratz notes that systems that rely on error correction may still be years away from mainstream adoption, as Huang has suggested. However, Baratz also makes clear that D-Wave’s approach to quantum, called annealing, does not require error correction.
“When errors creep into our computation, it does not destroy [it], " Baratz explains. “We may not get to the optimal solution, but we'll typically get to a good solution.”
The other quantum approach is known as ‘gate,’ and as Baratz notes, it requires error correction since errors are much more prevalent in those systems, and they destroy the computation as soon as they enter it.
Baratz predicts the industry has a long way to go before it reaches full error correction. However, he sees Google’s (GOOGL) recent progress with its Willow quantum computing chip as a good step forward, describing it as “The first time that somebody demonstrated on an interesting scale, the ability to ever correct a qubit.”
Quantum computing simplified
Anyone following these market developments should understand the technology that Huang and Baratz are discussing.
Broadly speaking, quantum computing refers to a type of advanced computing that leverages the power of quantum mechanics to perform tasks at an extremely fast rate.
While traditional computers typically only solve one problem at a time, quantum machines can handle multiple tasks at once by utilizing quantum bits (qubits) of information.
The current AI boom, from which companies like Nvidia, which builds graphics processing units (GPUs), have significantly benefited, has sparked a strong need for advanced computational power to run large language models (LLMs). Some experts have suggested that quantum computing could provide the solution.
Use cases for D-Wave’s quantum technology
To illustrate the commercial aspects of D-Wave’s quantum computing technology, Baratz cited several use cases involving prominent companies spanning different industries.
Japanese mobile phone operator NTT Docomo uses D-Wave technology to optimize its cell tower resources. “With this approach, they are able to support up to 15% more smartphones per cell tower,” notes Baratz.
Ford Otosan, a joint venture between Ford Motors (F) and Turkish industrial conglomerate Koc Holdings, has utilized D-Wave’s quantum systems, reducing a computation that previously took 30 minutes down to mere seconds.
Another customer is Canadian supermarket operator Pattison Food Group. It has achieved an 80% reduction in the time it takes to schedule its workforce and optimize last-mile routing for e-commerce delivery.
D-Wave also has a relationship with Mastercard (MA) . It's helping the leading credit card provider optimize its loyalty rewards programs to improve uptake.
“These are all real business applications that have been built and are being used based on our quantum computers today, and there are many more that are in process,” Baratz notes.
The D-Wave CEO adds that just about every business has optimization problems that need to be solved, and which quantum computing technology could address.
Other experts have expressed similar views to Baratz, including IDC Research Manager Heather West, PhD, who recently published a report stating:
“The range of optimization problems that can be applied to the annealing quantum computer, including AI and materials science problems, demonstrates a versatility once thought to be limited only to gate-based systems.”
The synergistic future of quantum computing and AI
Baratz agrees with West’s optimism regarding the potential for quantum computing to impact AI positively.
As he sees it, there are potentially many instances where each can bring value to different parts of the problem, thereby benefiting both markets.
One example is using AI to predict product demand and quantum computing to optimize the supply chain.
“Beyond that,” he states, “We are also working on how you could use quantum computing to speed up and reduce the energy required to do model training and inference, which is very significant because obviously, energy consumption is a huge issue right now on GPUs and AI.”
If AI and quantum computing complement each other so well, why would someone like Huang express a perspective that sparks negativity?
Veteran Wall Street trader Stephen Guilfoyle recently examined this topic, raising the possibility that “Huang, who can barely keep up with AI-focused demand, just does not need the next big thing to explode just yet ... and does not want these players to shop elsewhere.”
Regardless of what Huang wants, though, Baratz’s statements make it clear he sees quantum computing technology, especially at D-Wave Systems, making steady advancements that disprove Huang’s hypothesis.
"We have practical, large-scale quantum computers today," he states. "D-Wave Systems are 5,000 qubit quantum computers, and that order of magnitude is larger than any other quantum computer available today."
