Mission accomplished: NVIDIA CEO’s quantum mea culpa brings Microsoft and AWS to GTC table after market-rattling comments

NVIDIA CEO Jensen Huang hosted the company’s first-ever “Quantum Day” at its GTC conference on Thursday, bringing together executives from across the quantum computing industry just two months after dismissing the technology as something that won’t be useful for “15 to 30 years.” In the immediate aftermath of Huang’s skepticism, shares of IonQ, Rigetti Computing, and D-Wave Quantum each dipped between 39% and 45%. As of today, two of those stocks remain down more than 50% year to date, while D-Wave’s share price is off by around 11%.

Yet at “Quantum Day,” Huang seemed keen to reset the narrative. He acknowledged his earlier doubts, remarking, “If I had to be wrong to show everybody in the world that quantum computing is worthwhile to do and that the industry is built of amazing people and the work that the industry is going to make a great impact, if I had to do a mea culpa in order for us to demonstrate [that] to the world, mission accomplished.”

So what’s so amazing potentially about quantum?

During the final panel session of Quantum Day, Microsoft’s Krysta Svore—who is the company’s Technical Fellow for Advanced Quantum Development—highlighted the striking progress in error-corrected “logical qubit” development. “One year ago… we had zero logical qubits that were better than our physical qubits… And within the last year… we showed 28 logical qubits with atom computing. And now… this calendar year, we’re working on the 50 logical qubits with atom computing.” One way to analogize logical qubits is to think of them as teams of physical qubits working in unison to detect and correct errors, thus working toward stable quantum operations. “We believe the most promising set of applications is in chemistry and material science, biochemistry as well,” Svore added,

Svore also dished on the rapid acceleration of logical qubits, emphasizing that these “error-resistant teams” protect quantum information from errors caused by noise or hardware imperfections. Why are logical qubits important? Think of them as groups of workers where each member (physical qubit) cross-checks the others to catch and fix mistakes. This redundancy allows quantum computers to perform much longer and more complex calculations than would be possible with error-prone physical qubits alone, opening the door to practical quantum applications in chemistry, materials science, and beyond.

We believe the most promising set of applications is in chemistry and material science, biochemistry

The promise of using quantum data as ground truth for AI models

Among the most exciting potentials for quantum is its synergistic potential with AI. Huang observed, “One of the areas that’s super exciting is in the area of materials and biology, where we would like to train a model… to train representation of biology. But where does that training data come from? It’s not like we have sensors and instruments. We’ve been collecting data about biology and cells and proteins. Now we could simulate that using a quantum computer and use that as ground truth data to then go train an AI model.”

Svore also elaborated on how quantum-fed data can transform AI: “The idea is to get a faster, more predictive, more accurate AI model. This is a classical AI model. It deploys in your current infrastructure, right? And it’s fast, and so that’s really the promise, right? You’re using the quantum computer to produce data that you cannot otherwise efficiently get on this planet.” She also emphasized the natural parallel, noting: “Nature is incredibly efficient, and I think of quantum computers as enabling us to take a step closer to computing like nature does—being able to kind of see and understand electrons in a new way.”

AWS’s “Ocelot” and the journey to error correction

Meanwhile, Simone Severini (Director, Applied Science at AWS) spotlighted the company’s new “Ocelot” superconducting chip, which aims to reduce error-correction overhead by up to 90%. He stressed that quantum machines—often referred to as “QPUs”—won’t replace classical supercomputers or GPUs but instead serve as specialized accelerators for tasks previously out of reach.

“We build quantum computers based on superconducting technology. We give strong emphasis to error correction—we believe that’s really key for quantum computing to deliver on its long-term promise. I’m not saying that quantum computers so far are useless. Actually, they’re extremely useful to learn a lot about how to build quantum computers for the future. Recently, we announced a superconducting chip called ‘Ocelot.'”

“You know, Ocelot is a kind of wild cat,” Severini said. The Ocelot chip uses “cat qubits,” which are a reference to Schrödinger’s famous feline thought experiment. These cat qubits rely on oscillators, hence the name “Ocelot,” Severini said..

To that, Huang joked: “That’s clever. Almost as clever as Nvidia.”

According to Oskar Painter, AWS director of Quantum Hardware, the Ocelot architecture could accelerate the timeline to a practical quantum computer by up to five years, potentially requiring as little as one-tenth of the resources compared to common approaches

Severini elaborated on AWS’s rationale in choosing superconducting devices, explaining: “My mental model hinges on three terms: knowledge, speed, and experience. Knowledge, because there’s a lot of experiments out there done with superconducting devices in industry, in academia, proof of concept for error correction. Speed, because microwaves make it easier to implement error correction. And experience, since AWS has a solid background in custom silicon and semiconductors we can translate into superconducting technology.”

Severini also offered a philosophical perspective on why quantum matters: “Quantum computers are the only instrument we have today that we know so far for accessing that layer of physical reality governed by quantum mechanics—laws that don’t apply to the everyday physics we experience with our senses. Quantum computers are going to be catalysts for scientific innovation that will allow us to discover certain things that are very hard to predict. And in a way, we must build quantum computers, because otherwise that layer of reality won’t be accessible to us.”

Why quantum computing is like the space program

Both Huang and Severini described quantum computing as a multi-stage journey, akin to the space program. Huang suggested that quantum systems can “make a classical computer way better” by providing accurate simulation data, which classical AI can then leverage for discovery in biology, materials science, and more.

As Severini put it: “This is like the space program. The goal is going to the moon… But we’re going to discover a lot of things on the way. You know, we discover firemen suits trying to go to the moon… It’s a grand adventure, but we need to get there. And it’s not ‘0 or 1’… It’s a journey, and lots of things are going to be discovered in science and technology as we get there.”