What is Ising from NVIDIA and why is it important for the future of quantum computing

A quantum computer is not just a fast computer

When people talk about quantum computers, they usually focus on speed. But that’s not the point. Quantum machines are fundamentally different from everything we use today.

A classical bit is always either zero or one. A quantum bit, or qubit, can exist in both states simultaneously. This is called superposition. Thanks to it, a quantum computer can process a vast number of possibilities in parallel—where a classical machine would check them one by one.

This sounds like the perfect tool. But in practice, it’s more complex. Qubits only work under extreme conditions—temperatures close to absolute zero and complete isolation from the outside world. And even then, they remain incredibly fragile.

What is a quantum error—and why is it so hard to catch?

Any external influence—heat, vibration, electromagnetic interference—can flip a qubit from one state to another during computation. The result will be incorrect, and the system won’t report it.

This is a quantum error. It’s not a rare glitch—it happens constantly, as an inherent part of how any quantum processor operates.

The problem is that detecting such errors is incredibly difficult. You can’t simply pause the computation and check the qubit—measurement itself destroys the quantum state. Therefore, errors must be detected and corrected indirectly, in real time, before they accumulate and ruin the entire calculation.

This is precisely why quantum computers have not yet moved beyond laboratories. Not because the hardware is bad, but because controlling this hardware at an industrial scale is still nearly impossible.

What NVIDIA did—and how it works

In April 2026, NVIDIA launched Ising—the world’s first family of open AI models designed specifically for quantum computing. The company describes it as a tool that makes AI the control layer for quantum machines.

Ising consists of two parts.

The first is Ising Calibration. Before running a quantum processor, it must be calibrated: tuned to specific hardware, accounting for noise and aligning signals. Previously, this took days and required engineers with narrow specialization. Ising Calibration is a model with 35 billion parameters that can read graphs and signals from the processor and independently determine what needs to be adjusted and how. With it, calibration time is reduced from days to hours.

The second is Ising Decoding. This consists of two neural networks that operate directly during computation and correct errors—before they accumulate and ruin the entire calculation. According to NVIDIA, these models are 2.5 times faster and 3 times more accurate than the standard solutions previously used.

All models are open-source, free, and do not require expertise in machine learning. The models run on classical GPUs and are available to any researcher.

What does this have to do with robots?

NVIDIA has long been building open AI models for various industries.

1. Gr00t—for robots that learn to move and interact with the world.
2. Cosmos—for physical AI that understands the laws of real-world space.
3. BioNeMo—for biomedicine and drug development.
4. Ising—the next step in the same strategy, now for quantum systems.

Behind this lies one big idea: robots, quantum processors, and supercomputers are all parts of a unified future infrastructure. And AI becomes the layer that connects and controls all of this. It doesn’t just answer questions—it tunes machines, monitors their state, corrects errors, and decides which processor should perform which task.

This is especially relevant for robotics: next-generation robots will work in tandem with both quantum and classical systems simultaneously. And for all of this to work cohesively, exactly such a control layer is needed.

Why this matters right now

Even in January 2025, at the CES exhibition, Huang stated that truly useful quantum computers would appear in 15–30 years. This announcement shook the market—IonQ shares fell by 39% in one day, and Rigetti lost 45%.

Now NVIDIA is entering the quantum industry itself. And it’s doing so not through hardware, but through AI—using the same approach that worked with GPUs and data centers. The quantum computing market is projected to exceed $11 billion by 2030.

Among the first users of Ising are Fermilab, Harvard, and Academia Sinica.

Quantum computers are still a technology of tomorrow. But with Ising, that tomorrow has become noticeably closer.