NVIDIA's new Ising AI could finally make quantum computing usable

NVIDIA has officially unveiled a new family of open-source AI models it's calling Ising, which is aimed at solving some of the biggest challenges holding back quantum computing.

Quantum computing is the next stage of computing, and while we know that current quantum computers are intensely powerful, some solving equations that a classic computer would take millions of years to solve, in just seconds, they aren't particularly useful in everyday life. NVIDIA proposes to use AI to bridge the gap between quantum computing and real-world usefulness, and at the core of its recent announcement is its CUDA-Q platform.

NVIDIA explained that this new platform is designed to be "qubit-agnostic," meaning it can work with different types of quantum hardware without being tied to a specific architecture. Think "open-source-level" interoperability, but between hardware powering quantum computers. Ising introduces a layer of intelligence, designed to stabilize quantum processors and yield more consistent results.

That last point is quite a big deal, as the current biggest problem quantum computers are facing is error rates. Qubits, or quantum bits, are the foundation of quantum computers and are extremely sensitive to environmental factors, which can disturb them and cause errors. At the moment, error rates are at about one out of every thousand operations. For comparison, for a quantum computer to become practical for everyday computation, it would need to be closer to one error per trillion operations. Here's where NVIDIA's Ising comes in.

NVIDIA claims to deliver 3x greater accuracy with Ising, along with 2.5x faster performance than current industry-standard tools. Another key efficiency gain is the 10x less training data, making it cheaper and faster to deploy models. There is also significant improvement on the calibration front, with NVIDIA claiming it can reduce calibration time for quantum systems from days to just hours.