Intel has edged one step closer to practical quantum computers. The chipmaker and its partner QuTech have successfully controlled “hot” qubits (that is, at temperatures above 1 kelvin) that are also coherent and dense, making it easier to put qubits and control electronics on the same chip and thus produce more advanced quantum computers. Until now, quantum computers had to run at temperatures in the millikelvin range, or barely above absolute zero (just under -460F) — for context, the average temperature in outer space is a balmy 3 kelvin.

The demonstration was still relatively modest. Intel and QuTech completed their test using two-qubit logic where cutting-edge quantum computers have dozens of qubits and a full-featured computer may need over 1 million. This is “just one step” toward scalable quantum computers, Intel said. It’s still an important step, though, and hints that the technology is more viable than it seems today.

To capture a picture of the Bell entanglement, physicists created a system that shoots off streams of entangled photons from a quantum source of light at what they call “non-conventional objects.” These objects are displayed on liquid-crystal materials, which can change the phase of the photons as they move through them. A camera capable of detecting photons was then set to snap a photo when it identified one photon entangled with another.

According to the researchers, quantum entanglement is one of the primary pillars of quantum mechanics. The concept is used in practical applications like quantum computing and cryptography, but no one has ever managed to capture an image of it in action. Physicists involved in the project believe that the image can help to advance the field of quantum computing and may lead to new types of imaging.

It’s difficult to simulate quantum physics, as the computing demand grows exponentially the more complex the quantum system gets — even a supercomputer might not be enough. AI might come to the rescue, though. Researchers have developed a computational method that uses neural networks to simulate quantum systems of “considerable” size, no matter what the geometry. To put it relatively simply, the team combines familiar methods of studying quantum systems (such as Monte Carlo random sampling) with a neural network that can simultaneously represent many quantum states.