Why AI could eat quantum computing’s lunch

AI is now being applied to fundamental physics, chemistry, and materials science in a way that suggests quantum computing’s purported home turf might not be so safe after all.

The scale and complexity of quantum systems that can be simulated using AI is advancing rapidly, says Giuseppe Carleo , a professor of computational physics at the Swiss Federal Institute of Technology .

In recent years there’s been an explosion of research using DFT to generate data on chemicals, biomolecules, and materials.

These AI models learn patterns in the data that allow them to predict what properties a particular chemical structure is likely to have, but are orders of magnitude cheaper to run than conventional DFT calculations.

Simulating large systems using these approaches requires considerable computing power.

Machine-learning approaches piggyback on massive investments in AI software and hardware.

Because the ground states are effectively found through trial and error rather than explicit calculations, they are only approximations.

The approach could make progress on what has looked like an intractable problem, says Juan Carrasquilla , a researcher at ETH Zurich .