Finding the perfect alloy from scrap metal is like baking a cake from leftover ingredients without an exact recipe. You need to mix pieces of different aluminum alloys to get a strong and thermally conductive material. Scientists assigned this task to a quantum computer.

Unlike a conventional computer, which explores options one by one, the quantum computer 'tastes' many mixtures simultaneously thanks to superposition — the ability to be in multiple states at once. And quantum entanglement links parts of the recipe so that changing one instantly affects the others. But if the mixture is too complex, interference occurs — quantum decoherence, and the machine makes mistakes.

To get around this, they added machine learning to the process — a program that predicts the alloy's properties from its composition. The cycle is simple: the program suggests recipes, the quantum algorithm picks the best ones, and numerical simulation (a virtual experiment) checks the result. By repeating these steps, scientists found the ideal metallicity — the precise chemical composition where high strength meets excellent thermal conductivity. The key was not to get stuck on one variant but to maintain diversity in the search — what physicists call entropy. This approach gives a second life to cans and profiles, saving almost all the energy needed for primary aluminum production.