The buzz around quantum computers has been for good reason. The groundbreaking computers are designed to imitate what happens in nature at the microscopic level, implying they have the ability to understand quantum realm and speed up discovery of new materials, including environmentally friendly chemicals, pharmaceuticals, and more.
However, existence of viable quantum computers is still a decade away, say experts. What do researchers do in the meantime?
A study published in Science explains how machine learning tools, based on classical computers, can be used to make projection about quantum systems, and thus help researchers explain some of the most challenging chemistry and physics problems.
“Quantum computers serve to help with several types of physics and materials science questions, stated the lead author.
At the microscopic level, the physical world is an incredibly complex place guided by the laws of quantum physics. In this space, particles can exist in superposition of states, or in two at a time.
Meanwhile, superposition of states can lead to being tangled, a phenomenon in which particles are associated or correlated, without being in contact with each other. The strange states and connections, which are widely present within natural and human-made materials, are difficult to be described mathematically.
In fact, predicting the low-energy state of a material is difficult, stated the lead author. Materials have huge number of atoms, and they are superimposed and entangled.
Importantly, the new study is the first mathematical demonstration of classical machine learning that can be used to bridge gap between humans and the quantum world. Machine learning is a type of computer application that imitates human brain for knowledge from data.
“We are classical creatures living in a quantum world,” stated the researcher holding multiple research positions.
