Physicist uses quantum computing and machine learning to find out
Physicists at the University of Michigan are trying to better understand the idea called holographic duality, a mathematical conjecture that connects theories of particles and their interactions with the theory of gravity, using quantum computing and machine learning.
According to holographic duality, the theory of gravity and the theory of particles are mathematically equivalent. Both theories describe different dimensions, but the number of dimensions they represent differs by one.
In a new study, scientists identified a way to explore holographic duality using quantum computing and deep learning to find the lowest energy state of mathematical problems called quantum matrix models. The quantum matrix model represents particle theory.
Consider the example of a black hole. The gravity of a black hole exists in three- dimensions, whereas the particles dance above it in two dimensions. Hence, the black hole exists in a three-dimensional space, but we see it projected through particles. Hence, solving such a quantum matrix model could reveal information about gravity.
Scientists used two matrix models simple enough to be solved using traditional methods. These models have all the more complicated matrix models used to describe black holes through the holographic duality.
It should be noted that nothing happens to the system unless you add something to it that perturbs it. Scientists defined quantum wave function as the mathematical description of the quantum state of their matrix model. Using a particular neural network, they were able to find the wave function of the matrix with the lowest possible energy—its ground state.