Machine learning and quantum computing have been pegged as the next big computer revolution.
However, experts have pointed out that these techniques are only a leap forward in computer power for very specialized algorithms, and even more rarely will they be able to work on the same problem.
Modeling the answer to one of the thorniest problems in physics is an example of where they might work together.
A group of researchers at the University of Michigan and RIKEN think they may have developed an algorithm. There aren't many places where the two great physics models collide, but around a black hole is one of them.
Black holes are ruled by the physics of General Relativity. The Standard Model structure deals directly with the physics of particles, and many of them are immune to gravity.
There is a theory that the particles above a black hole might be a two-dimensional projection of what the black hole is doing.
There is a way to look for that critical interface between the Standard model of particle physics and the theory of black hole physics.
It is difficult to model holograms with modern-day computing. A physicist at the University of Michigan and RIKEN tried to develop a new model that utilized quantum computing and machine learning.
Some of the physics underlying the computing platform itself are subject to foreign physical laws that can be helpful in modeling particle physics.
Dr. Rinaldi and his team used a quantum computer to create a simulation of the particles that make up the project.
They used a concept called a quantum matrix model. The goal of the simulation was to find the lowest energy state of the system.
The lowest energy state of the particle systems projected above a black hole would be found with the help of quantum matrix models.
There are other ways to find the lowest energy state of the system. A neural network is a type of artificial intelligence. The systems used in these are similar to those found in human brains.
The matrix model that the team applied to is still based on quantum ideas but does not require quantum computing.
The activity of the particles on the surface of the black hole was represented by the quantum wave function. The neural network was able to find the ground state of the optimization problem.
Rinaldi said in a press release that the new techniques represent a significant improvement over previous efforts.
The interface between the standard model and general relativity is still a bit of a black box. There should be a way to model the inside of a black hole using the types of quantum wave functions defined by these algorithms.
The work that could lead to an underlying quantum theory of gravity is still being done. It is almost certain that someone will attempt to shine some light on that black box as these computing architectures continue to gain in popularity.
The article was published by Universe Today. The original article is worth a read.
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