(ORDO NEWS) — Both quantum computing and machine learning have been touted as the next big computing revolution for quite some time now.
However, experts note that these methods are not universal tools – they will only be a big jump in computing power for very specialized algorithms, and even more rarely they will be able to work on the same problem.
One example of where they can work together is in modeling the answer to one of the hardest problems in physics: how does general relativity compare to the standard model ?
A group of researchers from the University of Michigan and RIKEN believe that they could develop just such an algorithm. There aren’t many places where two great physical models collide, but around a black hole one of them is.
By themselves, black holes are massive gravity wells, completely controlled by the physics defined by general relativity. However, there are countless particles orbiting their event horizons that are virtually immune to gravity, but fall under the structure of the Standard Model, which is directly related to particle physics.
There is a long-standing theory that the motions and accelerations of particles directly above a black hole could be a two-dimensional projection of what the black hole itself is doing in three dimensions.
This concept is called holographic duality and may offer a way to find a critical interface between relativity (i.e., black hole physics) and the Standard Model (i.e., particle physics).
However, the holographic duality itself is difficult to model using modern computational algorithms. So Enrico Rinaldi, a physicist at the University of Michigan and RIKEN , tried to develop a new model that would use two highly publicized computing architectures – quantum computing and machine learning.
Quantum computing itself can be useful in modeling particle physics, since some of the physical phenomena underlying the computing platform itself obey physical laws that are so foreign to us at the macroscale.
In this case, Dr. Rinaldi and his team used an algorithm running on a quantum computer to simulate the particles that make up the design part of holographic duality.
To do this, they used a concept called the quantum matrix model. As with many physics simulations, the ultimate goal of the simulation was to find the lowest energy state of the system.
Quantum matrix models will help to efficiently solve optimization problems that will allow finding the lowest energy state of particle systems projected over a black hole.
Algorithms using a quantum computer are not the only way to find these “ground states”, as the lowest energy state of a system is called. Another method is to use an artificial intelligence technique called a neural network. They are based on the use of systems similar to those found in the human brain.
The team applied these algorithms to a type of matrix model, still based on quantum ideas but not requiring quantum computing.
Known as the quantum wave function, they again represented particle activity on the surface of a black hole. Once again, the neural network algorithm was able to solve the optimization problem and find its “ground state”.
According to Rinaldi, the new methods represent a significant improvement over other previous attempts to solve these algorithms. “Other methods that people commonly use can find the ground state energy, but not the entire structure of the wave function,” Rinaldi said in a press release.
What this means for understanding the interior of a black hole, or the interface between the standard model and general relativity, is still something of a black box. Theoretically, there should be a way to model the interior of a black hole using the types of quantum wave functions defined by these algorithms.
But this work, which could lead to the fundamental quantum theory of gravity according to Rinaldi, is yet to be done. However, as computing architectures continue to gain popularity, it is almost certain that someone will try to shed some light on this black box.
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