(ORDO NEWS) — Both quantum computing and machine learning have been called the next big computing revolution for quite some time now.
However, experts point out that these methods are not universal tools – they will only be a big leap forward 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 relate to the Standard Model?
The team, led by researchers from the University of Michigan and RIKEN, believes they have succeeded in developing just such an algorithm. There aren’t many places where two great physical models collide, but around a black hole is one of them.
Black holes themselves are massive gravity wells that are completely controlled by the physics defined by General Relativity. However, countless particles swirl around their event horizons, which are not actually affected by gravity, but fall under the structure of the Standard Model, which deals directly with particle physics.
There has long been a 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 dualism and can offer a way to find a critical interface between relativity (i.e. black hole physics) and the Standard Model (i.e. particle physics).
However, holographic dualism 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 uses two very popular computing architectures – quantum computing and machine learning.
Quantum computing itself can be useful in modeling particle physics, since some of the physical processes that underlie the computing platform itself obey physical laws that are so foreign to us at the macro scale.
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 dualism.
To do this, they used a concept called the quantum matrix model. As with many other physical simulations, the ultimate goal of the simulation was to find the lowest energy state of the system.
Quantum matrix models would effectively solve optimization problems to find the smallest 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 system’s lowest-energy state is called. Another way 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 quantum wavefunctions, 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, these new methods represent a significant improvement over other previous attempts at solving 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 a bit 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 Rinaldi says could lead to a quantum theory of gravity, is not yet complete. However, as these emerging computing architectures continue to gain momentum, you can almost be sure that someone will try to shed light on this black box.
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