Both quantum computing and machine learning have been touted as the next big computer revolution for a fair while now. However, experts have pointed out that these techniques aren’t generalized tools – they will only be the great leap forward in computer power for very specialized algorithms, and even more rarely will they be able to work on the same problem. One such example of where they might work together is modeling the answer to one of the thorniest problems in physics: how does General Relativity relate to the Standard Model?
A team led by researchers at the University of Michigan and RIKEN think they might have developed just such 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 themselves are massive gravity wells ruled entirely by the physics defined by General Relativity. However, innumerable particles are swirling around their event horizons that are effectively immune to gravity but do fall under the Standard Model structure, which deals directly with the physics of particles.
There has been a long-standing theory that the motions and accelerations of the particles directly above a black hole might be a two-dimension projection of what the black hole itself is doing in three dimensions. This concept is called holographic duality and might offer a way to look for that critical interface between relativity (i.e., black hole physics) and the Standard model (i.e., particle physics).
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