Understanding the insides of a black hole is still a mystery.
For a long time, quantum computing and machine learning have
been hailed as the next big computer revolutions. Experts have pointed out,
however, that these techniques aren't general-purpose tools; they'll only make
a significant leap forward in computing power for really specific algorithms,
and they'll only seldom be able to solve the same problem. One area where they
could collaborate is in simulating the answer to one of physics' most vexing
questions: How does General Relativity relate to the Standard Model?
A team lead by University of Michigan and RIKEN academics
believes they have built such an algorithm. There aren't many sites where the
two grand physics models intersect, but one of them is around a black hole.
Black holes are huge gravity wells that are totally governed by the physics of
General Relativity. Countless particles, on the other hand, are swirling around
their event horizons, effectively impervious to gravity but falling inside the
Standard Model structure, which deals directly with particle physics.
The motions and accelerations of the particles just above a
black hole have long been thought to be a two-dimensional projection of what
the black hole is doing in three dimensions. Holographic duality is a concept
that may provide a means to search for the critical interface between
relativity (i.e., black hole physics) and the Standard Model (i.e., particle
physics).
However, holographic duality is difficult to represent using
today's computing technologies. As a result, Enrico Rinaldi, a physicist at the
University of Michigan and RIKEN, set out to create a novel model that combined
the two most heralded computing architectures — quantum computing and machine
learning.
Because parts of the physics underpinning the computing
platform are subject to those physical principles that are so foreign to us on
a macro scale, quantum computing can be useful in modelling particle physics.
Dr. Rinaldi and his team employed a quantum computer approach to mimic the
particles that make up the project component of the holographic duality in this
example.
They did it by employing a notion known as a quantum matrix
model. The simulation's end goal, like many physics simulations, was to
determine the system's lowest energy state. Quantum matrix models could aid in
resolving optimization problems involving particle systems projected above a
black hole, such as determining the lowest energy state.
Quantum computer algorithms aren't the only technique to
identify those "ground states," as the system's lowest energy state
is known. Another option is to use a neural network, which is a form of AI
approach. These are built on systems that are similar to those in human brains.
These methods were used on a form of matrix model that was
still based on quantum principles but didn't require quantum computers. These
were known as quantum wave functions, and they represented the activity of the
particles on the black hole's surface once more. The neural network technique
solved the optimization challenge and found the "ground state" once
more.
These new strategies, according to Rinaldi, are a major
improvement above past attempts to solve these algorithms. "Other commonly
used approaches can discover the energy of the ground state, but not the full
structure of the wave function," Rinaldi said in a statement.
What this means for comprehending the innards of a black
hole, or the interaction between general relativity and the standard model, is
still a bit of a mystery. The types of quantum wave functions produced by these
methods should theoretically be able to model the innards of a black hole.
However, Rinaldi believes that more work needs to be done before an underlying
quantum theory of gravity can be developed. However, as these overhyped
computing designs rise in popularity, it's practically a foregone conclusion
that someone will try to shed some light on that black box.
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