(ORDO NEWS) — An international team of physicists has made the first attempt to understand what black holes are, what’s inside them, where they come from and what’s happening on the event horizon with the help of two cutting-edge technologies – quantum computing and machine learning.
Scientists believe that answers to these questions can be obtained by testing the holographic principle put forward by physicists at the end of the last century.
This principle states that particle theory and gravity theory are mathematically equivalent, which means that solving the quantum matrix model can reveal information about gravity. The first results of the calculations turned out to be encouraging.
Some scientists suggest that the entire universe is a holographic projection, and this allows us to derive a consistent quantum theory of gravity.
There are no particles in Einstein’s general theory of relativity, only spacetime. And in the Standard Model there is no gravity, only particles.
Combining these different theories is a long-standing problem in physics that has been tried since the last century, said Enrico Rinaldi, a physicist at the University of Michigan.
Rinaldi and his colleagues set themselves the task of studying holographic dualism using two methods:
- Quantum computing
- And deep learning.
They hoped to discover the lowest energy state of mathematical problems – quantum matrix models, representations of particle theory.
The holographic principle says that what happens mathematically in a system that represents particle theory affects the system in a way that represents gravity. It follows that the solution of the quantum matrix model can reveal information about gravity.
Matrix models are blocks of numbers that make up objects in string theory. Solving such models is an attempt to find a certain configuration of particles in a system that expresses its lowest energy state. Nothing happens in this basic state until it is violated.
Research scientists
In the study, the scientists used two matrix models that are simple enough to be solved by traditional methods, but which have all the features of more complex matrix models used to describe black holes using the holographic principle.
They determined the mathematical description of the quantum state of the matrix model, the so-called quantum wave function.
They then applied a neural network to find the wave function of the matrix with the lowest possible energy. The numbers of neural networks were run through an iterative optimization process to find the ground state of the model.
Both approaches allowed the scientists to find the ground state of both matrix models they investigated, but quantum processors are limited by the low number of qubits.
Other methods make it possible to find the energy of the ground state, but not the entire structure of the wave function. Ridley notes that the team of scientists showed how to get complete information about the ground state using the latest quantum computing and deep learning technologies.
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