New AI tool predicts quantum system behaviour by looking at network structure
Russian researchers have created an Artificial Intelligence (AI)-based tool that has learned to predict the behaviour of a quantum system by "looking" at its network structure.
The neural network autonomously finds solutions that are well-adapted toward quantum advantage demonstrations, according to a study published in the New Journal of Physics.
This is expeted to aid researchers in developing new efficient quantum computers.
"We have been quite successful in training the computer to make autonomous predictions of whether a complex network has a quantum advantage," said Leonid Fedichkin, Associate Professor at the Moscow Institute of Physics and Technology (MIPT).
A wide range of problems in modern science are solved through quantum mechanical calculations.
Some of the examples are research into chemical reactions and the search for stable molecular structures for medicine, pharmaceutics, and other industries.
The quantum nature of the problems involved makes quantum computations better-suited to them. Classical computations, by contrast, tend to return only bulky approximate solutions.
Creating quantum computers is costly and time-consuming, and the resulting devices are not guaranteed to exhibit any quantum advantage - that is, operate faster than a conventional computer.
So researchers need tools for predicting whether a given quantum device will have a quantum advantage.
One of the ways to implement quantum computations is quantum walks. In simplified terms, the method can be visualised as a particle travelling in a certain network, which underlies a quantum circuit.
If a particle's quantum walk from one network node to another happens faster than its classical analogue, a device based on that circuit will have a quantum advantage.
The search for such superior networks is an important task tackled by quantum walk experts.
What the Russian researchers did is they replaced the experts with AI. They trained the machine to distinguish between networks and tell if a given network will deliver quantum advantage. This pinpoints the networks that are good candidates for building a quantum computer.
The team used a neural network geared toward image recognition. An adjacency matrix served as the input data, along with the numbers of the input and output nodes. The neural network returned a prediction of whether the classical or the quantum walk between the given nodes would be faster.
"It was not obvious this approach would work, but it did," Fedichkin said.
"The line between quantum and classical behaviours is often blurred. The distinctive feature of our study is the resulting special-purpose computer vision, capable of discerning this fine line in the network space," added Alexey Melnikov from ITMO University in Russia.
The researchers created a tool that simplifies the development of computational circuits based on quantum algorithms. The resulting devices will be of interest in biophotonics research and materials science.