Designing and developing a quantum computer is an expensive & time-consuming process and the resulting devices do not always give the assurance that it will perform better than conventional computers, in order to address this issue Russian researchers built an AI that tells how a quantum computer will behave.
The Russian researchers from the Moscow Institute of Physics and Technology, Valiev Institute of Physics and Technology and ITMO University have developed a neural network that understands and learned to predict the behavior of a quantum computer system by looking at its network structure.
The system is so well that it also autonomously comes up with fine-tuned solutions that demonstrate quantum advantage, the ability of quantum computers to outperform classical computers.
Alexey Melnikov from Information Technologies, Mechanics and Optics University explained in a report by MIPT: “The line between quantum and classical behaviors 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.”
Quantum mechanical calculations help to solve a wide range of problems for example research into chemical reactions and the search for stable molecular structures for medicine, pharmaceuticals 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. The ability to process data on a subatomic level gives quantum computing various advantages over traditional, including faster speeds.
One of the ways to implement quantum computations is to perform quantum walks.
In simplified terms, the method can be visualized as a particle traveling in a certain network that 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.
The researchers have trained an artificial intelligence tool to do the experts’ job. They train the model to distinguish between networks and determine if a given network has a quantum advantage.
The artificial intelligence tool tells whether the networks that are good candidates for building a quantum computer. The team used a neural network geared toward image recognition.
With the help of image recognition technology, the AI tool is able to study the graphs of quantum walks and autonomously predict if a quantum computer will have a quantum advantage or not. The researchers have shown the ability of quantum walk algorithms to develop quantum computers
An adjacency matrix served as the input data, along with the numbers of the input and output nodes. Neural network predicts 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. We have been quite successful in training the computer to make autonomous predictions of whether a complex network has a quantum advantage,” said Associate Professor Leonid Fedichkin of the theoretical physics department at MIPT.
The AI tool has simplified the development of computational circuits based on quantum algorithms. The resulting devices will be of interest in biophotonics research and materials science.
More in AI