A neural network that learned to predict the behavior of a quantum system

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A neural network that learned to predict the behavior of a quantum system

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A wide range of issues in modern science are tackled through quantum mechanical calculations. The quantum nature of the problems involved improves quantum calculations better-suited to them.

Creating quantum computers is expensive and tedious, and the subsequent gadgets are not ensured to display any quantum advantage. That is, operate faster than a conventional computer. So specialists need devices for anticipating whether a given quantum device will have a quantum advantage. 

One of the approaches to execute quantum computations in quantum walks. In simple terms, the technique can be envisioned as a particle traveling in a specific system, which underlies a quantum circuit. On the off chance that a molecule’s quantum walks starting with one network node then onto the next happens quicker than its classical analog, a device-dependent on that circuit will have a quantum advantage. The quest for such superior systems is a significant errand handled by quantum walk specialists.

In a new study, Russian scientists from the Moscow Institute of Physics and Technology, Valiev Institute of Physics and Technology, and ITMO University have replaced quantum walk experts with artificial intelligence. They trained the machine to recognize networks and tell if a given system will convey quantum advantage. This pinpoints the networks that are good candidates for building a quantum computer.

Scientists used a neural network geared toward image recognition. An adjacency matrix served as the info data alongside the quantities of the input and output nodes. The neural system restored a forecast of whether the old style or the quantum walk between the given nodes would be quicker.

Associate Professor Leonid Fedichkin of the theoretical physics department at MIPT said“It was not obvious this approach would work, but it did. We have been quite successful in training the computer to make free predictions of whether a complex network has a quantum advantage. 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.”

Scientists also created a tool to simplify the development of computational circuits based on quantum algorithms. The resulting devices will be of interest in biophotonics research and materials science.

Scientists noted, “Solving a problem that formally involves finding the quantum walk time from one node to another may reveal what happens to an electron at a particular position in a molecule, where it will move, and what kind of excitation it will cause.”

“Compared with architectures based on qubits and gates, quantum walks are expected to offer an easier way to implement the quantum calculation of natural phenomena. The reason for this is that the walks themselves are a natural physical process.”

The findings are reported in the New Journal of Physics.



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