RCC: QBoard Cloud Quantum Computing Platform

The main articles are:

• PaaS - Platform As A Service
• Quantum Computers and Quantum Communications
• Quantum computers and networks in Russia

The cloud quantum computing platform provides a complete cycle of solving research and business problems of various industries (from quantum computers staging to the end result). The QBoard platform is a unified interface for quantum computers and quantum architectures, algorithms it allows you to speed up the solution of optimization, processing big data and comparative tests of promising computing architectures.

2023

Use with FermiNet to model larger chemical systems

Scientists from Russian Quantum Center together with colleagues from NPS MISiS increased the performance of FermiNet neural network , created by a subsidiary, a Google British system developer. During artificial intelligence DeepMind the experiment, carried out with the support of the Russian Research Institute and the Research Center, Nissan experts used the neural network FermiNet cloudy and the QBoard platform quantum computing to model larger chemical systems. The RCC announced this on March 7, 2023.

Researchers in a wide variety of fields of science regularly use computational architectures based on artificial neural networks to analyze huge amounts of data and predict the behavior of individual systems. So, in 2020, DeepMind first used a fermion neural network to solve one of the key problems in the field of chemistry - the Schrödinger equation for electrons in molecules.

Most problems in quantum mechanics cannot be solved with an accurate answer, so scientists are forced to use approximation, a scientific method consisting in finding approximate values ​ ​ by replacing objects with simplified analogues. By varying free parameters, physicists manage to find wave functions that most accurately describe the state of the system. This form of search - ansatz - is actively applied in quantum chemistry, since simulations of elementary chemical reactions are still given to scientists with great difficulty even for the small number of atoms in the system.

As part of the experiment, a joint team of physicists, chemists and machine learning specialists used the FermiNet architecture as an ansatz. Further, the experts began to iteratively improve the neural network through an updated procedure for its training. The calculations used the tools of the QBoard cloud quantum computing platform. Scientists not only got the opportunity to simulate systems of greater dimension than the original FermiNet architecture allowed, but also increased the accuracy of classical calculations in electron-nuclear and electron-electronic interaction.

The results were demonstrated in the simulation process of nitrogen, carbon monoxide, ethylene, hydrogen fluoride and a number of other molecules. In the future, the data obtained can be used in pharmacology to create new drugs, materials science and the fuel industry.

The combination of machine learning methods and quantum chemistry produces very interesting results. Such interdisciplinary interactions of physicists, chemists, biologists, programmers lead to the enrichment of classical approaches and such interesting hybrid solutions as our case on the use of QBoard for the development of the FermiNet network, "said Alexey Fedorov, head of the scientific group" Quantum Information Technologies "of the Russian Quantum Center.

Cloud Platform Architecture

QBoard Quantum Computing Cloud Platform Architecture for March 2023