РЕАЛИЗАЦИЯ ВЕРОЯТНОСТНОГО ДЕКОДЕРА ГЛУБОКОЙ НЕЙРОННОЙ СЕТИ ДЛЯ КОДОВ СТАБИЛИЗАТОРА

Abstract

Recently, there has been a rapid increase in interest in quantum computers. Their work isbased on the use of quantum-mechanical phenomena such as superposition and entanglement forcomputing to transform input data into outputs that can actually provide effective performance3–4 orders of magnitude higher than any modern computing devices, which will allow solving theabove and other tasks in real and accelerated time scale. This work is a study of the influence ofthe environment on a quantum system of qubits and the results of its implementation. A probabilisticdeep neural network decoder for stabilizer codes has been developed. The issues of error correctionfor a three-bit code without state decoding are analyzed and considered. The relevance ofthese studies lies in mathematical and software modeling and implementation of correction codesfor correcting several types of quantum errors in the development and implementation of quantumalgorithms for solving classes of problems of a classical nature. The scientific novelty of this directionis expressed in the elimination of one of the disadvantages of the quantum computationalprocess. The scientific novelty of this area is primarily expressed in the constant updating andsupplementation of the field of quantum research in a number of areas.