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Izvestiya SFedU
Engineering sciences
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ISSN 1999-9429 print
ISSN 2311-3103 online
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  • IMPLEMENTATION OF A PROBABLE DEEP NEURAL NETWORK DECODER FOR STABILIZER CODES

    S.M. Gushanskiy, V.N. Pukhovsky, V.S. Potapov
    2021-12-24
    Abstract ▼

    Recently, there has been a rapid increase in interest in quantum computers. Their work is
    based on the use of quantum-mechanical phenomena such as superposition and entanglement for
    computing to transform input data into outputs that can actually provide effective performance
    3–4 orders of magnitude higher than any modern computing devices, which will allow solving the
    above and other tasks in real and accelerated time scale. This work is a study of the influence of
    the environment on a quantum system of qubits and the results of its implementation. A probabilistic
    deep neural network decoder for stabilizer codes has been developed. The issues of error correction
    for a three-bit code without state decoding are analyzed and considered. The relevance of
    these studies lies in mathematical and software modeling and implementation of correction codes
    for correcting several types of quantum errors in the development and implementation of quantum
    algorithms for solving classes of problems of a classical nature. The scientific novelty of this direction
    is expressed in the elimination of one of the disadvantages of the quantum computational
    process. The scientific novelty of this area is primarily expressed in the constant updating and
    supplementation of the field of quantum research in a number of areas.

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