Russian physicists have created a neural network-based system that helps find and correct errors in quantum computers. This will greatly simplify the process of scaling quantum computers and increasing the number of quantum bits in these computing devices, the press service of NUST MISIS reported.
The main advantage of the development is the ability to learn from data obtained from a specific device. This is especially important in conditions where the nature of errors differs from theoretically assumed models. In addition, the proposed decoding algorithm does not depend on a specific correction code, which makes it universal and easily scalable.
Many physicists believe that for the further development of quantum computers, it is necessary to develop systems that will automatically detect and correct random errors in their operation. Such errors are inevitable due to the interaction of qubits — quantum memory cells and the simplest computing blocks — with objects in the surrounding world.
Experts have long noticed that random failures in the operation of quantum computers can be reduced by using logical qubits. These are virtual quantum memory cells consisting of several connected physical qubits. In recent years, error correction systems have been developed that well protect the execution of key logical operations.
Earlier, Russian scientists developed a technology based on the use of fuzzy neural networks for the production of biofuels. In the future, it will be able to quickly adapt the parameters of the technological process at a mobile biofuel production station to the needs of the client.
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