Russian scientists have developed a new method for transmitting quantum data
Scientists from Moscow State University and Lobachevsky State University have developed a new approach to creating a quantum interface for data transmission using superconducting structures operating in qubit mode, the main elements of quantum computers.

These structures can operate in two modes: stationary (storing and processing information) and in the mode of "flying" qubits transmitting data along a chain. The researchers modeled the qubit control system using magnetic flux pulses, which minimizes information loss during transmission. This method opens up prospects for creating compact and energy-efficient quantum processors capable of solving complex problems in the field of quantum communication, AI, and computing that are inaccessible to classical computers.
Quantum computers promise to revolutionize the way we solve problems that even supercomputers can’t handle, from modeling molecules to optimizing global logistics systems. However, their development is hampered by the problem of quantum communication: qubits are extremely sensitive to external influences and quickly lose their quantum properties, such as superposition (the ability to be in the “0” and “1” states simultaneously).
Scientists from Moscow State University and Lobachevsky State University have proposed a hybrid system based on adiabatic quantum parametrons — superconducting elements controlled by a magnetic field. When cooled to ultra-low temperatures, these elements can be in stable quantum states ("0", "1" or their superpositions), which makes them ideal for storing information.
The key advantage of the method is the use of one physical process (superconducting current circulation) for storing and transmitting information. This makes the system more compact and energy efficient compared to traditional resonators.
According to project manager Marina Bastrakova, this technology will accelerate the implementation of quantum computing, reduce the cost of systems and simplify their scaling. In addition, it can be useful for creating hybrid quantum-neuromorphic computing platforms that combine the advantages of quantum and neural network technologies.
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