Cesar Pronin, a senior lecturer at the Moscow Automobile and Road Construction State Technical University (MADI), has developed a new tool for quantum computing. According to the Russian Ministry of Science and Higher Education, this method allows quantum computers to train neural networks more than 300 times faster than classical systems.
The researcher proposed a quantum oracle, an algorithm that directs computations toward the correct answer without exhaustive searching. By using a reversible quantum multiplier as an oracle, the approach builds on quantum analogs of basic logic operations such as NOT, AND, OR, and XOR. This framework supports the training of a quantum perceptron.
Calculations indicate that the Grover algorithm, when utilizing this new component, achieves results approximately 326 times faster than classical methods on a 16-qubit register. Future development will focus on scaling for 2-3 qubit registers, adapting to NISQ devices, and addressing quantum arithmetic bit-depth requirements.
The research aims to establish a theoretical foundation for quantum processors to handle real-world industrial and social tasks. By processing large datasets in fractions of a second, this technology seeks to move beyond abstract applications toward practical implementation in various sectors.
