Quantum interference in the service of information technology

phys.org | 7/16/2019 | Staff
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Scientists from the Faculty of Physics, University of Warsaw, in collaboration with the University of Oxford and NIST, have shown that quantum interference enables processing of large sets of data faster and more accurately than with standard methods. Their studies may boost applications of quantum technologies in artificial intelligence, robotics and medical diagnostics, for example. The results of this work have been published in Science Advances.

Contemporary science, medicine, engineering and information technology demand efficient processing of data—still images, sound and radio signals, as well as information coming from different sensors and cameras. Since the 1970s, this has been achieved by means of the Fast Fourier Transform algorithm (FFT). The FFT makes it possible to efficiently compress and transmit data, store pictures, broadcast digital TV, and talk over a mobile phone. Without this algorithm, medical imaging systems based on magnetic resonance or ultrasound would not have been developed. However, it is still too slow for many demanding applications.

Goal - Scientists - Years - Mechanics - Development

To meet this goal, scientists have been trying for years to harness quantum mechanics. This resulted in the development of a quantum counterpart of the FFT, the Quantum Fourier Transform (QFT), which can be realized with a quantum computer. As the quantum computer simultaneously processes all possible values (so-called "superpositions") of input data, the number of operations decreases considerably.

In spite of the rapid development of quantum computing, there is a relative stagnation in the field of quantum algorithms. Now scientists have shown that this result can be improved, and in a rather surprising way.

Mathematics - Transforms - Kravchuk - Transform - FFT

Mathematics describes many transforms. One of them is a Kravchuk transform. It is very similar to the FFT, as it allows processing of discrete (e.g. digital) data, but uses Kravchuk functions to decompose the input sequence into the spectrum. At the end of the 1990s, the Kravchuk transform was "rediscovered" in...
(Excerpt) Read more at: phys.org
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