Version 1
: Received: 30 September 2023 / Approved: 2 October 2023 / Online: 3 October 2023 (03:09:23 CEST)
How to cite:
Werner, K.; Wereszczyński, K.; Potempa, R.; Cyran, K.A. Deep learning as a tool of quantum error reduction in quantum image processing. Preprints2023, 2023100073. https://doi.org/10.20944/preprints202310.0073.v1
Werner, K.; Wereszczyński, K.; Potempa, R.; Cyran, K.A. Deep learning as a tool of quantum error reduction in quantum image processing. Preprints 2023, 2023100073. https://doi.org/10.20944/preprints202310.0073.v1
Werner, K.; Wereszczyński, K.; Potempa, R.; Cyran, K.A. Deep learning as a tool of quantum error reduction in quantum image processing. Preprints2023, 2023100073. https://doi.org/10.20944/preprints202310.0073.v1
APA Style
Werner, K., Wereszczyński, K., Potempa, R., & Cyran, K.A. (2023). Deep learning as a tool of quantum error reduction in quantum image processing. Preprints. https://doi.org/10.20944/preprints202310.0073.v1
Chicago/Turabian Style
Werner, K., Rafał Potempa and Krzysztof A Cyran. 2023 "Deep learning as a tool of quantum error reduction in quantum image processing" Preprints. https://doi.org/10.20944/preprints202310.0073.v1
Abstract
Quantum image representation is a widely researched area of quantum computing. Currently developed methods use angle parameter of the rotation gate (e.g., the FRQI method), sequences of qubits (ex. NEQR method) or phase shift (ex. LPIQE method) for storing color information of pixels. All of those methods are affected by decoherence and other classical and quantum noise, which is an inseparable part of quantum computing in a NISQ (Noisy Intermediate Scale Quantum) era. These all phenomenons influence the measurements, its probability distribution over the measurement basis and, as the result, makes the extracted images not even similar to those, which was stored in quantum computers. Since this process is, in its foundation, quantum as well, the computational reversal of this process is possible. There are a lot of methods for error correction, mitigation and reduction, but all of them use quantum computer time, or additional qubits to achieve desired result. We report a successful use of the Generative Adversarial Network tuned for image-to-image translation in conjunction with PDU method, for error reduction in images encoded using LIPQE (Local Phase Image Quantum Encoding) method.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
