A hybrid-qudit representation of digital RGB images
Year: 2023
Authors: Das S., Caruso F.
Autors Affiliation: Univ Florence, Dept Phys & Astron, Via Sansone 1, I-50019 Sesto Fiorentino, Italy; Univ Florence, European Lab Nonlinear Spect LENS, Via Nello Carrara 1, I-50019 Sesto Fiorentino, Italy; QSTAR, Largo Enrico Fermi 2, I-50125 Florence, Italy; CNR INO, Largo Enrico Fermi 2, I-50125 Florence, Italy.
Abstract: Quantum image processing is an emerging topic in the field of quantum information and technology. In this paper, we propose a new quantum image representation of RGB images with deterministic image retrieval, which is an improvement over all the similar existing representations in terms of using minimum resource. We use two entangled quantum registers constituting of total 7 qutrits to encode the color channels and their intensities. Additionally, we generalize the existing encoding methods by using both qubits and qutrits to encode the pixel positions of a rectangular image. This hybrid-qudit approach aligns well with the current progress of NISQ devices in incorporating higher dimensional quantum systems than qubits. We then describe the image encoding method using higher-order qubit-qutrit gates, and demonstrate the decomposition of these gates in terms of simpler elementary gates. We use the Google Cirq’s quantum simulator to verify the image preparation in both the ideal noise-free scenario and in presence of realistic noise modelling. We show that the complexity of the image encoding process is linear in the number of pixels. Lastly, we discuss the image compression and some basic RGB image processing protocols using our representation.
Journal/Review: SCIENTIFIC REPORTS
Volume: 13 (1) Pages from: to:
More Information: The work was financially supported by the European Union’s Horizon 2020 research and innovation programme under FET-OPEN Grant Agreement No. 828946 (PATHOS). We acknowledge the use of Google Cirq platform.KeyWords: Quantum Computation; CircuitsDOI: 10.1038/s41598-023-39906-9Citations: 2data from “WEB OF SCIENCE” (of Thomson Reuters) are update at: 2024-12-08References taken from IsiWeb of Knowledge: (subscribers only)Connecting to view paper tab on IsiWeb: Click hereConnecting to view citations from IsiWeb: Click here