Abstract
Quantum programs allow to process multiple bits of information at the same time, which is useful in multidimensional data handling. Images are an example of such multidimensional data. Our work reviews 14 quantum image encoding works and compares implementations of 8 of them by 3 metrics: number of utilized qubits, quantum circuit depth, and quantum volume. Our work includes a practical comparison of 2n × 2n images encoding, where n varies from 1 up to 8. We observed that Qubit Lattice approach shows the minimum circuit depth as well as quantum volume, Flexible Representation of Quantum Images (FRQI) utilizes the minimum number of qubits. If to talk about variety of processing techniques, FRQI and Novel Enhanced Quantum Representation (NEQR) representations are the most fruitful. As far as quantum computers are limited in qubit number, we concluded that almost all approaches except Qubit Lattice are promising for the near future of quantum image representation and processing. From the point of view of the quantum depth, discrete methods showed the most appropriate result.
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Data sharing not applicable to this article as no datasets were generated or analysed during the current study.
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The python code is available in the GitHub repository https://github.com/UralmashFox/QPI.
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Marina Lisnichenko had the idea for the article, performed the literature search and data analysis. Stanislav Protasov critically revised the work.
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Lisnichenko, M., Protasov, S. Quantum image representation: a review. Quantum Mach. Intell. 5, 2 (2023). https://doi.org/10.1007/s42484-022-00089-7
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DOI: https://doi.org/10.1007/s42484-022-00089-7