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- W3193259085 abstract "Abstract In the past few decades, quantum computation has become increasingly attractivedue to its remarkable performance. Quantum image scaling is considered a common geometric transformation in quantum image processing, however, the quantum floating-point data version of which does not exist. Is there a corresponding scaling for 2-D and 3-D floating-point data? The answer is yes.In this paper, we present quantum scaling up and down scheme for floating-point data by using trilinear interpolation method in 3-D space. This scheme offers better performance (in terms of the precision of floating-point numbers) for realizing the quantum floating-point algorithms compared to previously classical approaches. The Converter module we proposed can solve the conversion of fixed-point numbers to floating-point numbers of arbitrary size data with p + q qubits based on IEEE-754 format, instead of 32-bit single-precision, 64-bit double precision or 128-bit extended-precision. Usually, we use nearest neighbor interpolation and bilinear interpolation to achieve quantum image scaling algorithms, which are not applicable in high-dimensional space. This paper proposes trilinear interpolation of floating-point numbers in 3-D space to achieve quantum algorithms of scaling up and down for 3-D floating-point data. Finally, the circuits of quantum scaling up and down for 3-D floating-point data are designed." @default.
- W3193259085 created "2021-08-16" @default.
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- W3193259085 date "2021-08-04" @default.
- W3193259085 modified "2023-09-23" @default.
- W3193259085 title "Scaling Up and Down of 3-D Floating-point Data in Quantum Computation" @default.
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- W3193259085 doi "https://doi.org/10.21203/rs.3.rs-757209/v1" @default.
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