A Generalized Floating-Point Representation and Manipulation of Quantum Signals Based on IEEE-754

Zhang, Rui; Lu, Dayong; Yin, Haiting

doi:10.1007/s10773-019-04379-y

Cited by 4 publications

(3 citation statements)

References 22 publications

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“…10 . module Zhang et al 12 design the divided by 2 module in 2020. The divided by 2 module is to make a floating-point number to , where .…”

Section: Quantum Scaling Up and Downmentioning

confidence: 99%

“…Researches in quantum image processing field started with proposals on quantum image representations such as Qubit Lattice 4 , Real Ket 5 and Entangled Image 6 . After 2011, some more practical quantum image models using entangled states (FRQI 7 ) and superposition states (NEQR 8 , INEQR 9 , GQIR 10 , QRDS 11 , GFPRQS 12 , QR2-DD 13 , etc.) have been proposed.…”

Section: Introductionmentioning

confidence: 99%

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Scaling up and down of 3-D floating-point data in quantum computation

Sun

2022

Sci Rep

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In the past few decades, quantum computation has become increasingly attractive due 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 a 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 than 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 $$p+q$$ p + q qubits based on IEEE-754 format, instead of 32-bit single-precision, 64-bit double-precision and 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 data in 3-D space to achieve quantum algorithms of scaling up and down for 3-D floating-point data. Finally, the quantum scaling circuits of 3-D floating-point data are designed.

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“…10 . module Zhang et al 12 design the divided by 2 module in 2020. The divided by 2 module is to make a floating-point number to , where .…”

Section: Quantum Scaling Up and Downmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Scaling up and down of 3-D floating-point data in quantum computation

Sun

2022

Sci Rep

Self Cite

View full text Add to dashboard Cite

show abstract

“…Researches in quantum image processing field started with proposals on quantum image representations such as Qubit Lattice [4], Real Ket [5] and Entangled Image [6]. After 2011, some more practical quantum image models using entangled states and superposition states have been proposed including FRQI [7], NEQR [8], INEQR [9], GQIR [10], QRDS [11], GFPRQS [12], QR2-DD [13], etc.…”

Section: Introductionmentioning

confidence: 99%

Scaling Up and Down of 3-D Floating-point Data in Quantum Computation

Sun

2021

Preprint

Self Cite

View full text Add to dashboard Cite

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.

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Quantum Windowed Fourier Transform and its Application to Quantum Signal Processing

Yin

Zhang

2021

Int J Theor Phys

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A Generalized Floating-Point Representation and Manipulation of Quantum Signals Based on IEEE-754

Cited by 4 publications

References 22 publications

Scaling up and down of 3-D floating-point data in quantum computation

Scaling up and down of 3-D floating-point data in quantum computation

Scaling Up and Down of 3-D Floating-point Data in Quantum Computation

Quantum Windowed Fourier Transform and its Application to Quantum Signal Processing

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