Quantum image with high retrieval performance

Ruan, Yue; Chen, Hanwu; Liu, Zhihao; Tan, Jianing

doi:10.1007/s11128-015-1208-5

Cited by 7 publications

(1 citation statement)

References 12 publications

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“…Wang et al extended FRQI to polar coordinates and replaced pixels' spatial position with polar diameters and polar angles [11,12]. Ruan et al expressed the pixel's gray value as |0〉 + e iθ |1〉, replacing the cos θ|0〉 + sin θ|1〉 that stored the gray value in FRQI [13], which actually replaced the ZOX plane's rotation on Bloch ball with a rotation of θ angle in the XOY plane. All these extended schemes' basic idea is to prepare the image into a quantum superposition state in terms of pixels' spatial distribution information, which is not fundamentally different from FRQI.…”

Section: Quantum Image Formatmentioning

confidence: 99%

Quantum Image Processing: Opportunities and Challenges

Ruan

Xue

Shen

2021

Mathematical Problems in Engineering

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Quantum image processing (QIP) is a research branch of quantum information and quantum computing. It studies how to take advantage of quantum mechanics’ properties to represent images in a quantum computer and then, based on that image format, implement various image operations. Due to the quantum parallel computing derived from quantum state superposition and entanglement, QIP has natural advantages over classical image processing. But some related works misuse the notion of quantum superiority and mislead the research of QIP, which leads to a big controversy. In this paper, after describing this field’s research status, we list and analyze the doubts about QIP and argue “quantum image classification and recognition” would be the most significant opportunity to exhibit the real quantum superiority. We present the reasons for this judgment and dwell on the challenges for this opportunity in the era of NISQ (Noisy Intermediate-Scale Quantum).

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Section: Quantum Image Formatmentioning

confidence: 99%

Quantum Image Processing: Opportunities and Challenges

Ruan

Xue

Shen

2021

Mathematical Problems in Engineering

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Quantum computation for large-scale image classification

Ruan

Chen

Tan

et al. 2016

Quantum Inf Process

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A hybrid classical-quantum algorithm for digital image processing

Shukla

Vedula

2022

Quantum Inf Process

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Quantum state preparation involving a uniform superposition over a non-empty subset of n-qubit computational basis states is an important and challenging step in many quantum computation algorithms and applications. In this work, we address the problem of preparation of a uniform superposition state of thewhere M denotes the number of distinct states in the superposition state and 2 ≤ M ≤ 2 n . We show that the superposition state |Ψ ⟩ can be efficiently prepared with a gate complexity and circuit depth of only O(log 2 M) for all M. This demonstrates an exponential reduction in gate complexity in comparison to other existing approaches in the literature for the general case of this problem.Another advantage of the proposed approach is that it requires only n = ⌈log 2 M⌉ qubits. Furthermore, neither ancilla qubits nor any quantum gates with multiple controls are needed in our approach for creating the uniform superposition state |Ψ ⟩. It is also shown that a broad class of nonuniform superposition states that involve a mixture of uniform superposition states can also be efficiently created with the same circuit configuration that is used for creating the uniform superposition state |Ψ ⟩ described earlier, but with modified parameters.

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Quantum image with high retrieval performance

Cited by 7 publications

References 12 publications

Quantum Image Processing: Opportunities and Challenges

Quantum Image Processing: Opportunities and Challenges

Quantum computation for large-scale image classification

A hybrid classical-quantum algorithm for digital image processing

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