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A quantum image segmentation method based on neqr expression

A quantum image and image segmentation technology, applied in image analysis, image data processing, instruments, etc., can solve problems such as increasing the complexity of quantum image reading, and achieve the effects of high parallelism, improved capability, and improved performance

Active Publication Date: 2021-12-28
CHONGQING UNIV OF POSTS & TELECOMM
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, research in this field has also encountered difficulties common to quantum computing. Most quantum image processing algorithms can speed up the process of image processing, but if you want to show people the results of image processing, you need to convert them into classical images, and transforming classical The image process requires quantum measurement, and the collapse effect of quantum measurement increases the complexity of quantum image reading, so it is urgent to design effective quantum image processing algorithms

Method used

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  • A quantum image segmentation method based on neqr expression
  • A quantum image segmentation method based on neqr expression
  • A quantum image segmentation method based on neqr expression

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Experimental program
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Effect test

Embodiment 1

[0046] This embodiment proposes a quantum image segmentation method based on NEQR expression, which specifically includes the following steps:

[0047] Step S1, preparing the quantum image NEQR expression.

[0048] Specifically in this embodiment, step S1 is specifically:

[0049] The first step is to obtain the basic information of the image, including converting the grayscale information of the image into the binary digit size m, and the size information of the image 2 n ×2 n ; In this embodiment, to prepare an image f with a size of 4×4 and a gray value range of [0, 255] 4×4 For example, this image is:

[0050]

[0051] The second step is to set the corresponding qubits according to the basic information of the graph, wherein the gray level information is m qubits, the position information is 2n qubits, and the auxiliary bit is 2 qubits; for example, the above image f 4×4 , the size of m is 8, and n is 2, that is, the gray information needs 8 bits, the position infor...

Embodiment 2

[0073] In this embodiment, the experimental simulation of the quantum image segmentation method proposed in Embodiment 1 is realized by means of a quantum programming language under a classical computer, specifically including:

[0074]The first step is to prepare a quantum image system. First, according to step S1 in the method proposed in Example 1, a quantum image system based on the NEQR expression is prepared. The system is in a linear superposition state when it is not measured. is a 4×4 image g with a gray value in the range [0, 7] 4×4 For example, this image is represented as:

[0075]

[0076] When the preparation is completed, 16 states in the quantum image system with 9 qubits will exist simultaneously, such as Figure 4 (a) shows the superposition state representation of the NEQR quantum image produced by the preparation method in step S1 of the above-mentioned embodiment 1, and the output data of the qubit string from left to right corresponds to the input of ...

Embodiment 3

[0085] In Example 3, the same method as in Example 2 is used to perform the same quantum image segmentation experiment on a quantum image with a size of 8×8 and a gray value range of [0, 7]. The thresholds are still 2 and 6, and Figure 7 Probability histograms of measurements taken after the split shown, and Figure 8 The image comparison before and after the segmentation is shown, where Figure 8 Schematic diagram of the pre-segmentation image shown in (a), resulting in Figure 8 (b) shows a schematic diagram of the image after the Z word is segmented. Table 2 shows the simulation results of quantum image segmentation with a size of 8×8 and a gray value range of [0, 7], including the running and compiling time, the number of measurements and the number of basic quantum logic gates.

[0086] Table 2 Experimental data after different quantum image operations

[0087] Quantum image manipulation image size time(s) Measurement times Number of quantum gates ...

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Abstract

The invention discloses a quantum image segmentation method based on NEQR expression. The method comprises: step S1, preparing a quantum image NEQR expression; step S2, designing a quantum circuit of a threshold quantum image segmentation algorithm, and performing the quantum image expression prepared in step S1. Segmentation processing; step S3, measuring the expression of the quantum image processed in step S2 to obtain the probability information of each state in the quantum image. The invention optimizes the quantum image NEQR expression of the original graphics, optimizes the number of auxiliary qubits in the quantum expression circuit of the original image, and can reduce the number of qubits in the quantum expression circuit by multiplexing the auxiliary qubits The number greatly improves the performance of the quantum image expression algorithm, makes it easier to realize the simulation under the classical computer, and provides the possibility to process the quantum image of a larger size, and improves the ability of the classical computer to process the quantum image algorithm.

Description

technical field [0001] The invention relates to the technical field of quantum image processing, in particular to a quantum image segmentation method based on NEQR expression. Background technique [0002] Quantum image processing is a technology for image processing in a quantum computer. The difference between a quantum computer and a classical computer is that its information unit is not a bit (bit) containing only 0 and 1, but a quantum bit (qubit). The state can be 0, 1 or a superposition of 0 and 1. Therefore, the quantum CPU has powerful parallel storage and data processing capabilities, and its storage and computing capabilities increase exponentially with the increase in the number of quantum processors. Therefore, with the massive increase of image data and the increasingly high real-time requirements of image processing, classical computers will not be able to meet the needs of image processing, and traditional image processing algorithms cannot be implemented on...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/136
CPCG06T7/136
Inventor 袁素真文超王艳莫小红符正欣陈柯润张露元张维博赵延明李维谭森文张乐怡谭雁婷
Owner CHONGQING UNIV OF POSTS & TELECOMM
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