Image enhancement method and system of adaptive quantum circuit, storage medium and equipment

Abstract

The invention discloses an image enhancement method and system of an adaptive quantum circuit, a storage medium and equipment, and belongs to the technical field of image enhancement, and the method comprises the steps: S1, collecting different types of image data sets; s2, preprocessing images in the image data set; s3, based on the preprocessed image data, designing an initial quantum circuit in combination with image features; s4, performing coarse-grained screening on the initial quantum circuit, and selecting a quantum circuit framework most suitable for the current image feature; s5, performing fine-grained optimization on the selected quantum circuit architecture, and performing fine tuning on parameters in the quantum circuit architecture; and S6, the optimized quantum circuit outputs an enhanced image through quantum measurement. The adaptive quantum circuit can dynamically adjust the circuit architecture and parameters according to different image characteristics and enhancement task requirements, ensures that each image task can obtain the optimal enhancement effect, and breaks through the limitations of fixed architecture and poor adaptability of a traditional image enhancement method.

Description

Technical Field

[0001] The present invention relates to the field of quantum computing and image enhancement technology, and in particular to an image enhancement method, system, storage medium and device for an adaptive quantum circuit. Background Art

[0002] The rapid development of quantum computing technology has opened up new research opportunities for image processing. Leveraging the superposition and entanglement of quantum states, quantum computing offers parallel processing capabilities that surpass those of traditional computing. This capability is particularly advantageous in modeling high-dimensional data and computing complex tasks. Currently, quantum algorithms have been initially applied to image classification, image recognition, and similarity detection. However, these studies remain largely theoretical and have yet to see widespread practical application.

[0003] Image enhancement, a crucial technology for improving image quality, has broad applications in fields such as medical image analysis, remote sensing image processing, and industrial inspection. Traditional image enhancement techniques primarily rely on classic deep learning models, such as convolutional neural networks (CNNs) and generative adversarial networks (GANs). While these methods perform well on certain tasks, their fixed network architecture and reliance on manual parameter tuning make them difficult to adapt to diverse image processing needs. The enhancement effect is often limited, particularly in complex scenes or high-noise images.

[0004] Quantum computing combined with image enhancement technology is expected to surpass traditional methods in efficiency and effectiveness through its unique computing properties. However, existing quantum image enhancement methods generally have the following problems: 1. Lack of adaptability: Existing quantum circuit designs typically have fixed structures, making them difficult to dynamically adjust based on the characteristics of the input image and task requirements. This fixed architecture can limit the enhancement effect, especially when dealing with diverse image data, where lack of adaptability becomes a major bottleneck.

[0005] 2. High optimization difficulty: The optimization of parameterized quantum gates in quantum circuits relies on complex quantum gradient calculations, and existing optimization strategies lack guidance based on task characteristics, resulting in low optimization efficiency.

[0006] 3. Hardware resource limitations: The constraints on the number of quantum bits and the depth of quantum gates make it difficult for existing methods to process large-scale image data, affecting the practical usability of the enhancement algorithm. Summary of the Invention

[0007] The purpose of the present invention is to overcome the technical problems existing in the prior art and provide an image enhancement method, system, storage medium and device based on adaptive quantum circuits, specifically providing efficient solutions for diverse image enhancement tasks. The present invention significantly improves the efficiency and effectiveness of image enhancement through the intelligent design and optimization of quantum circuits.

[0008] The object of the present invention is achieved through the following technical solutions: First, the present invention designs a quantum circuit consisting of a basic set of quantum gates {RZ, RY, RX, CNOT, H}. The architecture and depth of this quantum circuit are dynamically adjusted through an adaptive optimization process. This adaptive optimization process consists of two parts: coarse-grained screening (architecture) and fine-grained optimization (parameters). Coarse-grained screening selects the appropriate circuit architecture, while fine-grained optimization fine-tunes the parameters in the quantum circuit to achieve optimal results for the image enhancement task.

[0009] In practice, the quantum circuit architecture consists of multiple parameterized quantum gates, whose combination and depth are adjusted based on task requirements. Through optimization techniques such as reinforcement learning or evolutionary algorithms, the quantum circuit architecture and gate parameters are dynamically adjusted based on the characteristics of the input image and the requirements of the augmented task. Specifically, a coarse-grained screening method is first used to generate multiple candidate quantum circuit architectures. These candidate architectures are then evaluated for performance in small-scale experimental simulations. Through operations such as selection, crossover, and substitution, the diversity and adaptability of the quantum circuit architectures are continuously optimized, ultimately selecting the one with the best performance.

[0010] The fine-grained optimization phase further refines the selected quantum circuit architecture, optimizing the rotation angles of the quantum gates and other circuit parameters. Using quantum gradient optimization, the impact of each parameter on the image enhancement is accurately calculated. The multi-task loss function employed during this optimization process incorporates both pixel-level and perceptual loss components to minimize the difference between the enhanced image and the target image while maintaining visual consistency and detail.

[0011] In practical applications of image enhancement, the input image is first converted into a quantum state through quantization and then processed by an optimized quantum circuit. After processing, the quantum state output by the quantum circuit is measured and converted into classical information, generating an enhanced image. Post-processing can then be used to adjust details of the output image, such as restoring resolution and color balance, to achieve the desired image enhancement effect. Furthermore, to verify the effectiveness of quantum circuit-based image enhancement methods, performance evaluation is necessary.

[0012] Compared with the prior art, the present invention has the following beneficial effects: 1. By combining a designed adaptive quantum circuit with a quantum gradient optimization algorithm, the present invention significantly improves the efficiency and effectiveness of image enhancement. The adaptive quantum circuit can dynamically adjust the circuit architecture and parameters according to different image characteristics and enhancement task requirements, thereby ensuring that each image task can achieve the best enhancement effect. The parallelism and nonlinear characteristics of quantum computing further accelerate the calculation process, making it possible to significantly reduce calculation time and improve processing efficiency when processing complex images. The architectural depth and combination of quantum circuits have been intelligently optimized, making the enhancement effect more refined. In particular, in low-contrast, high-noise, or low-resolution image enhancement, quantum circuits show stronger detail recovery capabilities than traditional methods.

[0013] 2. The multi-task loss function employed in this paper, including pixel-level and perceptual loss, comprehensively considers intuitive image differences while preserving high-level features, resulting in image enhancement results that more closely resemble the visual quality of the target image. This multi-dimensional loss function design not only improves the accuracy of the enhancement effect but also enhances the adaptability of the quantum circuit to different image types, thereby ensuring higher image quality and wider application applicability.

[0014] 3. The quantum circuit optimization method of this invention enables adaptive adjustments across diverse image enhancement tasks, avoiding the limitations of a fixed circuit architecture. Through continuous optimization iterations, the invention can flexibly address a variety of complex tasks, such as denoising, super-resolution reconstruction, and detail enhancement, ensuring the continuous improvement of quantum circuits and enhanced enhancement effects.

[0015] 4. The implementation of this invention will provide new breakthroughs for the further development of quantum image enhancement technology, possessing significant scientific significance and broad application prospects. By fully leveraging the powerful potential of quantum computing, this invention not only improves the efficiency and accuracy of image enhancement but also provides a highly efficient and precise new tool for quantum image processing, promoting advancement and innovation in related technologies. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a simplified flow chart of an image enhancement method using an adaptive quantum circuit according to the present invention; Figure 2 This is a schematic diagram of the coarse-grained screening process of the present invention; Figure 3 Schematic diagram of quantum convolution circuit of circuit structure 2 of the present invention; Figure 4 Schematic diagram of the fine-grained screening process of the present invention. DETAILED DESCRIPTION

[0017] The technical solutions of the present invention are described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present application generally described and shown in the drawings herein can be arranged and designed in various different configurations. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0018] It should be noted that the defects existing in the solutions in the above-mentioned prior art are the results obtained by the inventor after practice and careful research. Therefore, the discovery process of the above-mentioned problems and the solutions proposed in the embodiments of this application below for the above-mentioned problems should be the contributions made by the inventor to this application in the process of invention and creation, and should not be understood as technical contents known to technical personnel in this field.

[0019] In response to the technical problems pointed out in the background technology, the embodiments provided by the present invention are as follows: Reference Figure 1 In an exemplary embodiment, a method for image enhancement of an adaptive quantum circuit is provided, comprising the following steps: S1. Collect different types of image datasets; S2, preprocessing the images in the image dataset; S3. Design the initial quantum circuit based on the preprocessed image data and image features; S4. Perform coarse-grained screening on the initial quantum circuits and select the quantum circuit architecture that best suits the current image features; S5. Perform fine-grained optimization on the selected quantum circuit architecture and fine-tune the parameters in the quantum circuit architecture; S6. The optimized quantum circuit outputs an enhanced image through quantum measurement.

[0020] The collection of image datasets in step S1 is the basis for ensuring the efficiency and reliability of quantum circuit optimization. First, we select various types of image datasets (such as natural images, medical images, etc.) and preprocess the images in step S2. The preprocessing includes: The image is denoised, normalized, and resized in sequence to ensure data quality is suitable for subsequent quantum circuit processing; The adjusted image data is converted into quantum states.

[0021] Specifically, denoising: use Gaussian filtering or median filtering to remove noise, expressed as: in, is the denoised image, is the original image.

[0022] Normalization: Mapping pixel values ​​to the range [0,1]: in, and are the minimum and maximum values ​​of the original image, respectively.

[0023] Resizing: Resize images to a uniform size by cropping or scaling: These steps ensure that the image is adapted to quantum computing requirements, improves image quality, and is ready to be input into the quantum circuit for further processing. Specifically, the adjusted image data is converted into a quantum state. In the framework of quantum computing, image data quantization is a key step in converting classical image data into a quantum state suitable for quantum circuit processing. Through quantization operations, the pre-processed image data is mapped to quantum bits, and then optimized using quantum algorithms. Currently, amplitude coding is one of the commonly used quantization methods. In amplitude coding, each pixel value of the image is is mapped to the amplitude of the quantum bit and expressed as: Among them, among them, Represents the square root of the normalized pixel value. Finally, through this encoding method, each pixel of the image is converted into the amplitude of the quantum bit, forming the quantum circuit input.

[0024] Furthermore, the design of the quantum circuit is one of the core steps of the present invention. Step S3 specifically includes: Based on the processed image data and image features (such as texture and edge information), an initial quantum circuit is designed. This circuit is composed of basic quantum gates (such as RZ, RY, RX, H, and CNOT) to perform quantum encoding and basic quantum operations on the image data, enabling efficient image processing.

[0025] In order to further improve the adaptability of the initial quantum circuit to different image features, the present invention introduces reinforcement learning and evolutionary algorithms to assist in guiding the combination, arrangement and parameter initialization of quantum gates, thereby dynamically generating a quantum circuit architecture with better initial performance.

[0026] The initial quantum circuit can be simply expressed as: in, Representative A quantum gate, The initial quantum circuit is mainly used to establish the framework of quantum processing and process basic quantum transformations of images.

[0027] The optimization process of quantum circuits consists of two parts: coarse-grained screening and fine-grained training, aiming to further improve the image enhancement effect. The coarse-grained screening of the initial quantum circuit described in step S4 includes: generating multiple candidate quantum circuit architectures; Use simulation experiments to quickly evaluate each candidate quantum circuit architecture; Through selection, crossover and replacement operations, the diversity and adaptability of candidate quantum circuit architectures are continuously optimized.

[0028] Specifically, the coarse-grained screening phase involves selecting the quantum circuit architecture that best suits the current image characteristics from multiple candidate quantum circuit architectures. This process relies heavily on the efficiency of quantum computing and its ability to adapt to image characteristics.

[0029] Reference Figure 2 Taking the quantum convolution circuit structure as an example, its structure consists of multiple "quantum convolution layers," each of which typically includes the following steps: encoding, parameterized gate operations, entanglement operations, and measurement. First, multiple different quantum convolution circuit structures are designed and generated as elements of the search space. For example, circuit structure 1 includes two convolution layers and linear superposition, circuit structure 2 includes three convolution layers and local entanglement, and circuit structure 3 includes two convolution layers, global entanglement, and a nonlinear measurement mechanism.

[0030] During the search process, simulations are used to quickly evaluate each circuit structure. This evaluation is performed on a small sample of images with randomly initialized parameters, and its performance on image enhancement tasks is observed over a small number of iterations. Common performance metrics include image quality indicators such as peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), while quantum resource overhead (such as the number of qubits, gate depth, and runtime) is also considered.

[0031] When comparing multiple circuit structures, the circuit structure that strikes the best balance between image quality improvement and resource consumption is selected as the foundation for subsequent fine-grained training. For example, circuit structure 2 demonstrated strong image enhancement capabilities and manageable resource consumption in preliminary evaluations, and was therefore selected as the foundation for subsequent fine-grained training.

[0032] like Figure 3 As shown in the figure, the circuit has 10 quantum bits and contains three layers of convolutional structure. Each layer contains RY, RZ parameterized rotation and local CNOT entanglement operations. The execution order and connection relationship of each layer of convolution and entanglement operations are shown, which are used to extract and fuse image features layer by layer.

[0033] Furthermore, in step S5, the selected quantum circuit architecture is fine-grained optimized, and the parameters of each quantum gate in the selected circuit are fine-tuned based on a quantum gradient optimization algorithm (such as the parameter shift rule, SPSA, etc.). This process uses a joint loss function as the optimization target, including pixel-level loss and perceptual loss. The loss function of the fine-grained optimization process is expressed as: in, is the loss function, is the enhanced image output by the quantum circuit. pixels, The target image is pixels, are the quantum circuit parameters, is the perceptual loss, which aims to measure the difference between the enhanced image and the target image in high-level features. is a hyperparameter that adjusts the weight between the pixel-level loss and the perceptual loss. Here, the pixel-level loss (using mean square error (MSE)) focuses on accurate reconstruction of each pixel, while the perceptual loss (using a pre-trained neural network to extract high-level features) focuses on the overall structure and perceived quality of the image. By jointly optimizing these two losses, the quantum circuit will be assisted to further enhance image detail and perceptual quality.

[0034] Taking the quantum circuit of the above circuit structure 2 as an example, refer to Figure 4 ,The specific process of fine-grained parameter optimization is: a) Parameter initialization: In the circuit structure 2, each RY revolving gate and RZ revolving gate has a trainable parameter, and all parameters are initialized randomly in a small range.

[0035] b) Forward computation: The input image is encoded and fed into the circuit, where it is executed using the current parameters to produce the output image. The quantum circuit sequence of Circuit Structure 2 is followed: each qubit is subjected to parameterized RY and RZ rotation gates, followed by local CNOT entanglement (e.g., q0→q1, q2→q3, etc.). After three layers of convolution, a measurement is performed to produce the quantum state output.

[0036] c) Loss function calculation: The measurement result is decoded into an enhanced image, compared with the target image, and the pixel-level loss and perceptual loss between the output image and the target image are calculated.

[0037] d) Dynamic parameter optimization: Select an appropriate optimizer (such as SPSA, Adam, etc.), calculate the quantum gradient or approximate gradient of the loss function relative to each parameter (such as the RY and RZ gate rotation angles), and dynamically adjust the parameter value in real time according to the optimizer's update rules. For example, in At the iteration, Will be updated to .

[0038] e) Iterative convergence: Repeat the above steps until the loss function converges or the maximum number of iterations is reached, and finally obtain a set of optimal quantum circuit parameters.

[0039] Through the above steps, the quantum circuit can adaptively adjust parameters and ultimately output high-quality, more detailed enhanced images.

[0040] Furthermore, step S6 performs image restoration on the optimized quantum circuit. The optimized quantum circuit performs quantum measurement and outputs an enhanced image. This process is based on the principle of quantum measurement. After measurement, the quantum state is converted into classical image data, completing image restoration and enhancement.

[0041] The output of the quantum circuit and the image restoration process can be expressed as: in, is the enhanced image of the quantum circuit output, is the quantum measurement operator, It is the quantum state after the execution of the quantum circuit.

[0042] Furthermore, to verify the effectiveness of quantum circuit-based image enhancement methods, performance evaluation is required. The evaluation criteria mainly include image quality indicators such as PSNR and SSIM, and comparison with traditional methods is performed.

[0043] The commonly used evaluation index formula is as follows: PSNR (Peak Signal-to-Noise Ratio): PSNR measures the quality of image reconstruction and is the logarithmic ratio of the error between the original image and the enhanced image, expressed in decibels (dB). The formula is as follows: in, Maximize the pixel value of the image. is the mean square error, which is used to measure the error between the original image pixel value and the enhanced image pixel value.

[0044] SSIM (Structural Similarity Index) structural similarity index: used to evaluate the similarity of images in terms of brightness, contrast, and structure, with a value range of , the closer the value is to The higher the similarity, the better. The formula is as follows: in, Representing an image and The average value of Representing an image and The variance of Representing an image and The covariance of is a small constant introduced to prevent the denominator from being zero.

[0045] In another exemplary embodiment, based on the same inventive concept as the method embodiment, an image enhancement system of an adaptive quantum circuit is provided, comprising: Image collection module, used to collect different types of image datasets; An image preprocessing module, used to preprocess images in an image dataset; The initial quantum circuit design module is used to design the initial quantum circuit based on the pre-processed image data and image features; The coarse-grained screening module is used to perform coarse-grained screening on the initial quantum circuit and select the quantum circuit architecture that best suits the current image characteristics; A fine-grained optimization module is used to perform fine-grained optimization on the selected quantum circuit architecture and fine-tune the parameters in the quantum circuit architecture; The enhanced image output module is used to perform quantum measurement on the optimized quantum circuit and output the enhanced image.

[0046] In another exemplary embodiment, based on the same inventive concept as the method embodiment, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the image enhancement method of the adaptive quantum circuit provided by the embodiment of the present invention is implemented. Based on this understanding, the technical solution of this embodiment, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0047] In another exemplary embodiment, based on the same inventive concept as the method embodiment, an electronic device is provided, including a memory and a processor, wherein the memory stores computer instructions that can be executed on the processor, and when the processor executes the computer instructions, the image enhancement method of the adaptive quantum circuit provided in the embodiment of the present invention is executed.

[0048] The processor may be a single-core or multi-core central processing unit or a specific integrated circuit, or one or more integrated circuits configured to implement the present invention.

[0049] Embodiments of the subject matter and functional operations described in this specification may be implemented in: tangibly embodied computer software or firmware, computer hardware including the structures disclosed in this specification and their structural equivalents, or a combination of one or more thereof. Embodiments of the subject matter described in this specification may be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on a tangible, non-transitory program carrier for execution by a data processing apparatus or to control the operation of the data processing apparatus. Alternatively or in addition, the program instructions may be encoded on an artificially generated propagated signal, such as a machine-generated electrical, optical, or electromagnetic signal, that is generated to encode and transmit information to a suitable receiver apparatus for execution by the data processing apparatus.

[0050] The processes and logic flows described in this specification can be performed by one or more programmable computers executing one or more computer programs to perform the corresponding functions by operating on input data and generating output. The processes and logic flows can also be performed by, and apparatus can be implemented as, special purpose logic circuitry, such as an FPGA (field programmable gate array) or an ASIC (application-specific integrated circuit).

[0051] Processors suitable for executing computer programs include, for example, general-purpose and / or special-purpose microprocessors, or any other type of central processing unit. Typically, a central processing unit will receive instructions and data from a read-only memory and / or random access memory. The basic components of a computer include a central processing unit for implementing or executing instructions and one or more memory devices for storing instructions and data. Typically, a computer will also include one or more mass storage devices for storing data, such as magnetic disks, magneto-optical disks, or optical disks, or the computer will be operably coupled to such a mass storage device to receive data from it or to transmit data to it, or both. However, a computer does not necessarily have such a device. In addition, a computer can be embedded in another device, such as a mobile phone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a global positioning system (GPS) receiver, or a portable storage device such as a universal serial bus (USB) flash drive, to name a few.

[0052] It should be understood that each box in the flow chart or block diagram can represent a module, program segment or a part of code, and the part of the module, program segment or code comprises one or more executable instructions for realizing the logical function of the provision. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the function or action of the provision, or can be implemented with a combination of dedicated hardware and computer instructions.

[0053] The above specific implementation methods are detailed descriptions of the present invention. It cannot be considered that the specific implementation methods of the present invention are limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, they can make several simple deductions and substitutions without departing from the concept of the present invention, which should be regarded as falling within the scope of protection of the present invention.

Claims

1. An image enhancement method using an adaptive quantum circuit, characterized in that: The following steps are involved: S1. Collect different types of image datasets; S2, preprocessing the images in the image dataset; S3. Design the initial quantum circuit based on the preprocessed image data and image features; S4. Perform coarse-grained screening on the initial quantum circuits and select the quantum circuit architecture that best suits the current image features; S5. Perform fine-grained optimization on the selected quantum circuit architecture and fine-tune the parameters in the quantum circuit architecture; S6. The optimized quantum circuit outputs an enhanced image through quantum measurement.

2. The image enhancement method of an adaptive quantum circuit according to claim 1, characterized in that: The preprocessing of images in the image dataset includes: Denoise, normalize, and resize the image in sequence; The adjusted image data is converted into quantum states.

3. The image enhancement method of an adaptive quantum circuit according to claim 1, characterized in that: The step of designing an initial quantum circuit in combination with image features includes: The initial quantum circuit is designed based on the texture and edges of the image, and the combination and arrangement of quantum gates are optimized by combining reinforcement learning and evolutionary algorithms.

4. The image enhancement method of an adaptive quantum circuit according to claim 1, characterized in that: The coarse-grained screening of the initial quantum circuit includes: generating multiple candidate quantum circuit architectures; Use simulation experiments to quickly evaluate each candidate quantum circuit architecture; Through selection, crossover and replacement operations, the diversity and adaptability of candidate quantum circuit architectures are continuously optimized.

5. The image enhancement method of an adaptive quantum circuit according to claim 1, characterized in that: Fine-grained optimization of selected quantum circuit architectures, including: Based on the quantum gradient optimization algorithm, the parameters of each quantum gate in the selected quantum circuit architecture are optimized and adjusted. The loss function of the optimization process includes pixel-level loss and perceptual loss.

6. The image enhancement method of an adaptive quantum circuit according to claim 5, characterized in that: The loss function of the optimization process is expressed as: ； in, is the loss function, is the enhanced image output by the quantum circuit. pixels, The target image is pixels, are the quantum circuit parameters, is the perceptual loss, It is a hyperparameter that adjusts the weight between pixel-level loss and perceptual loss.

7. The image enhancement method of an adaptive quantum circuit according to claim 1, characterized in that: Also includes: The performance is evaluated on the output enhanced images.

8. An image enhancement system based on an adaptive quantum circuit, characterized in that: include: Image collection module, used to collect different types of image datasets; An image preprocessing module, used to preprocess images in an image dataset; The initial quantum circuit design module is used to design the initial quantum circuit based on the pre-processed image data and image features; The coarse-grained screening module is used to perform coarse-grained screening on the initial quantum circuit and select the quantum circuit architecture that best suits the current image characteristics; A fine-grained optimization module is used to perform fine-grained optimization on the selected quantum circuit architecture and fine-tune the parameters in the quantum circuit architecture; The enhanced image output module is used to perform quantum measurement on the optimized quantum circuit and output the enhanced image.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the image enhancement method of the adaptive quantum circuit according to any one of claims 1 to 7 is implemented.

10. An electronic device comprising a memory and a processor, wherein the memory stores computer instructions that can be executed on the processor, wherein: When the processor runs the computer instructions, the image enhancement method of the adaptive quantum circuit described in any one of claims 1 to 7 is executed.

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