Image processing method for ion trap quantum computing, embedded system, electronic equipment and storage medium

Abstract

The invention provides an image processing method for ion trap quantum computing, which can be applied to the technical field of quantum computing. The method comprises the following steps: taking a pixel at a local peak position of a target ion as an initial generation point of a target pixel set, and calculating a first pixel difference value of the target ion in a quantum bright state and a quantum dark state; calculating a second pixel difference value of each pixel in the boundary tentative pixel set of the target ion in a quantum bright state and a quantum dark state; when the second pixel difference value is greater than the first pixel difference value, taking a pixel corresponding to the second pixel difference value as a criterion pixel of the target ion; and carrying out collection and operation on the criterion pixel and the pixel at the local peak position of the target ion to obtain an updated target pixel collection, updating the first pixel difference value based on the updated target pixel collection, and carrying out iteration to carry out the operation until a preset termination condition is met to obtain a final pixel collection. The invention further provides an embedded system, electronic equipment and a storage medium.

Description

Technical Field

[0001] This invention relates to the field of quantum computing technology, and more specifically to an image processing method, embedded system, electronic device, and storage medium for ion trap quantum computing. Background Technology

[0002] Ion trap quantum computing is a method for quantum computing that utilizes ion trap technology. Image readout feedback strategies have been a hot topic in the field of ion trap quantum computing in recent years. Generally speaking, the image readout feedback strategy of ion trap quantum computing involves the ion trap array isotropically emitting fluorescence of certain selected wavelengths under laser excitation. This fluorescence is then detected and read out by an optical sensor, and the input circuit control unit performs image processing to infer the state of the ions in the array. Based on the output ion state, the quantum computing system receives real-time feedback.

[0003] In ion trap quantum computing, depending on the wavelength of the excitation laser and the atomic characteristics, the Region-of-Interest (ROI) of each ion may exhibit two distributions: a bright distribution and a dark distribution. In the bright distribution, the optical sensor captures the scattered fluorescence of the ion, resulting in a bright spot with high pixel count in the ROI, which follows a two-dimensional decay distribution centered at a local maximum. In the dark distribution, no bright spot appears in the ROI, resulting in a white noise image. If the system's mechanical vibration is not too large, the receiving pixel coordinates of the scattered fluorescence of each ion remain approximately unchanged. Therefore, a reasonable image readout strategy is to find the set of pixels most "relevant" to each ion and determine the brightness state of the ion based on the pixel values ​​within that set. In this technical field, the algorithm for mapping the set of pixels on the readout image to each ion is defined as the pixel-ion matching algorithm.

[0004] Current classic pixel-ion matching algorithms, such as Gaussian Fit, often require a large number of exponential and logarithmic operations and solving complex linear algebraic equations, resulting in high hardware overhead and system latency. In addition, they output a circular region centered on the regression expectation, which often fails to accurately fit the geometry of the ion ROI. Summary of the Invention

[0005] In view of the above problems, the present invention provides an image processing method, embedded system, electronic device and storage medium for ion trap quantum computing, in order to solve at least one of the above problems.

[0006] According to a first aspect of the present invention, an image processing method for ion trap quantum computing is provided, the method comprising:

[0007] The pixel corresponding to the local peak position of the target ion in the quantum computing image of the ion trap is used as the initial generation point of the target pixel set, and the first pixel difference of the target ion in the quantum bright state and quantum dark state is calculated.

[0008] Generate a set of boundary trial pixels for the target ion in the quantum computing image of the ion trap, and calculate the second pixel difference for each pixel in the boundary trial pixel set in the quantum bright state and the quantum dark state;

[0009] Based on a preset attenuation coefficient, the first pixel difference is compared with the second pixel difference. When the comparison result is that the second pixel difference is greater than the first pixel difference, the pixel corresponding to the second pixel difference is used as the criterion pixel for the target ion.

[0010] The set of the criteria pixel and the corresponding pixels of the local peak position of the target ion in the quantum computing image of the ion trap are combined to obtain the updated target pixel set, and the first pixel difference is updated based on the updated target pixel set.

[0011] The process iteratively performs operations such as generating the boundary trial pixel set, calculating the second pixel difference, comparing pixel differences, performing a union operation on the pixel set, and updating the first pixel difference, until a preset termination condition is met, resulting in the final target pixel set.

[0012] According to an embodiment of the present invention, updating the first pixel difference based on the updated target pixel set includes:

[0013] The pixel difference is averaged based on the number of pixels in the updated target pixel set and the prime difference between the quantum bright and quantum dark states of each pixel in the updated target pixel set. The resulting average value is used as the updated first pixel difference.

[0014] According to an embodiment of the present invention, the above-mentioned preset termination condition includes:

[0015] Obtain the subset of boundary trial pixels in the boundary trial pixel set that has not undergone pixel difference comparison operation;

[0016] The number of pixels in the updated target pixel set is summed with the number of pixels in the boundary trial pixel subset, and the summation result is compared with a preset threshold.

[0017] If the comparison result is greater than the preset threshold, the boundary trial pixel subset and the updated target pixel set are processed, and the loop iteration is stopped.

[0018] According to an embodiment of the present invention, the above-mentioned processing of the boundary trial pixel subset and the updated target pixel set when the comparison result is greater than a preset threshold includes:

[0019] The pixels in the boundary trial pixel subset are sorted in descending order using the second pixel difference corresponding to each pixel in the boundary trial pixel subset;

[0020] Calculate the difference between the preset threshold and the number of pixels in the updated target pixel set, and based on the difference, select the top-ranked pixels from the sorted boundary trial pixel subset and incorporate them into the updated target pixel set.

[0021] According to an embodiment of the present invention, the above-mentioned preset termination condition includes: canceling the current iteration round and stopping the cyclic iteration when the number of iterations is greater than a preset iteration threshold.

[0022] According to an embodiment of the present invention, the above-mentioned preset termination condition includes:

[0023] If the set of boundary trial pixels corresponding to the target ion in the current iteration has an intersection with the set of boundary trial pixels corresponding to the target ion in other iterations, cancel the current iteration and stop the loop iteration.

[0024] According to a second aspect of the present invention, an embedded system is provided for an image processing method for ion trap quantum computing. The embedded system includes: a low-voltage differential signal data conversion module, a data packet allocation module based on a high-speed transmission protocol, an address-mapped interface image stream module, a monitoring path, and an image readout processing core.

[0025] The monitoring path includes an image format conversion unit, a channel overlay unit, a timing control unit, and a high-definition multimedia interface conversion unit.

[0026] The image readout processing includes a detection unit and a cache unit;

[0027] The embedded system is connected to a high frame rate enhanced camera to image the ion trap quantum computing process, and to identify and read out the imaging results of the ion trap quantum computing process to obtain the image processing results.

[0028] The embedded system is connected to a high-definition multimedia display, which displays the ion status to the user in real time.

[0029] According to an embodiment of the present invention, the above-mentioned embedded system is connected to a high frame rate enhanced camera, which is used to image the ion trap quantum computing process, and the imaging results of the ion trap quantum computing process are identified and read out to obtain image processing results including:

[0030] The low-voltage differential signal data conversion module is used to process the ion trap calculation image transmitted from the high frame rate enhanced camera to obtain the standard image timing signal;

[0031] The standard image timing signal is split by a data packet distribution module based on a high-speed transmission protocol to obtain the first branch timing signal and the second branch timing signal;

[0032] The first branch timing signal is transmitted to the monitoring path using the address-mapped interface image stream module.

[0033] The monitoring path is used to perform image format conversion, channel overlay and signal conversion on the timing signal of the first branch, and the processing results are displayed to the user in real time through a high-definition multimedia display.

[0034] The image readout processing core performs quantum state detection on the second branch timing signal transmitted from the data packet distribution module based on the high-speed transmission protocol to obtain image recognition and readout results.

[0035] A third aspect of the present invention provides an electronic device comprising: one or more processors; and a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the method described above.

[0036] A fourth aspect of the present invention also provides a computer-readable storage medium having a computer program or instructions stored thereon, wherein the computer program or instructions, when executed by a processor, implement the steps of the above-described method.

[0037] The image processing method for ion trap quantum computing provided by this invention offers an efficient and concise pixel-ion matching technique. For each ion, a matching pixel set corresponding to the bright and dark states of the ion in the ion trap quantum computing image is constructed. During ion state detection, only the pixels within this set need to be processed, thereby reducing external pixel interference and computational complexity, thus improving accuracy and reducing latency. Furthermore, the method provided by this invention only requires simple arithmetic operations and a search algorithm, without needing to perform high-hardware-complexity operations such as exponential / logarithmic calculations or solving linear algebraic equations. This makes it suitable for programming implementation on hardware platforms such as embedded systems, thereby improving system integration, reducing configuration latency, and providing practical convenience for quantum computing platform construction and system design. Attached Figure Description

[0038] The above-described features, other objects, and advantages of the present invention will become clearer from the following description of embodiments of the invention with reference to the accompanying drawings, in which:

[0039] Figure 1This is a flowchart of an image processing method for ion trap quantum computing according to an embodiment of the present invention;

[0040] Figure 2 This is a schematic diagram of the image pixel-ion dynamic matching process in ion trap quantum computing according to an embodiment of the present invention;

[0041] Figure 3 This is an architecture diagram of an embedded system for ion trap quantum computing image processing according to an embodiment of the present invention;

[0042] Figure 4 A block diagram of an electronic device suitable for implementing an ion trap quantum computing method according to an embodiment of the present invention is shown schematically. Detailed Implementation

[0043] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the invention. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of the invention for ease of explanation. However, it will be apparent that one or more embodiments may be practiced without these specific details. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concept of the invention.

[0044] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the invention. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0045] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0046] When using expressions such as "at least one of A, B and C", they should generally be interpreted in accordance with the meaning that is commonly understood by those skilled in the art (e.g., "a system having at least one of A, B and C" should include, but is not limited to, a system having A alone, a system having B alone, a system having C alone, a system having A and B, a system having A and C, a system having B and C, and / or a system having A, B and C, etc.).

[0047] In the process of ion trap quantum computing, in view of the technical problems of large hardware overhead and high system latency of existing pixel-ion matching methods, this invention provides an efficient and simple pixel-ion matching technology solution, which can complete the readout-feedback closed loop within the ion decoherence time. At the same time, the image processing method of ion trap quantum computing provided by this invention has the characteristics of high precision and low latency, and can determine the ion state as accurately as possible and quickly feed the results back to the quantum computing system.

[0048] Figure 1 This is a flowchart of an image processing method for ion trap quantum computing according to an embodiment of the present invention.

[0049] like Figure 1 As shown, the image processing method for ion trap quantum computing described above includes operations S110 to S150.

[0050] In operation S110, the pixel corresponding to the local peak position of the target ion in the quantum computing image of the ion trap is taken as the initial generation point of the target pixel set, and the first pixel difference of the target ion in the quantum bright state and quantum dark state is calculated.

[0051] In each iteration, an ion is randomly selected as the target ion, and the target pixel set of that target ion is calculated.

[0052] In operation S120, a set of boundary trial pixels of the target ion in the quantum computing image of the ion trap is generated, and the second pixel difference of each pixel in the boundary trial pixel set in the quantum bright state and the quantum dark state is calculated.

[0053] In operation S130, based on a preset attenuation coefficient, the first pixel difference is compared with the second pixel difference. When the comparison result is that the second pixel difference is greater than the first pixel difference, the pixel corresponding to the second pixel difference is used as the criterion pixel for the target ion.

[0054] The preset attenuation coefficient mentioned above is a hyperparameter that can be set based on user needs.

[0055] In each iteration, the second pixel difference corresponding to a pixel in the boundary trial pixel set is compared with the first pixel difference.

[0056] In operation S140, the criterion pixel and the pixel corresponding to the local peak position of the target ion in the ion trap quantum computing image are set and combined to obtain the updated target pixel set, and the first pixel difference is updated based on the updated target pixel set.

[0057] In operation S150, the boundary trial pixel set generation operation, the second pixel difference calculation operation, the pixel difference comparison operation, the pixel set union operation, and the first pixel difference update operation are performed iteratively until the preset termination condition is met, and the final target pixel set is obtained.

[0058] The image processing method for ion trap quantum computing provided by this invention offers an efficient and concise pixel-ion matching technique. For each ion, a matching pixel set corresponding to the bright and dark states of the ion in the ion trap quantum computing image is constructed. During ion state detection, only the pixels within this set need to be processed, thereby reducing external pixel interference and computational complexity, thus improving accuracy and reducing latency. Furthermore, the method provided by this invention only requires simple arithmetic operations and a search algorithm, without needing to perform high-hardware-complexity operations such as exponential / logarithmic calculations or solving linear algebraic equations. This makes it suitable for programming implementation on hardware platforms such as embedded systems, thereby improving system integration, reducing configuration latency, and providing practical convenience for quantum computing platform construction and system design.

[0059] According to an embodiment of the present invention, the above-mentioned updating the first pixel difference based on the updated target pixel set includes: performing a pixel difference averaging operation based on the number of pixels in the updated target pixel set and the prime difference between each pixel in the updated target pixel set in the quantum bright state and the quantum dark state, and using the obtained average value as the updated first pixel difference.

[0060] The following specific embodiments, in conjunction with the appendix, demonstrate this process. Figure 2 The image processing procedure for the ion trap quantum computing provided by the present invention will be described in further detail.

[0061] Figure 2 This is a schematic diagram of the image pixel-ion dynamic matching process in ion trap quantum computing according to an embodiment of the present invention.

[0062] The image processing method of ion trap quantum computing of the present invention is used to achieve efficient matching between pixels and ions. It is an image recognition and matching method. Using a heuristic generation algorithm based on the distribution of ion bright images, it starts from a local extremum point (which can be considered the "center" of the ion image) and, in each iteration, sequentially considers the "diffusion" pixels of its outer boundary for judgment. For details, see [link to details]. Figure 2 First, the pixels corresponding to the local peak positions of the target ion are used as the initial generation points. Through boundary generation-pixel determination (filtering)-boundary generation-pixel determination (filtering) and multiple rounds of iterative cycles, the target pixel set of the target ion is finally obtained.

[0063] The image processing method for ion trap quantum computing described above is further illustrated below through specific embodiments.

[0064] For each ion We expect to find the set of pixels. This allows for optimal resolution of ions using the pixel units within the set. The state.

[0065] In the initialization phase of the algorithm, ions are found. Corresponding local peak position As the initial point for generating the matching set, its position in the bright map (denoted as ) is calculated. ) and hidden map (noted as Pixel difference As shown in formula (1):

[0066] (1).

[0067] turn up The set of the first nearest-neighbor boundary trial pixels For each pixel The difference between the bright and dark pixels is also calculated, as shown in formula (2):

[0068] (2).

[0069] If the pixel readout difference satisfies formula (3):

[0070] (3),

[0071] It is then considered to be an ion. The criterion pixel, of which The attenuation coefficient is used as a hyperparameter in the calculation of the method provided in this invention. All criterion pixels are included. and Taking the union of the sets constructs the pixel set for the first iteration. As shown in formula (4):

[0072] (4).

[0073] At this point, the pixel difference criterion threshold is updated as shown in formula (5):

[0074] (5).

[0075] Then, a loop iteration is performed for... No. The pixel set generated in the next iteration , generate the first A set of boundary probing pixels For each pixel Calculate the pixel difference between the bright and dark images, as shown in formula (6):

[0076] (6),

[0077] If it satisfies formula (7):

[0078] (7),

[0079] It is then considered to be an ion. The criterion pixel will All pixels that meet the conditions and Take the union of sets and construct a set. And update the pixel difference criterion threshold, as shown in formula (8):

[0080] (8).

[0081] This process is repeated iteratively until a preset termination condition is met.

[0082] According to an embodiment of the present invention, the aforementioned preset termination condition includes: obtaining a subset of boundary trial pixels in the boundary trial pixel set that has not undergone pixel difference comparison operation; summing the number of pixels in the updated target pixel set with the number of pixels in the subset of boundary trial pixels, and comparing the summation result with a preset threshold; if the comparison result is that the summation result is greater than the preset threshold, processing the subset of boundary trial pixels and the updated target pixel set, and stopping the loop iteration.

[0083] According to an embodiment of the present invention, when the comparison result is greater than the preset threshold, the processing of the boundary trial pixel subset and the updated target pixel set includes: sorting each pixel in the boundary trial pixel subset in descending order using the second pixel difference corresponding to each pixel in the boundary trial pixel subset; calculating the difference between the preset threshold and the number of pixels in the updated target pixel set; and, based on the difference, selecting the top-ranked pixels from the sorted boundary trial pixel subset and incorporating them into the updated target pixel set.

[0084] The first preset termination condition is illustrated below through specific embodiments.

[0085] like and The sum of the number of elements in the set is greater than a preset threshold. At this time The elements are sorted from largest to smallest according to their difference in brightness, and the first few are placed in the top order. Each element is added to the final matching set, and the loop terminates.

[0086] According to an embodiment of the present invention, the above-mentioned preset termination condition includes: canceling the current iteration round and stopping the cyclic iteration when the number of iterations is greater than a preset iteration threshold.

[0087] The second preset termination condition is illustrated below through specific embodiments.

[0088] If the number of iteration rounds Greater than the preset threshold If the condition is met, then cancel the current iteration and stop the loop.

[0089] According to an embodiment of the present invention, the above-mentioned preset termination condition includes: if the set of boundary probe pixels corresponding to the target ion in the current iteration round has an intersection with the set of boundary probe pixels corresponding to the target ion in other iteration rounds, the current iteration round is canceled and the loop iteration is stopped.

[0090] The third preset termination condition is illustrated below through specific embodiments.

[0091] In parallel generation, if the first... In the next iteration, ions Boundary probing set With a certain ion The set of probing There is overlap, as shown in formula (9):

[0092] (9),

[0093] Then cancel their first The loop iterates through the rounds until it terminates.

[0094] Figure 3 This is an architecture diagram of an embedded system for quantum computing image processing in an ion trap, according to an embodiment of the present invention.

[0095] like Figure 3 As shown, the above-mentioned embedded system is applied to the image processing method of ion trap quantum computing. The embedded system includes: a low voltage differential signal data conversion module (e.g., LVDS Parallel Converting), a data packet allocation module based on a high-speed transmission protocol (e.g., CameraLink Data-allocation), an address-mapped interface image stream module (e.g., AXI4-Stream), a monitoring path, and an image readout processing core (DetectionCore).

[0096] The monitoring path includes an image format conversion unit (e.g., De-mosaic Bayer-RGB), a channel overlay unit, a timing control unit (e.g., Video Timing Controller), and a high-definition multimedia interface conversion unit (e.g., HDMI Transform).

[0097] The image readout processing includes a detection unit (e.g., SPDU) and a cache unit (e.g., SRAM 64KB).

[0098] The embedded system is connected to a high-frame-rate enhanced camera (e.g., ICMOS or QCMOS) to image the ion trap quantum computing process, and to identify and read out the imaging results of the ion trap quantum computing process to obtain the image processing results.

[0099] The embedded system is connected to the high-definition multimedia display and uses the high-definition multimedia display to display the ion status to the user in real time.

[0100] The aforementioned embedded system may optionally be a hardware platform such as an FPGA or an MCU.

[0101] According to an embodiment of the present invention, the above-mentioned embedded system is connected to a high frame rate enhanced camera, and uses the high frame rate enhanced camera to image the ion trap quantum computing process, and identifies and reads out the imaging results of the ion trap quantum computing process to obtain image processing results including: processing the ion trap computing image transmitted from the high frame rate enhanced camera using a low voltage differential signal data conversion module to obtain a standard image timing signal; splitting the standard image timing signal using a data packet allocation module based on a high-speed transmission protocol to obtain a first branch timing signal and a second branch timing signal; transmitting the first branch timing signal to a monitoring path using an address-mapped interface image stream module; performing image format conversion, channel superposition, and signal conversion on the first branch timing signal using the monitoring path, and displaying the processing results to the user in real time through a high-definition multimedia display; and performing quantum state detection on the second branch timing signal transmitted from the data packet allocation module based on a high-speed transmission protocol using an image readout processing core to obtain image recognition and readout results.

[0102] The embedded system applied to ion trap quantum computing image processing is further illustrated below through specific embodiments.

[0103] The aforementioned embedded system can be applied to hardware platforms such as FPGA / MCU to execute the image recognition and matching method based on the threshold comparison strategy of ion image readout provided in this invention. Figure 3As shown, this embedded system is a solution based on the TRC411 ICMOS image sensor interface and the XilinX Zynq7045 FPGA core. This embedded platform is used to read out a 300-pin image sensor. The platform, constructed of ions, receives ICMOS exposure images via a Cameralink 2.0 interface into the FPGA. Standard image timing signals are extracted and then split into two branches. One branch is monitored by an HDMI display stream input via an AXI4 bus, while the other branch performs quantum state detection via a readout core. The system clock is 50MHz, and the image size... With this configuration, ion state readout can be completed within 10ms, far shorter than the ion decoherence time. The system's readout core has a memory configuration of only 64kB, far below the resource constraints of most MCUs / FPGAs.

[0104] Figure 4 A block diagram of an electronic device suitable for implementing an ion trap quantum computing method according to an embodiment of the present invention is shown schematically.

[0105] like Figure 4 As shown, an electronic device 400 according to an embodiment of the present invention includes a processor 401, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 402 or a program loaded from a storage portion 408 into a random access memory (RAM) 403. The processor 401 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 401 may also include onboard memory for caching purposes. The processor 401 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present invention.

[0106] RAM 403 stores various programs and data required for the operation of electronic device 400. Processor 401, ROM 402, and RAM 403 are interconnected via bus 404. Processor 401 executes various operations of the method flow according to embodiments of the present invention by executing programs in ROM 402 and / or RAM 403. It should be noted that the programs may also be stored in one or more memories other than ROM 402 and RAM 403. Processor 401 may also execute various operations of the method flow according to embodiments of the present invention by executing programs stored in said one or more memories.

[0107] According to an embodiment of the present invention, the electronic device 400 may further include an input / output (I / O) interface 405, which is also connected to a bus 404. The electronic device 400 may also include one or more of the following components connected to the input / output (I / O) interface 405: an input section 406 including a keyboard, mouse, etc.; an output section 407 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN card, modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the input / output (I / O) interface 405 as needed. A removable medium 411, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 410 as needed so that computer programs read from it can be installed into the storage section 408 as needed.

[0108] The present invention also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs, which, when executed, implement the method according to the embodiments of the present invention.

[0109] According to embodiments of the present invention, a computer-readable storage medium may be a non-volatile computer-readable storage medium, such as including, but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to embodiments of the present invention, a computer-readable storage medium may include ROM 402 and / or RAM 403 and / or one or more memories other than ROM 402 and RAM 403 described above.

[0110] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0111] Those skilled in the art will understand that the features described in the various embodiments of the present invention can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in the present invention. In particular, the features described in the various embodiments of the present invention can be combined and / or combined in various ways without departing from the spirit and teachings of the present invention. All such combinations and / or combinations fall within the scope of the present invention.

[0112] The embodiments of the present invention have been described above. However, these embodiments are merely illustrative and not intended to limit the scope of the invention. Although various embodiments have been described above, this does not mean that the measures in the various embodiments cannot be used advantageously in combination. Various substitutions and modifications can be made by those skilled in the art without departing from the scope of the invention, and all such substitutions and modifications should fall within the scope of the invention.

Claims

1. An image processing method for ion trap quantum computing, characterized in that, The method includes: The pixel corresponding to the local peak position of the target ion in the quantum computing image of the ion trap is used as the initial generation point of the target pixel set, and the first pixel difference of the target ion in the quantum bright state and the quantum dark state is calculated. Generate a set of boundary trial pixels for the target ion in the quantum computing image of the ion trap, and calculate the second pixel difference for each pixel in the boundary trial pixel set between the quantum bright state and the quantum dark state; Based on a preset attenuation coefficient, the first pixel difference is compared with the second pixel difference. When the comparison result is that the second pixel difference is greater than the first pixel difference, the pixel corresponding to the second pixel difference is used as the criterion pixel of the target ion. The criteria pixel and the pixel corresponding to the local peak position of the target ion in the ion trap quantum computing image are combined and processed to obtain an updated target pixel set, and the first pixel difference is updated based on the updated target pixel set. The process iteratively performs operations such as generating the boundary trial pixel set, calculating the second pixel difference, comparing pixel differences, performing a union operation on the pixel set, and updating the first pixel difference, until a preset termination condition is met, resulting in the final target pixel set.

2. The method according to claim 1, characterized in that, Updating the first pixel difference based on the updated target pixel set includes: A pixel difference averaging operation is performed based on the number of pixels in the updated target pixel set and the quantum difference between the quantum bright and quantum dark states of each pixel in the updated target pixel set, and the resulting average value is used as the updated first pixel difference.

3. The method according to claim 1, characterized in that, The preset termination conditions include: Obtain the subset of boundary trial pixels in the boundary trial pixel set that has not undergone pixel difference comparison operation; The number of pixels in the updated target pixel set is summed with the number of pixels in the boundary trial pixel subset, and the summation result is compared with a preset threshold. If the comparison result is greater than the preset threshold, the boundary trial pixel subset and the updated target pixel set are processed, and the loop iteration is stopped.

4. The method according to claim 3, characterized in that, If the comparison result is greater than the preset threshold, the processing of the boundary trial pixel subset and the updated target pixel set includes: The pixels in the boundary trial pixel subset are sorted in descending order using the second pixel difference corresponding to each pixel in the boundary trial pixel subset; Calculate the difference between the preset threshold and the number of pixels in the updated target pixel set, and based on the difference, select the top-ranked pixels from the sorted boundary trial pixel subset and incorporate them into the updated target pixel set.

5. The method according to claim 1, characterized in that, The preset termination conditions include: canceling the current iteration round and stopping the loop iteration when the number of iterations exceeds a preset iteration threshold.

6. The method according to claim 1, characterized in that, The preset termination conditions include: If the set of boundary trial pixels corresponding to the target ion in the current iteration has an intersection with the set of boundary trial pixels corresponding to the target ion in other iterations, cancel the current iteration and stop the loop iteration.

7. An embedded system, applied to the method according to any one of claims 1-6, characterized in that, The embedded system includes: a low-voltage differential signal data conversion module, a data packet distribution module based on a high-speed transmission protocol, an address-mapped interface image stream module, a monitoring path, and an image readout processing core; The monitoring path includes an image format conversion unit, a channel overlay unit, a timing control unit, and a high-definition multimedia interface conversion unit. The image readout processing includes a detection unit and a cache unit; The embedded system is connected to a high frame rate enhanced camera, which is used to image the ion trap quantum computing process, and the imaging results of the ion trap quantum computing process are identified and read out to obtain image processing results. The embedded system is connected to the high-definition multimedia display and uses the high-definition multimedia display to display the ion status to the user in real time.

8. The system according to claim 7, characterized in that, The embedded system is connected to a high-frame-rate enhanced camera, which is used to image the ion trap quantum computing process. The imaging results of the ion trap quantum computing process are then identified and read out to obtain image processing results, including: The low-voltage differential signal data conversion module is used to process the ion trap calculation image transmitted from the high frame rate enhanced camera to obtain a standard image timing signal; The standard image timing signal is split using the data packet distribution module based on the high-speed transmission protocol to obtain a first branch timing signal and a second branch timing signal; The first branch timing signal is transmitted to the monitoring path using the address-mapped interface image stream module. The monitoring path is used to perform image format transformation, channel superposition, and signal transformation on the first branch timing signal, and the processing results are displayed to the user in real time through the high-definition multimedia display. The image readout processing core is used to perform quantum state detection on the second branch timing signal transmitted by the data packet distribution module based on the high-speed transmission protocol to obtain image recognition and readout results.

9. An electronic device, comprising: One or more processors; Memory, used to store one or more computer programs. The characteristic feature is that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 6.

10. A computer-readable storage medium having a computer program or instructions stored thereon, characterized in that, When the computer program or instructions are executed by a processor, they implement the steps of the method according to any one of claims 1 to 6.

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