Quantum computing task optimization processing method and device and quantum computer

Abstract

The invention discloses a quantum computing task optimization processing method and device and a quantum computer.The processing method comprises the steps that a server side calls a first data packet to conduct task analysis, and the first data packet is analyzed into a plurality of different subtasks; obtaining a target sub-task, and performing waveform calculation and compiling on the target sub-task to obtain a compiling result; the server side issues the compilation result to a subsequent module for processing, updates the next subtask to the target subtask, and returns to perform waveform calculation and compilation on the target subtask until all subtasks are processed by the subsequent module; the first data packet is formed by performing waveform description on waveform data, and the waveform data corresponds to a quantum computing task to be executed. The method has the advantage that the overall processing speed of the quantum computing task can be greatly increased.

Description

Technical Field

[0001] The present invention relates to the field of quantum computer technology, and in particular to a method and device for optimizing quantum computing tasks and a quantum computer. Background Art

[0002] Quantum computers use quantum bits as basic units and use quantum superposition, quantum entanglement and other characteristics to achieve disruptive data computing and processing methods. They can provide a huge amount of information carrying capacity and super-strong parallel computing and processing capabilities that potentially surpass all classical computing technologies. The operation of quantum computers requires the cooperation and collaboration of quantum chips, quantum computing measurement and control systems, and quantum computing operating systems. The quantum computing measurement and control system includes quantum measurement and control application software and quantum control devices used to provide corresponding control signals and read measurement operations.

[0003] The existing quantum measurement and control application software includes a client and a server that processes the client's waveform data. The client performs waveform calculation and waveform data upload for the quantum computing task to be executed. During this process, the server is in a waiting state. The server processes the data only after receiving the waveform data uploaded by the client. The processed data is sent to the quantum control device. The results fed back by the quantum control device are processed and then fed back to the user. Therefore, the client needs to wait for a long time from issuing the task to obtaining the corresponding result. A long time is spent by the client performing waveform calculation and waveform upload. During this period of time, the server and the quantum control device are in a waiting state, which greatly reduces the execution efficiency of the quantum computing task.

[0004] It should be noted that the information disclosed in the background technology section of this application is only intended to deepen the understanding of the general background technology of this application, and should not be regarded as an admission or suggestion in any form that the information constitutes prior art already known to those skilled in the art. Summary of the invention

[0005] In order to solve the problem of how to improve the execution efficiency of quantum computing tasks, the present invention provides the following technical solutions:

[0006] The present application discloses a method for optimizing a quantum computing task, comprising:

[0007] The server side retrieves the first data packet to perform task analysis, and analyzes it into a plurality of different subtasks; obtains the target subtask, performs waveform calculation and compilation on the target subtask, and obtains a compilation result;

[0008] The server sends the compilation result to the subsequent module for processing, updates the next subtask to the target subtask, and returns to perform waveform calculation and compilation on the target subtask until all subtasks complete waveform calculation and compilation and are processed by the subsequent module;

[0009] The first data packet is formed by performing waveform description on waveform data, and the waveform data corresponds to a quantum computing task to be executed.

[0010] Optionally, the first data packet is formed by performing waveform description on the waveform data, including:

[0011] The first data packet is formed by the client performing waveform description on the waveform data.

[0012] Optionally, waveform calculation is performed on the target subtask, including obtaining a sub-data packet corresponding to the target subtask from the first data packet, and performing waveform calculation on the sub-data packet to obtain basic waveform data corresponding to the target subtask according to a set protocol and a calling pulse module.

[0013] Optionally, waveform calculation is performed on the target subtask, including: obtaining a sub-data packet corresponding to the target subtask from the first data packet, managing the sub-data packet to divide it into multiple data packet units, respectively performing waveform calculation on the corresponding data packet units according to the set protocol and calling the pulse module, and then aggregating the data to obtain basic waveform data corresponding to the target subtask.

[0014] Optionally, performing waveform calculation on the target subtask further includes: performing crosstalk compensation, delay compensation, and distortion compensation on the basic waveform data to obtain waveform data.

[0015] Optionally, the server retrieves the first data packet to perform task analysis, specifically including:

[0016] The server retrieves the first data packet from the first database to perform task analysis.

[0017] Optionally, after parsing into a plurality of different subtasks, the method further includes: storing the plurality of different subtasks in a task stack, and the task stack releasing the target subtask.

[0018] Optionally, the processing by the subsequent module includes: a data processing unit in the subsequent module calls the compilation result, and then sends it to the quantum control device through a router unit in the subsequent module, and the quantum control device sends the processed data to the data processing unit.

[0019] The present application discloses a quantum computing task optimization processing device, comprising:

[0020] A task calling module, used to call a first data packet; the first data packet is formed by performing waveform description on waveform data, and the waveform data corresponds to the quantum computing task to be executed;

[0021] A task analysis module performs task analysis on the first data packet into a plurality of different subtasks;

[0022] A waveform calculation and compilation module obtains a target subtask, performs waveform calculation and compilation on the target subtask, and obtains a compilation result;

[0023] The subsequent module is used to send the compilation result to the subsequent module for processing.

[0024] The present application also discloses a quantum computer, including an optimization processing method for executing the above-mentioned quantum computing task, or an optimization processing device for executing the above-mentioned quantum computing task.

[0025] The beneficial effects of the present invention are:

[0026] The speed of the first data packet formed by describing the waveform data retrieved by the server in the quantum computing task optimization processing method of the present invention is greatly improved compared with the speed of retrieving the waveform data. In addition, the server parses the acquired task, parses it into multiple subtasks and stores it in different task stacks. The subtasks released in the task stack are first subjected to waveform calculation, and the waveform data corresponding to the subtask is obtained through the waveform description corresponding to the subtask, and then compiled. After the compilation is completed, it is sent to the subsequent module for processing. When the subsequent module processes the compiled waveform data, the new subtask is released in the task stack for waveform calculation and compilation. Compared with the prior art in which the server waits for the client to process all the waveform data and send it before starting to process, the pipelined work greatly improves the data processing speed of the quantum computing measurement and control system, thereby improving the execution efficiency of the quantum computing task. In addition, waveform calculation is only performed on the subtasks popped out of the task stack, and waveform calculation is not required for the subtasks that are not popped out. Therefore, compared with the client directly calculating and transmitting all the waveforms, it also greatly saves time, thereby submitting execution efficiency.

[0027] The tasks issued by the task stack in the present invention are divided into parallel multi-path calculations after calculation, and then aggregated and compiled. The multi-path waveform calculation can improve the efficiency of waveform data processing, thereby improving the execution efficiency of quantum computing tasks.

[0028] The quantum computing task optimization processing device and quantum computer provided by the present invention include the above-mentioned processing method, and therefore have the same beneficial effects, which will not be described in detail here. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a framework flow chart of the quantum computing measurement and control system in the prior art;

[0030] Figure 2 A framework flow chart of a quantum computing measurement and control system provided by an embodiment of the present invention;

[0031] Figure 3 The first execution flow chart for compiling tasks on the server side;

[0032] Figure 4 The second execution flow chart for compiling tasks on the server side;

[0033] Figure 5 The first structural diagram for compiling tasks on the server side;

[0034] Figure 6 The second structure diagram for compiling tasks on the server side. DETAILED DESCRIPTION

[0035] In order to enable those skilled in the art to better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this application. The embodiments described below with reference to the drawings are exemplary and are only used to explain the present application, and cannot be interpreted as limitations on the present application.

[0036] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0037] like Figure 1As shown, the process of realizing waveform data processing in the quantum computing measurement and control system in the prior art is as follows: the quantum computing measurement and control system includes a client and a server. The quantum measurement and control application software of the client sends the quantum computing task to be executed, completes the calculation and conversion of the large waveform data corresponding to the quantum computing task to be executed, and uploads the converted large waveform data to the first database. The first database is a mongo database in the existing embodiment. The server retrieves the processed waveform data from the first database, that is, obtains the task corresponding to the waveform data, performs task parsing, parses it into multiple subtasks and stores it in different task stacks. The task stack releases the subtasks in turn, and the released subtasks are used as target subtasks. The compiler compiles the target subtasks. After the target subtasks are compiled, they are sent to the subsequent module for processing. At the same time, the task stack releases the next subtask as the target subtask. The compiler compiles the new target subtask until all subtasks are processed in the subsequent module, and the compilation results are stored in the second database. The subsequent module in the server includes a router unit and a data processing unit. The data processing unit calls the compilation result in the second database and sends it to the quantum control device through the router unit. The quantum control device sends the processed data to the data processing unit.

[0038] In the existing solution, when the client performs waveform calculation and uploads waveform data, the server is in a waiting state, so the entire system has serious time-consuming problems. When facing characterization experiments similar to random benchmarks, there is also a risk of crashing due to excessive memory usage.

[0039] Based on the above problems, Figure 2 As shown, the present application discloses a method for optimizing quantum computing tasks, and the steps for implementing waveform data processing are as follows:

[0040] The server side retrieves the first data packet to perform task analysis, and analyzes it into multiple different subtasks; obtains the target subtask, performs waveform calculation and compilation on the target subtask, and obtains a compilation result; the server side sends the compilation result to the subsequent module for processing, and updates the next subtask to the target subtask, and returns to perform waveform calculation and compilation on the target subtask until all subtasks complete waveform calculation and compilation and are processed by the subsequent module; the first data packet is formed by waveform description of waveform data, and the waveform data corresponds to the quantum computing task to be executed.

[0041] The first data packet is formed by waveform description of waveform data. In this solution, the first data packet is formed by waveform description of waveform data by the client. The client does not need to calculate the waveform, but only needs to describe the waveform, which greatly reduces the waiting time for the server to obtain the data sent by the client. The amount of data of the first data packet formed by the waveform description is several orders of magnitude less than the amount of data calculated in the client compared to the second data packet formed by directly calculating the waveform. Therefore, the amount of data transmitted by the client is greatly reduced, thereby reducing the transmission time for the server to obtain the first data packet. In addition, the reduced amount of data transmitted by the client also avoids the risk of crash caused by excessive memory usage of the client.

[0042] Optionally, after the first data packet is formed by waveform description of the waveform data by the client, the first data packet is uploaded to a first database, and the server retrieves the first data packet from the first database.

[0043] Specifically, the first data packet includes: a description file describing the waveform data, a preset waveform script text, and configuration information under the corresponding environment. The first data packet is obtained by serializing the waveform. In detail, the first data packet is obtained by the quantum measurement and control application software of the user-oriented client and then processed. The client directly describes the waveform data corresponding to the quantum computing task to be executed to form the first data packet and then outputs it. The client only needs to describe the waveform and does not need to perform the waveform calculation process. In addition, compared with the transmission of waveform data, the client's data processing speed is accelerated as a whole.

[0044] Optionally, the description file describing the waveform data is a file in any format, such as a file in json format, a file in YAML format, etc. In an embodiment of the present application, the description file is a file in Json format, which is used to generate a waveform description on the client side and parse and calculate the waveform description on the server side.

[0045] Specifically, the server retrieves the first data packet for task parsing, and parses it into multiple different subtasks; obtains the target subtask, performs waveform calculation and compilation on the target subtask, and obtains the compilation result; the server sends the compilation result to the subsequent module for processing, and updates the next subtask to the target subtask, returns to perform waveform calculation and compilation on the target subtask, until all subtasks complete waveform calculation and compilation and are processed by the subsequent module. This scheme utilizes the asynchronous execution characteristics of the server. When the waveform calculation and compilation of the current subtask are performed, the compiled compilation result that has been compiled is processed in the subsequent module. Compared with the prior art in which the server waits for the client to process all waveform data and send it before starting to process, the data processing speed of the quantum computing measurement and control system is greatly improved, thereby improving the execution efficiency of the quantum computing task. In addition, the server only performs waveform calculation and compilation on the target subtask. When the subtask is not the target subtask, waveform calculation does not need to be performed. Therefore, compared with the client directly calculating and transmitting all waveforms, it also greatly saves the processing time of the entire device, thereby improving the execution efficiency of the entire device.

[0046] In one embodiment, after parsing into a plurality of different subtasks, the method includes: storing the plurality of different subtasks in a task stack, releasing the target subtask from the task stack, and the task stack is used to store the plurality of subtasks and pop the target subtask.

[0047] The waveform calculation for the target subtask includes: obtaining a sub-data packet corresponding to the target subtask from the first data packet, and performing waveform calculation on the sub-data packet to obtain basic waveform data corresponding to the target subtask according to the setting protocol and calling the pulse module. The setting protocol here refers to a file on waveform description agreed upon by both the client and the server, which is generated by the client and mainly describes the parameters required to generate the actual waveform and the timing of the waveform, and is parsed by the server to finally generate the actual waveform. In this embodiment, the quantum computing task is parsed and used as a sub-data packet of the target subtask. Since there is no relevant module for waveform calculation in the server, this application is based on the setting protocol, according to the waveform description in the sub-data packet corresponding to the target subtask and the functions and classes in the pulse module, the target subtask generates the basic waveform data corresponding to the sub-data packet. In addition, the same setting protocol and the functions and classes in the pulse module are required to describe the waveform to form the first data packet, so as to ensure that the sub-data packet in the data packet formed by the waveform description can generate the waveform data corresponding to the target subtask in the quantum computing task. In one embodiment: the same pulse module in the client is installed in the server, and the waveform description in the sub-data packet corresponding to the target subtask is parsed and calculated by calling the pulse module on the server to obtain the basic waveform data. In another embodiment, if Figure 2 As shown, the pulse module is an independent module that belongs neither to the server nor to the client. The client and server can call its functions to obtain results.

[0048] Preferably, the waveform calculation for the target subtask further includes: performing crosstalk compensation, delay compensation, and distortion compensation on the basic waveform data to obtain waveform data. The basic waveform data is compensated in sequence according to crosstalk compensation, delay compensation, and distortion compensation to obtain the waveform data. In this application, crosstalk compensation, delay compensation, and distortion compensation belong to the prior art, so they are not described here in detail.

[0049] In this embodiment, the workflow on the server side is as follows:

[0050] like Figure 3 As shown, the server-side workflow of the above solution is as follows:

[0051] S21, the server side retrieves the first data packet to perform task analysis, analyzes it into a plurality of different subtasks, and stores the plurality of different subtasks into a task stack;

[0052] S22, obtaining a target subtask from the task stack;

[0053] S23, performing waveform calculation and compilation on the target subtask to obtain a compilation result;

[0054] S24, the server sends the compilation result to the subsequent module for processing, updates the next subtask to the target subtask, and returns to perform waveform calculation and compilation on the target subtask until all subtasks are processed in the subsequent module.

[0055] In another embodiment, waveform calculation is performed on the target subtask, including: obtaining a sub-data packet corresponding to the target subtask from the first data packet, managing the sub-data packet to separate into a plurality of data packet units, and respectively performing waveform calculation on the corresponding data packet units according to the set protocol and calling the functions and classes of the pulse module, and then aggregating the data to obtain the basic waveform data corresponding to the target subtask. In the pipeline processing process of waveform calculation, compilation, and subsequent module processing, the waveform calculation still takes a long time. The compilation result corresponding to the previous subtask has been processed in the subsequent module, and it is necessary to wait for the target subtask being waveform calculated to complete the compilation. In order to further improve the execution efficiency of the device, this solution manages the sub-data packet to separate into a plurality of data packet units, and then aggregates them after multi-channel parallel processing, and finally obtains the basic waveform data corresponding to the target subtask. This solution improves the processing speed of the target subtask in the waveform calculation process, and can reduce the waiting time of the subsequent module or eliminate the need for waiting, thereby improving the efficiency of quantum computing tasks and optimizing the processing of quantum computing tasks.

[0056] The waveform calculation for the target subtask also includes: performing crosstalk compensation, delay compensation, and distortion compensation on the basic waveform data to obtain waveform data. The basic waveform data is compensated in sequence according to crosstalk compensation, delay compensation, and distortion compensation to obtain the waveform data. In this application, crosstalk compensation, delay compensation, and distortion compensation belong to the prior art, so they are not described here.

[0057] In this embodiment, if Figure 4 As shown, the workflow on the server side is as follows:

[0058] S21, the server side retrieves the first data packet to perform task analysis, analyzes it into a plurality of different subtasks, and stores the plurality of different subtasks into a task stack;

[0059] S22, obtaining a target subtask from the task stack;

[0060] S231, the calculation management unit in the server side divides the obtained target subtask into multiple task units, and performs parallel calculation through multiple waveform calculation units;

[0061] S232, summarizing the parallel computing results and compiling them;

[0062] S24, the server sends the compilation result to the subsequent module for processing, updates the next subtask to the target subtask, and returns to perform waveform calculation and compilation on the target subtask until all subtasks are processed in the subsequent module.

[0063] The server sends the compilation result to the subsequent module for processing, including: the data processing unit in the subsequent module calls the compilation result in the second database, and then sends it to the quantum control device through the router unit in the subsequent module, and the quantum control device sends the processed data to the data processing unit.

[0064] In this embodiment, the server introduces parallel waveform calculation to optimize the performance bottleneck caused by waveform calculation. In steps S231 and S232, through multi-channel parallel waveform calculation, after the slice task is parsed and completed in step S21, the calculation management unit is started, and a certain number of waveform calculation processes are created and controlled by the calculation management unit. The number of creations is determined by the number of slices of the task, and then the task slices are sent to each waveform calculation process, and then the parallel calculation results are compiled and uploaded to the second database. This embodiment maximizes the execution efficiency of the device.

[0065] The present application also provides a quantum computing task optimization processing device, including a server, the server retrieves a first data packet for task analysis, and analyzes it into multiple different subtasks; obtains a target subtask, performs waveform calculation and compilation on the target subtask, and obtains a compilation result; sends the compilation result to a subsequent module for processing, and updates the next subtask to the target subtask, and returns to perform waveform calculation and compilation on the target subtask until all subtasks are processed by the subsequent module. The first data packet is formed by waveform description of waveform data, and the waveform data corresponds to the quantum computing task to be executed.

[0066] Specifically, it also includes a client, which performs waveform description on the waveform data to form a first data packet.

[0067] Specifically, the device further includes a first database and a second database, the first data packet is uploaded to the first database, and the second database is used to store the compilation result.

[0068] Specifically, in one embodiment disclosed in the present application, Figure 5 As shown, the server side includes a task acquisition module 201, a task parsing module 202, a task stack 203, a waveform calculation module 204, a compilation module 205, and a second database 206 which are arranged in sequence.

[0069] The task acquisition module 201 retrieves a first data packet from a first database.

[0070] The task parsing module 202 performs task parsing on the first data packet into a plurality of different subtasks.

[0071] The task stack 203 is used to store a plurality of different subtasks and receive instructions to release a target subtask.

[0072] The waveform calculation module 204 obtains a sub-data packet corresponding to the target sub-task from the first data packet, and performs waveform calculation on the sub-data packet to obtain basic waveform data corresponding to the target sub-task according to the set protocol and the functions and classes of the called pulse module; and performs crosstalk compensation, delay compensation, and distortion compensation on the basic waveform data to obtain the waveform data. Specifically, the pulse module is also linked to the waveform description module of the client, and the waveform description module ensures that the sub-data packet in the data packet formed by the waveform description by the client can be restored into the waveform data corresponding to the target sub-task in the quantum computing task according to the set protocol and the functions and classes of the called pulse module.

[0073] The compiling module 205 compiles the waveform data.

[0074] The second database 206 receives the compilation result output by the compilation module 205 .

[0075] The subsequent module includes a router unit and a data processing unit. The data processing unit calls the compilation result in the second database and then sends it to the quantum control device through the router unit. The quantum control device sends the processed data to the data processing unit. The subsequent module and the second database are both located on the server side.

[0076] In another embodiment, if Figure 6 As shown, Figure 5 The difference between the embodiment in the embodiment is that the waveform calculation module includes a calculation management unit 2041, a plurality of parallel waveform calculation units 2042, and a data summary unit 2043. The calculation management unit 2041 divides the obtained target subtask into multiple task units. The plurality of parallel waveform calculation units 2042 receive a plurality of task units in correspondence and perform waveform calculations respectively. The data summary unit 2043 receives the waveform calculation results of each waveform calculation unit 2042 in correspondence with the initial order assigned to the waveform calculation unit 2042, and then sends the calculation results to the compilation module 205 for compilation.

[0077] In this embodiment, the server side also includes a calculation management unit 2041, multiple parallel waveform calculation units 2042, and a data summary unit 2043, which are used to implement parallel waveform calculation and optimize the performance bottleneck caused by waveform calculation. This solution starts the calculation management unit, creates a certain number of waveform calculation processes and hands them over to the calculation management unit 2041 for control. The number of creations is determined by the number of task slices, and then sends the task slices to each of the multiple waveform calculation units 2042. Subsequently, the task summary of the completed waveform calculation is received through the data summary unit 2043, and the completed waveform data is compiled and uploaded to the second database. This embodiment maximizes the execution efficiency of the device.

[0078] The present application also provides a quantum computer, which includes the optimization processing device for quantum computing tasks of the above-mentioned embodiments of the present application, or uses the optimization processing method for quantum computing tasks introduced in any of the above-mentioned embodiments of the present application to perform quantum computing tasks. The rest of the content can refer to the prior art and will not be described in detail here.

[0079] The above is a detailed introduction to an optimization processing method for a quantum computing task, an optimization processing device for a quantum computing task, and a quantum computer provided by the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. It should be pointed out that for ordinary technicians in this technical field, without departing from the principles of the present application, several improvements and modifications can be made to the present application, and these improvements and modifications also fall within the scope of protection of the claims of the present application.

Claims

1. A method for optimizing quantum computing tasks, characterized in that: include: The server side calls the first data packet to perform task analysis, and analyzes it into multiple different subtasks; Obtaining a target subtask, performing waveform calculation and compilation on the target subtask, and obtaining a compilation result; The server sends the compilation result to the subsequent module for processing, updates the next subtask to the target subtask, and returns to perform waveform calculation and compilation on the target subtask until all subtasks complete waveform calculation and compilation and are processed by the subsequent module; The first data packet is formed by performing waveform description on waveform data, and the waveform data corresponds to a quantum computing task to be executed.

2. The method for optimizing quantum computing tasks according to claim 1, wherein: The first data packet is formed by performing waveform description on the waveform data, including: The first data packet is formed by the client performing waveform description on the waveform data.

3. The method for optimizing quantum computing tasks according to claim 1, wherein: Performing waveform calculation on the target subtask includes obtaining a sub-data packet corresponding to the target subtask from the first data packet, and performing waveform calculation on the sub-data packet to obtain basic waveform data corresponding to the target subtask according to a set protocol and a calling pulse module.

4. The method for optimizing quantum computing tasks according to claim 1, wherein: Performing waveform calculation on the target subtask includes: obtaining a sub-data packet corresponding to the target subtask from a first data packet, managing the sub-data packet to divide it into a plurality of data packet units, respectively performing waveform calculation on the corresponding data packet units according to a set protocol and calling a pulse module, and then aggregating the data to obtain basic waveform data corresponding to the target subtask.

5. The method for optimizing quantum computing tasks according to claim 3 or 4, characterized in that: The waveform calculation is performed on the target subtask, and further includes: performing crosstalk compensation, delay compensation, and distortion compensation on the basic waveform data to obtain waveform data.

6. The method for optimizing quantum computing tasks according to claim 1, wherein: The server side retrieves the first data packet to perform task analysis, specifically including: The server retrieves the first data packet from the first database to perform task analysis.

7. The method for optimizing quantum computing tasks according to claim 1, wherein: After being parsed into a plurality of different subtasks, the method includes: storing the plurality of different subtasks into a task stack, and releasing the target subtask from the task stack.

8. The method for optimizing quantum computing tasks according to claim 1, wherein: The processing by the subsequent module includes: the data processing unit in the subsequent module calls the compilation result, and then sends it to the quantum control device through the router unit in the subsequent module, and the quantum control device sends the processed data to the data processing unit.

9. A quantum computing task optimization processing device, characterized in that: include: A task calling module, used for calling the first data packet; The first data packet is formed by performing waveform description on waveform data, and the waveform data corresponds to a quantum computing task to be performed; A task analysis module performs task analysis on the first data packet into a plurality of different subtasks; A waveform calculation and compilation module obtains a target subtask, performs waveform calculation and compilation on the target subtask, and obtains a compilation result; The subsequent module is used to send the compilation result to the subsequent module for processing.

10. A quantum computer, characterized in that: It includes a method for optimizing the quantum computing task according to any one of claims 1 to 8, or an optimization processing device for the quantum computing task according to claim 9.

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