Quantum bit calibration method and device, quantum computer, storage medium and product

Abstract

The invention discloses a quantum bit calibration method and device, a quantum computer, a storage medium and a product, and relates to the technical field of quantum computing, and the method comprises the steps: obtaining a parameter constraint relation of each quantum bit calibration experiment, and enabling the parameter constraint relation to represent the parameter dependence of the quantum bit calibration experiment; repeated output parameters are determined from the experiment output parameters of the parameter constraint relations, the parameter levels of the repeated output parameters in the overlapping constraint relations are determined, and the overlapping constraint relations are the parameter constraint relations where the repeated output parameters are located; the parameter hierarchy represents a progressive relationship level formed by repeatedly outputting the repeated output parameter in the calibration experiment process; and constructing a directed acyclic graph taking each quantum bit calibration experiment as a graph node based on each parameter constraint relationship and the parameter hierarchy, and executing each quantum bit calibration experiment based on the directed acyclic graph. According to the method and the device, the stability and the consistency of the directed acyclic graph are improved, so that the accuracy of quantum bit calibration based on the directed acyclic graph is improved.

Description

Technical Field

[0001] The present application relates to the field of quantum computing technology, and in particular to quantum bit calibration methods, devices, quantum computers, storage media and products. Background Art

[0002] In the field of superconducting quantum computing, achieving efficient computing power depends on the precise manipulation and reading of superconducting quantum bits (qubits). The manipulation and reading information of superconducting quantum bits are usually loaded on the phase and amplitude of microwave signals. Therefore, in order to achieve specific computing goals, the parameters of these signals must be accurately calibrated.

[0003] The existing automated calibration scheme is mainly based on directed acyclic graph (DAG) for calibration. This method represents each calibration experiment with a node, and the dependency between calibration experiments is represented by a directed acyclic graph, thereby realizing the forward calibration and reverse inspection functions. However, the existing DAG construction mainly relies on the personal experience of the experimenter to define the order between nodes, resulting in the construction of the DAG graph being completely dependent on the experimenter's personal understanding and judgment, affecting the stability and consistency of the directed acyclic graph, and reducing the accuracy of the automated calibration process.

[0004] The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art. Summary of the invention

[0005] The main purpose of this application is to provide a quantum bit calibration method, device, quantum computer, storage medium and product, aiming to solve the technical problem of poor stability and consistency of directed acyclic graphs, resulting in low accuracy of automatic quantum bit calibration.

[0006] To achieve the above objectives, the present application proposes a quantum bit calibration method, the method comprising:

[0007] Obtaining a parameter constraint relationship for each quantum bit calibration experiment, wherein the parameter constraint relationship is used to characterize the parameter dependence of the quantum bit calibration experiment;

[0008] Determine a repeated output parameter from the experimental output parameters of each of the parameter constraint relationships, and determine a parameter level of the repeated output parameter in each overlapping constraint relationship, wherein the overlapping constraint relationship is a parameter constraint relationship in which the repeated output parameter is located, and the parameter level is used to characterize the progressive relationship level formed by the repeated output parameter being output multiple times in the calibration experiment process;

[0009] Based on each of the parameter constraint relationships and the parameter hierarchy, a directed acyclic graph is constructed with each of the quantum bit calibration experiments as graph nodes, wherein each of the quantum bit calibration experiments is performed based on the directed acyclic graph.

[0010] In one embodiment, the step of determining the parameter level of the repeated output parameters in each overlapping constraint relationship includes:

[0011] Determine the calibration order of repeated output parameters in each overlapping constraint relationship in the calibration experiment process;

[0012] The parameter level of the repeated output parameter in each of the overlapping constraint relationships is determined based on each of the calibrated execution orders, wherein the earlier the calibrated order is, the lower the parameter level is.

[0013] In one embodiment, the step of constructing a directed acyclic graph with each of the qubit calibration experiments as a graph node based on each of the parameter constraint relationships and the parameter hierarchy includes:

[0014] Based on the parameter constraint relationships and the parameter levels, determining a dependency order between the parameter constraint relationships;

[0015] Each of the quantum bit calibration experiments is used as a graph node, directed edges are constructed based on the dependency order, and each of the graph nodes is connected according to each of the directed edges to obtain a directed acyclic graph.

[0016] In one embodiment, the step of constructing directed edges based on the dependency order and connecting the graph nodes according to the directed edges to obtain a directed acyclic graph includes:

[0017] Traversing each of the parameter constraint relationships, taking the parameter constraint relationship as the current parameter constraint relationship, and determining the upper-level parameter constraint relationship and the lower-level parameter constraint relationship of the current parameter constraint relationship based on the dependency order, wherein the experimental output parameters in the upper-level parameter constraint relationship are the experimental input parameters in the current parameter constraint relationship, and the experimental input parameters in the lower-level parameter constraint relationship are the experimental output parameters in the current parameter constraint relationship;

[0018] Generate a first connecting edge from the upper-level graph node to the current graph node, generate a second connecting edge from the current graph node to the lower-level graph node, connect the upper-level graph node, the current graph node and the lower-level graph node through the first connecting edge and the second connecting edge until all the graph nodes are connected, thereby obtaining a directed acyclic graph; wherein the upper-level graph node is the graph node corresponding to the upper-level parameter constraint relationship, the current graph node is the graph node corresponding to the current parameter constraint relationship, and the lower-level graph node is the graph node corresponding to the lower-level parameter constraint relationship.

[0019] In one embodiment, the step of determining the upper-level parameter constraint relationship of the current parameter constraint relationship based on the dependency order includes:

[0020] Taking the experimental input parameters in the current parameter constraint relationship as search parameters, determining candidate parameter constraint relationships whose experimental output parameters contain the search parameters from each of the parameter constraint relationships;

[0021] If there is a candidate parameter constraint relationship, the candidate parameter constraint relationship is used as the parent parameter constraint relationship of the current parameter constraint relationship;

[0022] If there are multiple candidate parameter constraint relationships, then among the candidate parameter constraint relationships, the candidate constraint relationship whose dependency order is before the current constraint relationship and adjacent to the current parameter constraint relationship is determined as the upper-level parameter constraint relationship.

[0023] In one embodiment, before the step of determining the parameter level of the repeated output parameters in the experimental output parameters of each of the parameter constraint relationships, the method further includes:

[0024] Update each of the parameter constraint relationships according to a preset period to obtain each target parameter constraint relationship;

[0025] The target parameter constraint relationship is used as the parameter constraint relationship, and the step of determining the parameter hierarchy of the repeated output parameters in the experimental output parameters of each of the parameter constraint relationships is performed.

[0026] In addition, to achieve the above-mentioned purpose, the present application also proposes a quantum bit calibration device, the quantum bit calibration device comprising:

[0027] An acquisition module, used to acquire a parameter constraint relationship of each quantum bit calibration experiment, wherein the parameter constraint relationship is used to characterize the parameter dependence of the quantum bit calibration experiment;

[0028] A determination module, used to determine the repeated output parameters from the experimental output parameters of each of the parameter constraint relationships, and determine the parameter level of the repeated output parameters in each overlapping constraint relationship, wherein the overlapping constraint relationship is the parameter constraint relationship in which the repeated output parameters are located, and the parameter level is used to characterize the progressive relationship level formed by the repeated output parameters being output multiple times in the calibration experiment process;

[0029] A construction module is used to construct a directed acyclic graph with each of the quantum bit calibration experiments as graph nodes based on each of the parameter constraint relationships and the parameter hierarchy, wherein each of the quantum bit calibration experiments is performed based on the directed acyclic graph.

[0030] In addition, to achieve the above-mentioned purpose, the present application also proposes a quantum computer, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the quantum bit calibration method as described above.

[0031] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the quantum bit calibration method described above are implemented.

[0032] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the quantum bit calibration method described above.

[0033] In the present application, the parameter constraint relationship of each quantum bit calibration experiment is obtained, wherein the parameter constraint relationship is used to characterize the parameter dependence of the quantum bit calibration experiment; the repeated output parameters are determined from the experimental output parameters of each parameter constraint relationship, and the parameter hierarchy of the repeated output parameters in each overlapping constraint relationship is determined, wherein the overlapping constraint relationship is the parameter constraint relationship in which the repeated output parameter is located, and the parameter hierarchy is used to characterize the progressive relationship level formed by the repeated output parameter being output multiple times in the calibration experiment process; based on each parameter constraint relationship and parameter hierarchy, a directed acyclic graph with each quantum bit calibration experiment as a graph node is constructed, wherein each quantum bit calibration experiment is performed based on the directed acyclic graph.

[0034] Compared to relying on the personal experience of experimenters to construct a directed acyclic graph, the directed acyclic graph in this application is constructed based on the parameter constraint relationship of the quantum bit calibration experiment. The parameter constraint relationship reflects the direct dependency between experiments and the parameter transfer path. Therefore, this application realizes the construction of a directed acyclic graph based on objective and quantifiable data, avoids the influence of artificial experience on the stability and consistency of the directed acyclic graph, improves the accuracy of the directed acyclic graph, and thus improves the accuracy of quantum bit calibration based on the directed acyclic graph.

[0035] In addition, since the manipulation and reading information of quantum bits are usually loaded on the phase and amplitude of microwave signals, the parameters of microwave signals will be repeatedly measured and adjusted in multiple calibration experiments. By determining the parameter hierarchy of repeated output parameters, the progressive relationship and dependency hierarchy of repeated output parameters in the experimental process can be determined, thereby avoiding the occurrence of loops caused by repeated output parameters in the process of constructing a directed acyclic graph, improving the accuracy of the directed acyclic graph, and thus improving the accuracy of quantum bit calibration. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0037] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0038] Figure 1 A schematic diagram of a process flow provided for the first embodiment of the quantum bit calibration method of the present application;

[0039] Figure 2 A schematic diagram of a simplified process of a quantum bit calibration method provided in one embodiment of the present application;

[0040] Figure 3 A schematic diagram of parameter hierarchy of a quantum bit calibration method provided in one embodiment of the present application;

[0041] Figure 4 A schematic diagram of experimental nodes involved in a quantum bit calibration method provided in one embodiment of the present application;

[0042] Figure 5 A schematic diagram of parameter constraint relationships involved in a quantum bit calibration method provided in one embodiment of the present application;

[0043] Figure 6 This is a schematic diagram of the module structure of the quantum bit calibration device according to an embodiment of the present application;

[0044] Figure 7 Schematic diagram of the device structure of the hardware operating environment involved in the quantum bit calibration method in the embodiment of the present application.

[0045] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0046] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0047] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0048] The main solution of the embodiment of the present application is: obtaining the parameter constraint relationship of each quantum bit calibration experiment, wherein the parameter constraint relationship is used to characterize the parameter dependence of the quantum bit calibration experiment; determining the repeated output parameter from the experimental output parameters of each parameter constraint relationship, and determining the parameter hierarchy of the repeated output parameter in each overlapping constraint relationship, wherein the overlapping constraint relationship is the parameter constraint relationship in which the repeated output parameter is located, and the parameter hierarchy is used to characterize the progressive relationship level formed by the repeated output parameter being output multiple times in the calibration experiment process; based on each parameter constraint relationship and parameter hierarchy, constructing a directed acyclic graph with each quantum bit calibration experiment as a graph node, wherein each quantum bit calibration experiment is executed based on the directed acyclic graph.

[0049] In this embodiment, for ease of description, the following description is made with a quantum computer as the execution subject.

[0050] The core of superconducting quantum computing is to achieve specific computing capabilities through the time evolution of superconducting quantum bits. The information of superconducting quantum bits is loaded on the phase and amplitude of microwave signals. Therefore, to achieve specific computing goals, the parameters of these signals need to be accurately calibrated. Each bit needs to be calibrated including multiple parameters such as bit information, control and reading parameters. Each parameter requires multiple experiments to be accurately calibrated. Each bit on the same chip needs to be calibrated separately. Moreover, the calibrated parameters will drift over time, so repeated calibration is required. At present, the manual calibration method of experimenters relies heavily on the operator's experience on the one hand, and on the other hand, it also brings repetitive and tedious work to the experimenters. Moreover, as the chip scale increases, the calibration work becomes more arduous. In this case, once the bit performance decreases, it will be a huge challenge for the experimental operator to locate which parameters have problems and recalibrate from the complicated calibration process. In order to achieve high-standard calibration capabilities and liberate experimenters from calibration work, the realization of automated calibration is an inevitable requirement in the field of superconducting quantum computing as the bit scale develops.

[0051] The current automated calibration solution is to represent each calibration experiment with a node, and the dependencies between calibration experiments with a directed acyclic graph. The forward calibration and reverse inspection functions are implemented based on the generated directed acyclic graph. The most important part is the construction of the directed acyclic graph. Currently, the directed acyclic graph is constructed by defining the order between nodes based on the builder's experimental experience. This method depends entirely on the builder's personal experimental experience. In addition, the input and output parameter dependencies of each experiment in the graph constructed by this method are completely hidden, so it is difficult to intuitively judge whether the dependencies between nodes are set appropriately. Therefore, a method for generating a directed acyclic graph with high quality and high input and output transparency is very necessary.

[0052] In summary, the existing directed acyclic graph is constructed according to the actual experimental operation sequence of the experimenter. This method has the following disadvantages:

[0053] 1. Poor stability: Since the order of nodes in the graph is artificially specified, the performance of graphs constructed by different people varies and is not stable enough. For example, some experimental personnel are inexperienced and the dependencies of the graphs they construct are not reasonable enough, which will result in poor performance of the automated calibration implemented based on the graph.

[0054] 2. Implicit parameter dependency: The node order of this method is constructed according to the parameter dependency understood by the builder. What is finally displayed is the node dependency, and the parameter dependency is not exposed. This is not friendly to situations where problems that cannot be solved by automatic calibration require manual intervention, especially when the chip scale is relatively large. It is difficult to analyze parameter problems on a complex directed acyclic graph based solely on node dependencies.

[0055] 3. High error rate: It is difficult to intuitively judge whether the node dependency is reasonable for a graph generated according to a manually specified node order, and it is more likely to have unreasonable definitions.

[0056] The present application provides a solution. Compared with relying on the personal experience of experimenters to construct a directed acyclic graph, the present application constructs a directed acyclic graph based on the parameter constraint relationship of the quantum bit calibration experiment. The parameter constraint relationship reflects the direct dependency between experiments and the parameter transfer path. Therefore, the present application realizes the construction of a directed acyclic graph based on objective and quantifiable data, avoids the stability and consistency of the directed acyclic graph being affected by artificial experience, improves the accuracy of the directed acyclic graph, and thus improves the accuracy and efficiency of the quantum bit calibration.

[0057] In addition, since the manipulation and reading information of quantum bits are usually loaded on the phase and amplitude of microwave signals, the parameters of microwave signals will be repeatedly measured and adjusted in multiple calibration experiments. By determining the parameter hierarchy of repeated output parameters, the progressive relationship and dependency hierarchy of repeated output parameters in the experimental process can be determined, thereby avoiding the occurrence of loops caused by repeated output parameters in the process of constructing a directed acyclic graph, improving the accuracy of the directed acyclic graph, and thus improving the accuracy and efficiency of quantum bit calibration.

[0058] In addition, the present application constructs a directed acyclic graph by separately constructing parameter constraints of independent nodes, thereby reducing the complexity of constructing the directed acyclic graph, and the parameter dependency relationship of each bit calibration experiment in the present application can be explicitly displayed through the parameter constraint relationship, thereby improving the convenience and visualization of subsequent directed acyclic graph maintenance.

[0059] It should be noted that the execution subject of this embodiment may be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions, a quantum computer, etc. The following takes a quantum computer as an example to illustrate this embodiment and the following embodiments.

[0060] Based on this, the embodiment of the present application provides a quantum bit calibration method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the quantum bit calibration method of the present application.

[0061] In this embodiment, the quantum bit calibration method includes steps S10 to S30:

[0062] Step S10, obtaining a parameter constraint relationship of each quantum bit calibration experiment, wherein the parameter constraint relationship is used to characterize the parameter dependence of the quantum bit calibration experiment;

[0063] It should be noted that the qubit calibration experiment (hereinafter referred to as the calibration experiment for convenience) is an experiment used to accurately manipulate and read the signal parameters of superconducting qubits. Parameter constraint relationships are used to characterize the dependencies between various parameters in the qubit calibration experiment. In the qubit calibration experiment, different parameters may be interdependent. For example, the value of a parameter may depend on the value of another parameter. Parameter constraint relationships are used to describe the dependencies between parameters.

[0064] In this embodiment, the process of obtaining the parameter constraint relationship is not limited here. It can be that the engineer determines the parameter constraint relationship based on the experimental input parameters and experimental output parameters of the quantum bit calibration experiment, and then the quantum computer receives the parameter constraint relationship given by the experimenter; it can also be that the quantum computer determines the experimental input parameters and experimental output parameters of the quantum bit calibration experiment, and then constructs the parameter constraint relationship based on the experimental input parameters and experimental output parameters. There is no specific limitation here, and it can be set according to actual needs. It should be noted that the experimental output parameter refers to the parameter calibrated or output in the quantum bit calibration experiment, and the experimental input parameter refers to the parameter used as the input of the calibration experiment. The experimental input parameters and experimental output parameters between each quantum bit calibration experiment may be repeated or different. The experimental input parameters and experimental output parameters of the same quantum bit calibration experiment may be repeated or different, depending on the specific quantum bit calibration experiment, and are not limited here.

[0065] It can be understood that by establishing parameter constraint relationships, it is possible to ensure that the parameter dependencies between experiments are accurately identified, providing the necessary input information for the subsequent establishment of a directed acyclic graph.

[0066] Step S20, determining a repeated output parameter from the experimental output parameters of each of the parameter constraint relationships, and determining a parameter level of the repeated output parameter in each overlapping constraint relationship, wherein the overlapping constraint relationship is a parameter constraint relationship in which the repeated output parameter is located, and the parameter level is used to characterize the progressive relationship level formed by the repeated output parameter being output multiple times in the calibration experiment process;

[0067] It should be noted that repeated output parameters refer to parameters that are output in multiple parameter constraint relationships, that is, repeated output parameters are parameters output by multiple quantum bit calibration experiments. The process of determining repeated output parameters can be: identifying the experimental output parameters of the calibration experiment in each parameter constraint relationship, traversing the experimental output parameters of all parameter constraint relationships, determining the parameters that appear repeatedly in multiple parameter constraint relationships, and determining the repeated parameters as repeated output parameters. It can be understood that by identifying all parameters that are repeatedly output in multiple parameter constraint relationships, it is ensured that the subsequent steps can accurately analyze the dependencies and hierarchies of these repeated output parameters, thereby improving the accuracy of the directed acyclic graph.

[0068] In this embodiment, the parameter constraint relationship in which the experimental output parameter is a repeated output parameter is called an overlapping constraint relationship. The same repeated output parameter corresponds to different parameter levels in different overlapping constraint relationships. The parameter level refers to the progressive relationship level formed by the repeated output parameter being output multiple times in the quantum bit calibration experiment process. The calibration experiment process refers to the experimental process composed of multiple quantum bit calibration experiments. For example, suppose there are three experimental nodes A, B and C. The experimental input parameters of experimental node A include parameter 1, the experimental output parameters of experimental node A include parameter a and parameter b, the experimental input parameters of experimental node B include parameter 2 and parameter a, the experimental output parameters of experimental node B include parameter b and parameter c, the experimental input parameters of experimental node C include parameter c and parameter 3, and the output parameters of experimental node C include parameter b and parameter d, wherein parameter b is output in experimental node A, experimental node B and experimental node C, so parameter b is a repeated output parameter. Based on the above It can be seen from the parameters that the parameter flow between each experimental node is: parameter 1 is input into experimental node A to output parameter a and parameter b, parameter a and parameter 2 are input into experimental node B to output parameter b and parameter c, parameter c and parameter 3 are input into experimental node C to output parameter b and parameter d. It can be seen that the execution order of each experimental node is experimental node A, experimental node B and experimental node C, so the parameter level of parameter b output by experimental node A is at the first level, the parameter level of parameter b output by experimental node B is at the second level, and the parameter level of parameter b output by experimental node C is at the third level.

[0069] The parameter level of the repeated output parameter in the overlapping constraint relationship is related to the logical relationship between the various parameters of the calibration experiment. The more forward the position of the repeated output parameter in the parameter flow formed by the various parameters, the lower the level of the progressive relationship formed by the repeated output parameter in the calibration experiment process, and the lower the parameter level of the repeated output parameter. The specific method of determining the parameter level of the repeated output parameter in the overlapping constraint relationship is not limited here. For example, the parameter flow formed by the various parameters of the calibration experiment can be determined according to the various parameter constraint relationships, and the execution order of the overlapping calibration experiment corresponding to the overlapping constraint relationship in the calibration experiment process is determined based on the parameter flow. The more forward the execution order of the overlapping calibration experiment in which the repeated output parameter is located in the calibration experiment process, the lower the level of the progressive relationship formed by the repeated output parameter being output multiple times in the calibration experiment process, and the lower the parameter level of the repeated output parameter in the overlapping constraint relationship; for example, in another feasible implementation, the order of the parameter constraint relationship in which the repeated output parameter is located in each parameter constraint relationship can also be determined, wherein the more forward the order of the parameter constraint relationship in which the repeated output parameter is located, the lower the parameter level. The two implementation methods proposed above are only feasible implementation methods for determining the parameter level and do not constitute a limitation on the steps. The parameter level can be determined by the above two methods or other methods according to actual needs, and no limitation is made here.

[0070] It can be understood that the parameter hierarchy is essentially the identification information of repeated output parameters in different overlapping constraint relationships. Through the parameter hierarchy, different repeated output parameters can be identified in the process of constructing a directed acyclic graph, thereby avoiding parameter loops in the subsequent process of constructing the directed acyclic graph and improving the accuracy of the directed acyclic graph.

[0071] Step S30, constructing a directed acyclic graph with each of the qubit calibration experiments as graph nodes based on each of the parameter constraint relationships and the parameter hierarchy, wherein each of the qubit calibration experiments is performed based on the directed acyclic graph;

[0072] The nodes in the directed acyclic graph represent calibration experiments, and the directed edges represent the dependencies between the experiments. In this embodiment, the construction of the directed acyclic graph can determine the experimental input parameters and experimental output parameters of each calibration experiment based on the parameter constraint relationship, and then determine the order of each parameter constraint relationship based on the flow direction of the experimental output parameters in the parameter constraint relationship, thereby determining the execution order of the calibration experiments corresponding to each parameter constraint relationship, and then construct directed edges based on the execution order of each calibration experiment, and construct a directed acyclic graph with each calibration experiment as a graph node, wherein the process of constructing the directed acyclic graph is not limited here and can be set according to actual needs.

[0073] After obtaining the directed acyclic graph, each quantum bit calibration experiment is performed in the order of the graph. Exemplarily, the specific process of performing the quantum bit calibration experiment can be: traversing each graph node of the directed acyclic graph, determining the execution order of each calibration experiment corresponding to each graph node; and performing each experiment in turn according to the execution order.

[0074] In a feasible implementation manner, before the step S20: determining the parameter level of the repeated output parameters in the experimental output parameters of each of the parameter constraint relationships, the method further includes:

[0075] Step S01, updating each of the parameter constraint relationships according to a preset period to obtain each target parameter constraint relationship;

[0076] In this embodiment, an update period is preset, and the update period can be determined based on factors such as the characteristics of the quantum bit calibration experiment, the stability of the experimental data, changes in equipment performance, fluctuations in the external environment, etc., and is not limited here.

[0077] When it is determined that the update cycle has arrived, the latest experimental data is collected, where the latest experimental data may include the latest experimental parameters of each quantum calibration experiment, such as experimental input parameters, experimental output parameters, etc. The latest experimental data may also include a newly added calibration experiment (hereinafter referred to as the newly added calibration experiment) and the experimental parameters of the newly added calibration experiment; each parameter constraint relationship is updated through the latest experimental data to obtain a new parameter constraint relationship (hereinafter referred to as the target parameter constraint relationship for distinction), and the target parameter constraint relationship can reflect the parameter dependency and transfer path under the current experimental conditions.

[0078] It can be understood that by using more up-to-date experimental data, the timeliness and accuracy of the parameter constraint relationship can be ensured to adapt to changes in experimental conditions and external environment, improve the reliability and accuracy of subsequent determination of parameter levels and construction of DAG graphs, and thus improve the accuracy of calibration experiments based on effective acyclic graphs.

[0079] Step S02, taking the target parameter constraint relationship as the parameter constraint relationship, and executing the step of determining the parameter hierarchy of the repeated output parameters in the experimental output parameters of each of the parameter constraint relationships.

[0080] After the target parameter constraint relationship is generated, the target parameter constraint relationship is used as the parameter constraint relationship, and the subsequent steps of constructing a directed acyclic graph based on the parameter constraint relationship are performed. It can be understood that using the latest parameter constraint relationship to determine the parameter hierarchy and ensure that the construction of the DAG graph is based on the most accurate information can improve the flexibility and adaptability of the entire calibration process and can quickly respond to changes in experimental conditions and external environment.

[0081] In this embodiment, a directed acyclic graph is constructed based on the parameter constraint relationship of the quantum bit calibration experiment. The parameter constraint relationship reflects the direct dependency between experiments and the parameter transfer path. Therefore, this embodiment realizes the construction of a directed acyclic graph based on objective and quantifiable data, avoids the influence of artificial experience on the stability and consistency of the directed acyclic graph, improves the accuracy of the directed acyclic graph, and thus improves the accuracy and efficiency of the quantum bit calibration.

[0082] In addition, since the manipulation and reading information of quantum bits are usually loaded on the phase and amplitude of microwave signals, the parameters of microwave signals will be repeatedly measured and adjusted in multiple calibration experiments. By determining the parameter hierarchy of repeated output parameters, the progressive relationship and dependency hierarchy of repeated output parameters in the experimental process can be determined, thereby avoiding the occurrence of loops caused by repeated output parameters in the process of constructing a directed acyclic graph, improving the accuracy of the directed acyclic graph, and thus improving the accuracy and efficiency of quantum bit calibration.

[0083] Moreover, this embodiment constructs a directed acyclic graph by separately constructing parameter constraints of independent nodes, thereby reducing the complexity of constructing the directed acyclic graph, and the parameter dependency relationship of each bit calibration experiment in this application can be explicitly displayed through the parameter constraint relationship, thereby improving the convenience and visualization of subsequent directed acyclic graph maintenance.

[0084] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated later. On this basis, step S20: determining the parameter level of the repeated output parameters in each overlapping constraint relationship includes:

[0085] Step S201, determining the calibration order of repeated output parameters in each overlapping constraint relationship in the calibration experiment process;

[0086] For each repeated output parameter, find out all parameter constraint relationships (hereinafter referred to as overlapping constraint relationships for distinction) in which the repeated output parameter is located; traverse each overlapping constraint relationship, determine the dependency relationship between the repeated output parameter and other parameters to obtain a parameter stream, which may specifically include determining which parameters are prerequisites (i.e., dependencies) for the repeated output parameter, and which subsequent experiments or parameters the repeated output parameter is a prerequisite for; based on the position of the repeated output parameter in the parameter stream in the overlapping constraint relationship, determine the order in which the repeated output parameters in each overlapping constraint relationship are calibrated in the calibration experiment process. In a specific implementation, the order in which the repeated output parameters are calibrated may be determined by using a parameter stream based on topological sorting, or by using a parameter stream based on depth-first search, which is not limited here and can be set according to actual needs.

[0087] It can be understood that by clarifying the parameter flow of each repeated output parameter in different overlapping constraint relationships, a basis is provided for the subsequent determination of the parameter hierarchy, which can improve the accuracy of constructing the DAG graph.

[0088] Step S202: determining the parameter level of the repeated output parameters in each of the overlapping constraint relationships based on each of the calibrated execution orders, wherein the earlier the calibrated order is, the lower the parameter level is.

[0089] The position of the repeated output parameter after its dependent item and before the dependent item reflects its progressive relationship and dependency depth in the experimental process. Therefore, in this embodiment, the parameter level of the repeated output parameter in each overlapping constraint relationship can be determined based on the calibrated execution order of the repeated output parameter, wherein the earlier the calibrated order is, the earlier the position of the repeated output parameter in the parameter flow and the lower the parameter level.

[0090] In one feasible implementation, the step S30: constructing a directed acyclic graph with each of the qubit calibration experiments as graph nodes based on each of the parameter constraint relationships and the parameter hierarchy, comprises:

[0091] Step S301, determining the dependency order between the parameter constraint relationships based on the parameter constraint relationships and the parameter levels;

[0092] In this implementation, the order of dependency of each parameter constraint relationship is determined based on the parameter inflow and outflow relationship between each parameter constraint relationship and the parameter hierarchy of the repeated output parameters. For example, parameter constraint relationship A inputs parameter 1 and outputs parameter 2 and first-level parameter 3, parameter constraint relationship B inputs parameter 2 and parameter 4 and outputs second-level parameter 3, and parameter constraint relationship C inputs first-level parameter 3 and outputs parameter 5. Then the order of dependency between the parameter constraint relationships is: parameter constraint relationship A is in the first order, parameter constraint relationship B is in the second order, and parameter constraint relationship C is in the third order.

[0093] It can be understood that by integrating hierarchical information and parameter constraint relationships, the chronological dependencies between various parameter constraint relationships are made more complete and accurate, providing a basis for the subsequent construction of a directed acyclic graph.

[0094] Step S302, taking each of the quantum bit calibration experiments as a graph node, constructing directed edges based on the dependency order, and connecting each of the graph nodes according to each of the directed edges to obtain a directed acyclic graph.

[0095] Each quantum bit calibration experiment is represented as a graph node. The execution dependency order between the calibration experiments is determined based on the dependency order between the parameter constraint relationships. Then, directed edges between the graph nodes are constructed based on the execution dependency order between the calibration experiments. The graph nodes are connected according to the directed edges to obtain a directed acyclic graph.

[0096] Furthermore, in order to ensure the acyclicity of the directed acyclic graph, after connecting the graph nodes, it is possible to check whether there is a cycle in the constructed initial graph, and eliminate the cycle if there is a cycle. Specifically, if there is a cycle in the constructed initial graph, it is detected whether there are repeated output parameters in the cycle. If there are repeated output parameters, the loop constraint relationship involved in the cycle can be determined, and the repeated output parameters (called target output parameters for distinction) whose parameter levels match the loop constraint relationship can be determined from the constraint relationships of different parameter levels, and the directed acyclic graph is reconstructed using the overlapping constraint relationship and the loop constraint relationship of the target output parameter.

[0097] It can be understood that through a directed acyclic graph with quantum bit calibration experiments as graph nodes, the dependencies between experiments and parameter transfer paths can be accurately reflected, providing a basis for subsequent calibration experiments, thereby improving the accuracy of the calibration experiments.

[0098] In one feasible implementation, the step S302: constructing directed edges based on the dependency order, and connecting the graph nodes according to the directed edges to obtain a directed acyclic graph, includes:

[0099] Step S3021, traverse each of the parameter constraint relationships, take the parameter constraint relationship as the current parameter constraint relationship, and determine the upper-level parameter constraint relationship and the lower-level parameter constraint relationship of the current parameter constraint relationship based on the dependency order, wherein the experimental output parameter in the upper-level parameter constraint relationship is the experimental input parameter in the current parameter constraint relationship, and the experimental input parameter in the lower-level parameter constraint relationship is the experimental output parameter in the current parameter constraint relationship;

[0100] From each parameter constraint relationship, select a parameter constraint relationship as the current parameter constraint relationship for traversal. For the current parameter constraint relationship, find its superior parameter constraint relationship and subordinate parameter constraint relationship until all parameter constraint relationships have been traversed. Among them, the superior parameter constraint relationship refers to those relationships whose experimental output parameters serve as the experimental input parameters of the current parameter constraint relationship, and the subordinate parameter constraint relationship refers to those relationships whose experimental input parameters serve as the experimental output parameters of the current parameter constraint relationship.

[0101] In a specific implementation, the upper-level parameter constraint relationship can be determined by searching for the experimental input parameters in the parameter constraint relationship and searching for the cases where these experimental input parameters are used as experimental output parameters in other parameter constraint relationships. The lower-level parameter constraint relationship can be determined by searching for the experimental output parameters of the current parameter constraint relationship and searching for the cases where these experimental output parameters are used as experimental input parameters in other parameter constraint relationships.

[0102] Step S3022, generate a first connecting edge from the upper-level graph node to the current graph node, generate a second connecting edge from the current graph node to the lower-level graph node, connect the upper-level graph node, the current graph node and the lower-level graph node through the first connecting edge and the second connecting edge until all the graph nodes are connected, thereby obtaining a directed acyclic graph; wherein the upper-level graph node is the graph node corresponding to the upper-level parameter constraint relationship, the current graph node is the graph node corresponding to the current parameter constraint relationship, and the lower-level graph node is the graph node corresponding to the lower-level parameter constraint relationship.

[0103] For each current parameter constraint relationship, generate a first connection edge from the upper-level graph node (i.e., the graph node corresponding to the upper-level parameter constraint relationship) to the current graph node, indicating that the experimental output parameter of the upper-level parameter constraint relationship serves as the experimental input parameter of the current parameter constraint relationship; generate a second connection edge from the current graph node to the lower-level graph node (i.e., the graph node corresponding to the lower-level parameter constraint relationship), indicating that the experimental output parameter of the current parameter constraint relationship serves as the experimental input parameter of the lower-level parameter constraint relationship; repeat the above steps until all graph nodes are connected by connection edges to obtain a directed acyclic graph.

[0104] In one feasible implementation, in step S3021: the step of determining the upper-level parameter constraint relationship of the current parameter constraint relationship includes:

[0105] Step S30211, using the experimental input parameter in the current parameter constraint relationship as a search parameter, and determining a candidate parameter constraint relationship whose experimental output parameter includes the search parameter from each of the parameter constraint relationships;

[0106] It should be noted that repeated output parameters may be used as input parameters in a certain parameter constraint relationship. Therefore, in the process of determining the upper-level parameter constraint relationship based on the experimental input parameters, it is possible to find multiple output parameter flows to the upper-level parameter constraint relationship of the current parameter constraint relationship. At this time, it is necessary to determine the upper-level parameter constraint relationship from multiple upper-level parameter constraint relationships.

[0107] Specifically, the experimental input parameters in the current parameter constraint relationship are used as search parameters, all parameter constraint relationships are traversed, and it is checked whether the experimental output parameters of each parameter constraint relationship contain the search parameters. If they do, the parameter constraint relationship is used as a candidate parameter constraint relationship. It can be understood that through this step, all parameter constraint relationships that may be the superior of the current parameter constraint relationship can be screened out, providing a basis for the subsequent determination of the superior parameter constraint relationship.

[0108] Step S30212: if there is a candidate parameter constraint relationship, use the candidate parameter constraint relationship as the parent parameter constraint relationship of the current parameter constraint relationship;

[0109] If there is a candidate parameter constraint relationship, it is directly determined as the parent parameter constraint relationship of the current parameter constraint relationship.

[0110] Step S30213, if there are multiple candidate parameter constraint relationships, determine the association order of the current parameter constraint relationship in each of the candidate parameter constraint relationships, and determine in each of the candidate parameter constraint relationships, the candidate constraint relationship whose parameter hierarchy of the experimental output parameter matches the association order as the upper-level parameter constraint relationship.

[0111] If there are multiple candidate parameter constraint relationships, the candidate constraint relationship whose dependency order is before the current constraint relationship and adjacent to the current parameter constraint relationship among the candidate parameter constraint relationships is determined as the superior parameter constraint relationship. It can be understood that when there are multiple candidate parameter constraint relationships, the superior parameter constraint relationship whose dependency order is before the current constraint relationship and adjacent to the current parameter constraint relationship is selected by comprehensively considering the association order and parameter hierarchy, which can provide necessary information and basis for the subsequent construction of a directed acyclic graph.

[0112] It can be understood that when determining the subordinate parameter constraint relationship, the experimental output parameter of the current parameter constraint relationship can be used as the subordinate search parameter, and the subordinate candidate parameter constraint relationship in which the experimental output parameter includes the search parameter is determined from each of the parameter constraint relationships; if there is one subordinate candidate parameter constraint relationship, the subordinate candidate parameter constraint relationship is used as the subordinate parameter constraint relationship of the current parameter constraint relationship; if there are multiple subordinate candidate parameter constraint relationships, the subordinate candidate constraint relationship in each of the subordinate candidate parameter constraint relationships whose dependency order is after the current constraint relationship and adjacent to the current parameter constraint relationship is determined as the subordinate parameter constraint relationship.

[0113] Compared to relying on the personal experience of experimenters to construct a directed acyclic graph, the directed acyclic graph in this embodiment is constructed based on the parameter constraint relationship of the quantum bit calibration experiment. The parameter constraint relationship reflects the direct dependency between experiments and the parameter transfer path. Thus, the embodiment realizes the construction of a directed acyclic graph based on objective and quantifiable data, avoids the stability and consistency of the directed acyclic graph being affected by artificial experience, improves the accuracy of the directed acyclic graph, and thus improves the accuracy of quantum bit calibration based on the directed acyclic graph.

[0114] In addition, since the manipulation and reading information of quantum bits are usually loaded on the phase and amplitude of microwave signals, the parameters of microwave signals will be repeatedly measured and adjusted in multiple calibration experiments. By determining the parameter hierarchy of repeated output parameters, the progressive relationship and dependency hierarchy of repeated output parameters in the experimental process can be determined, thereby avoiding the occurrence of loops caused by repeated output parameters in the process of constructing a directed acyclic graph, improving the accuracy of the directed acyclic graph, and thus improving the accuracy of quantum bit calibration.

[0115] For example, to help understand the implementation process of the quantum bit calibration method obtained by combining this embodiment with the above-mentioned embodiment 1, please refer to Figure 2 , Figure 2 A brief flow chart of a quantum bit calibration method is provided. Specifically, first, the parameter dependencies are compared and the parameters are layered. Specifically, the following steps are performed according to the parameter refinement level: Figure 3 The layered processing shown in the figure includes multiple parameters such as a, b, and c. Each parameter is output M times. Figure 3 Then, the parameter dependency relationship is constructed to generate nodes, and the following is constructed according to the defined parameter dependency relationship: Figure 4 The experiment node shown has input and output parameters. Figure 4 The experimental node includes n experimental input parameters and several experimental output parameters. Finally, the parameter dependency relationship is spliced ​​to generate a graph (directed acyclic graph), and directed edges are constructed according to the input and output relationship of the node. The specific process is as follows: Figure 5 As shown, Figure 5 The input parameters of the V1 node are parameter 1 and parameter 2, and the output parameter is 3. The input parameters of the V2 node are parameter 1 and parameter 3, and the output parameter is 4. Therefore, in the directed acyclic graph, the connection edge between the V1 node and the V2 node points from V1 to V2.

[0116] In this embodiment, each constraint relationship is used to describe the dependency relationship of a calibration experiment parameter. A calibration experiment depends on some parameters and also calibrates some parameters. Then, the dependent parameters are integrated into the experimental input parameter list of this experiment, and the calibrated parameters are integrated into the experimental output parameter list. The constraint relationship of this experiment can be defined as {experimental input parameter list + calibration experiment output parameter list}.

[0117] For parameter layering, if a parameter is calibrated multiple times, that is, it appears multiple times in the experimental output parameter list of the constraint relationship, then the parameter needs to be layered according to the accuracy of the parameter. For example, if a parameter appears M times in the experimental output parameter list of all constraint relationships, it needs to be divided into M layers of parameters with different accuracy. After that, all constraint relationships are updated again based on the layered parameters. Parameter layering is an important means to avoid loops in the subsequently generated directed acyclic graph.

[0118] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the quantum bit calibration method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0119] This application also provides a quantum bit calibration device, please refer to Figure 6 , the quantum bit calibration device comprises:

[0120] An acquisition module 10 is used to acquire a parameter constraint relationship of each quantum bit calibration experiment, wherein the parameter constraint relationship is used to characterize the parameter dependency relationship of the quantum bit calibration experiment;

[0121] A determination module 20, used to determine the repeated output parameters from the experimental output parameters of each of the parameter constraint relationships, and to determine the parameter level of the repeated output parameters in each overlapping constraint relationship, wherein the overlapping constraint relationship is the parameter constraint relationship in which the repeated output parameters are located, and the parameter level is used to characterize the progressive relationship level formed by the repeated output parameters being output multiple times in the calibration experiment process;

[0122] The construction module 30 is used to construct a directed acyclic graph with each of the quantum bit calibration experiments as graph nodes based on each of the parameter constraint relationships and the parameter hierarchy, wherein each of the quantum bit calibration experiments is performed based on the directed acyclic graph.

[0123] Optionally, the determining module 20 is further configured to:

[0124] Determine the calibration order of repeated output parameters in each overlapping constraint relationship in the calibration experiment process;

[0125] The parameter level of the repeated output parameter in each of the overlapping constraint relationships is determined based on each of the calibrated execution orders, wherein the earlier the calibrated order is, the lower the parameter level is.

[0126] Optionally, the building module 30 is further used for:

[0127] Based on the parameter constraint relationships and the parameter levels, determining a dependency order between the parameter constraint relationships;

[0128] Each of the quantum bit calibration experiments is used as a graph node, directed edges are constructed based on the dependency order, and each of the graph nodes is connected according to each of the directed edges to obtain a directed acyclic graph.

[0129] Optionally, the building module 30 is further used for:

[0130] Traversing each of the parameter constraint relationships, taking the parameter constraint relationship as the current parameter constraint relationship, and determining the upper-level parameter constraint relationship and the lower-level parameter constraint relationship of the current parameter constraint relationship based on the dependency order, wherein the experimental output parameters in the upper-level parameter constraint relationship are the experimental input parameters in the current parameter constraint relationship, and the experimental input parameters in the lower-level parameter constraint relationship are the experimental output parameters in the current parameter constraint relationship;

[0131] Generate a first connecting edge from the upper-level graph node to the current graph node, generate a second connecting edge from the current graph node to the lower-level graph node, connect the upper-level graph node, the current graph node and the lower-level graph node through the first connecting edge and the second connecting edge until all the graph nodes are connected, thereby obtaining a directed acyclic graph; wherein the upper-level graph node is the graph node corresponding to the upper-level parameter constraint relationship, the current graph node is the graph node corresponding to the current parameter constraint relationship, and the lower-level graph node is the graph node corresponding to the lower-level parameter constraint relationship.

[0132] Optionally, the building block 30 is further used for:

[0133] Taking the experimental input parameters in the current parameter constraint relationship as search parameters, determining candidate parameter constraint relationships whose experimental output parameters contain the search parameters from each of the parameter constraint relationships;

[0134] If there is a candidate parameter constraint relationship, the candidate parameter constraint relationship is used as the parent parameter constraint relationship of the current parameter constraint relationship;

[0135] If there are multiple candidate parameter constraint relationships, then among the candidate parameter constraint relationships, the candidate constraint relationship whose dependency order is before the current constraint relationship and adjacent to the current parameter constraint relationship is determined as the upper-level parameter constraint relationship.

[0136] Optionally, the device further comprises:

[0137] An updating module, used for updating each of the parameter constraint relationships according to a preset period to obtain each target parameter constraint relationship;

[0138] The determination module 20 is further configured to use the target parameter constraint relationship as the parameter constraint relationship and execute the step of determining the parameter hierarchy of the repeated output parameters in the experimental output parameters of each of the parameter constraint relationships.

[0139] The qubit calibration device provided by the present application adopts the qubit calibration method in the above embodiment, which can solve the technical problem that the stability and consistency of the directed acyclic graph are poor, resulting in low accuracy of automatic calibration of qubits. Compared with the prior art, the beneficial effects of the qubit calibration device provided by the present application are the same as the beneficial effects of the qubit calibration method provided by the above embodiment, and the other technical features in the qubit calibration device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0140] The present application provides a quantum computer, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the quantum bit calibration method in the above-mentioned embodiment.

[0141] Reference below Figure 7 , which shows a schematic diagram of the structure of a quantum computer suitable for implementing an embodiment of the present application. Figure 7 The quantum computer shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0142] like Figure 7As shown, the quantum computer may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the quantum computer are also stored. The processing device 1001, ROM1002, and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the quantum computer to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows a quantum computer with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have alternatively.

[0143] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0144] The quantum computer provided by the present application adopts the quantum bit calibration method in the above embodiment, which can solve the technical problem that the stability and consistency of the directed acyclic graph are poor, resulting in low accuracy of the automatic calibration of quantum bits. Compared with the prior art, the beneficial effects of the quantum computer provided by the present application are the same as the beneficial effects of the quantum bit calibration method provided by the above embodiment, and the other technical features in the quantum computer are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0145] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0146] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0147] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the quantum bit calibration method in the above-mentioned embodiment.

[0148] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: Random Access Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination of the above.

[0149] The computer-readable storage medium may be included in the quantum computer; or it may exist independently without being assembled into the quantum computer.

[0150] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by a quantum computer, the quantum computer is enabled to: obtain the parameter constraint relationship of each quantum bit calibration experiment, wherein the parameter constraint relationship is used to characterize the parameter dependence of the quantum bit calibration experiment; determine the repeated output parameter from the experimental output parameters of each of the parameter constraint relationships, and determine the parameter hierarchy of the repeated output parameter in each overlapping constraint relationship, wherein the overlapping constraint relationship is the parameter constraint relationship in which the repeated output parameter is located, and the parameter hierarchy is used to characterize the progressive relationship level formed by the repeated output parameter being output multiple times in the calibration experiment process; based on each of the parameter constraint relationships and the parameter hierarchy, construct a directed acyclic graph with each of the quantum bit calibration experiments as graph nodes, wherein each of the quantum bit calibration experiments is executed based on the directed acyclic graph.

[0151] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0152] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession 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 square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0153] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0154] The readable storage medium provided in the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned quantum bit calibration method, and can solve the technical problem that the stability and consistency of directed acyclic graphs are poor, resulting in low accuracy of quantum bit automatic calibration. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in the present application are the same as the beneficial effects of the quantum bit calibration method provided in the above-mentioned embodiment, and will not be repeated here.

[0155] The present application also provides a computer program product, including a computer program, which implements the steps of the quantum bit calibration method as described above when executed by a processor.

[0156] The computer program product provided by the present application can solve the technical problem that the stability and consistency of the directed acyclic graph are poor, resulting in low accuracy of automatic calibration of quantum bits. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as the beneficial effects of the quantum bit calibration method provided by the above embodiment, and will not be repeated here.

[0157] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A quantum bit calibration method, characterized in that: The quantum bit calibration method comprises: Obtaining a parameter constraint relationship for each quantum bit calibration experiment, wherein the parameter constraint relationship is used to characterize the parameter dependence of the quantum bit calibration experiment; Determine a repeated output parameter from the experimental output parameters of each of the parameter constraint relationships, and determine a parameter level of the repeated output parameter in each overlapping constraint relationship, wherein the overlapping constraint relationship is a parameter constraint relationship in which the repeated output parameter is located, and the parameter level is used to characterize the progressive relationship level formed by the repeated output parameter being output multiple times in the calibration experiment process; Based on each of the parameter constraint relationships and the parameter hierarchy, a directed acyclic graph is constructed with each of the quantum bit calibration experiments as graph nodes, wherein each of the quantum bit calibration experiments is performed based on the directed acyclic graph.

2. The quantum bit calibration method according to claim 1, characterized in that: The step of determining the parameter level of the repeated output parameters in each overlapping constraint relationship includes: Determine the calibration order of repeated output parameters in each overlapping constraint relationship in the calibration experiment process; The parameter level of the repeated output parameter in each of the overlapping constraint relationships is determined based on each of the calibrated execution orders, wherein the earlier the calibrated order is, the lower the parameter level is.

3. The quantum bit calibration method according to claim 1, characterized in that: The step of constructing a directed acyclic graph with each of the qubit calibration experiments as a graph node based on each of the parameter constraint relationships and the parameter hierarchy comprises: Based on the parameter constraint relationships and the parameter levels, determining a dependency order between the parameter constraint relationships; Each of the quantum bit calibration experiments is used as a graph node, directed edges are constructed based on the dependency order, and each of the graph nodes is connected according to each of the directed edges to obtain a directed acyclic graph.

4. The quantum bit calibration method according to claim 3, characterized in that: The step of constructing directed edges based on the dependency order and connecting the graph nodes according to the directed edges to obtain a directed acyclic graph includes: Traversing each of the parameter constraint relationships, taking the parameter constraint relationship as the current parameter constraint relationship, and determining the upper-level parameter constraint relationship and the lower-level parameter constraint relationship of the current parameter constraint relationship based on the dependency order, wherein the experimental output parameters in the upper-level parameter constraint relationship are the experimental input parameters in the current parameter constraint relationship, and the experimental input parameters in the lower-level parameter constraint relationship are the experimental output parameters in the current parameter constraint relationship; Generate a first connecting edge from the upper-level graph node to the current graph node, generate a second connecting edge from the current graph node to the lower-level graph node, connect the upper-level graph node, the current graph node and the lower-level graph node through the first connecting edge and the second connecting edge until all the graph nodes are connected, thereby obtaining a directed acyclic graph; wherein the upper-level graph node is the graph node corresponding to the upper-level parameter constraint relationship, the current graph node is the graph node corresponding to the current parameter constraint relationship, and the lower-level graph node is the graph node corresponding to the lower-level parameter constraint relationship.

5. The quantum bit calibration method according to claim 4, characterized in that: The step of determining the upper-level parameter constraint relationship of the current parameter constraint relationship based on the dependency order includes: Taking the experimental input parameters in the current parameter constraint relationship as search parameters, determining candidate parameter constraint relationships whose experimental output parameters contain the search parameters from each of the parameter constraint relationships; If there is a candidate parameter constraint relationship, the candidate parameter constraint relationship is used as the parent parameter constraint relationship of the current parameter constraint relationship; If there are multiple candidate parameter constraint relationships, then among the candidate parameter constraint relationships, the candidate constraint relationship whose dependency order is before the current constraint relationship and adjacent to the current parameter constraint relationship is determined as the upper-level parameter constraint relationship.

6. The quantum bit calibration method according to any one of claims 1 to 5, characterized in that: Before the step of determining the parameter level of the repeated output parameters in the experimental output parameters of each of the parameter constraint relationships, the method further includes: Update each of the parameter constraint relationships according to a preset period to obtain each target parameter constraint relationship; The target parameter constraint relationship is used as the parameter constraint relationship, and the step of determining the parameter hierarchy of the repeated output parameters in the experimental output parameters of each of the parameter constraint relationships is performed.

7. A quantum bit calibration device, characterized in that: The quantum bit calibration device comprises: An acquisition module, used to acquire a parameter constraint relationship of each quantum bit calibration experiment, wherein the parameter constraint relationship is used to characterize the parameter dependence of the quantum bit calibration experiment; A determination module, used to determine the repeated output parameters from the experimental output parameters of each of the parameter constraint relationships, and determine the parameter level of the repeated output parameters in each overlapping constraint relationship, wherein the overlapping constraint relationship is the parameter constraint relationship in which the repeated output parameters are located, and the parameter level is used to characterize the progressive relationship level formed by the repeated output parameters being output multiple times in the calibration experiment process; A construction module is used to construct a directed acyclic graph with each of the quantum bit calibration experiments as graph nodes based on each of the parameter constraint relationships and the parameter hierarchy, wherein each of the quantum bit calibration experiments is performed based on the directed acyclic graph.

8. A quantum computer, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the quantum bit calibration method according to any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the quantum bit calibration method according to any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the quantum bit calibration method according to any one of claims 1 to 6 are implemented.

Images