Method, device, equipment and storage medium for determining reading cavity frequency

Abstract

The present disclosure provides a method, device, equipment and storage medium for determining the reading cavity frequency, and relates to the field of computer technology, especially to the field of quantum computers, quantum chips and quantum signal technology. The specific implementation scheme is: obtaining N cavity frequency experimental values, the N cavity frequency experimental values ​​represent the frequency experimental values ​​obtained after measuring the frequencies of N target reading cavities on the target reading line in the quantum chip layout; matching the N cavity frequency experimental values ​​with N design frequency values ​​to obtain the target mapping relationship between the N cavity frequency experimental values ​​and the N design frequency values; the N design frequency values ​​represent the frequency values ​​that each target reading cavity in the N target reading cavities theoretically needs to reach, which are determined in the design stage of the quantum chip layout.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technology, and in particular to the field of quantum computers, quantum chips, and quantum signal technology. Background Art

[0002] Before a quantum device is used, specific technical means are needed to obtain basic information about the quantum device. This process can be called the characterization of the quantum device, and the characterization of the frequency of the reading cavity in the quantum device is the most basic step.

[0003] In actual scenarios, there are often multiple reading cavities on a reading line of a quantum chip in a quantum device. Therefore, how to automatically characterize the reading cavity has become a topic worthy of attention. Summary of the invention

[0004] The present disclosure provides a method, device, equipment and storage medium for determining a reading cavity frequency.

[0005] According to one aspect of the present disclosure, a method for determining a reading cavity frequency is provided, comprising:

[0006] Obtaining N cavity frequency experimental values, wherein the N cavity frequency experimental values ​​represent frequency experimental values ​​obtained after measuring the frequencies of N target reading cavities on the target reading line in the quantum chip layout;

[0007] Matching N cavity frequency experimental values ​​with N design frequency values ​​to obtain a target mapping relationship between the N cavity frequency experimental values ​​and the N design frequency values; wherein the N design frequency values ​​represent the frequency values ​​that each target reading cavity in the N target reading cavities theoretically needs to achieve, which are determined during the design phase of the quantum chip layout.

[0008] According to another aspect of the present disclosure, there is provided a device for determining a reading cavity frequency, comprising:

[0009] An acquisition unit, used to acquire N cavity frequency experimental values, wherein the N cavity frequency experimental values ​​represent frequency experimental values ​​obtained after measuring the frequencies of N target reading cavities on the target reading line in the quantum chip layout;

[0010] A matching unit is used to match N cavity frequency experimental values ​​with N design frequency values ​​to obtain a target mapping relationship between the N cavity frequency experimental values ​​and the N design frequency values; wherein the N design frequency values ​​represent the frequency values ​​that each target reading cavity in the N target reading cavities theoretically needs to achieve, which are determined in the design stage of the quantum chip layout.

[0011] According to another aspect of the present disclosure, there is provided a computing device, including:

[0012] At least one quantum processing unit QPU;

[0013] a memory coupled to the at least one QPU and configured to store executable instructions,

[0014] The instructions are executed by the at least one QPU so that the at least one QPU can perform the above method;

[0015] Or, include:

[0016] at least one processor; and

[0017] a memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores instructions that can be executed by the at least one processor. The instructions are executed by the at least one processor so that the at least one processor can perform the above-mentioned method.

[0019] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided. When at least one quantum processing unit executes the computer instructions, the at least one quantum processing unit performs the method described above.

[0020] Alternatively, the computer instructions are used to cause the computer to execute the above method.

[0021] According to yet another aspect of the present disclosure, there is provided a computer program product, comprising a computer program, wherein the computer program implements the above method when executed by at least one quantum processing unit;

[0022] Or the computer program implements the above method when executed by a processor.

[0023] In this way, the disclosed solution can obtain N cavity frequency experimental values ​​and obtain the target mapping relationship when it is necessary to characterize the N target reading cavities on the target reading line, so that the cavity frequency experimental value corresponding to the target reading cavity in the N target reading cavities can always be automatically obtained, thereby achieving the correct matching of the N cavity frequency experimental values ​​with the N target reading cavities. Moreover, since the above process does not require manual participation, it effectively saves manual resources. At the same time, as the scale of quantum chips increases, the efficiency of quantum chip characterization will be greatly improved.

[0024] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.

[0026] Figure 1 The following is a schematic diagram of the implementation process of the method for determining the cavity frequency according to the embodiment of the present disclosure. Figure 1 ;

[0027] Figure 2 The following is a schematic diagram of the implementation process of the method for determining the cavity frequency according to the embodiment of the present disclosure. Figure 2 ;

[0028] Figure 3 is a schematic diagram of the overall implementation steps of the method for determining the cavity frequency according to an embodiment of the present disclosure;

[0029] Figure 4 is a schematic diagram of a target change curve according to an embodiment of the present disclosure Figure 1 ;

[0030] Figure 5 is a schematic diagram of specific steps of the cavity frequency matching algorithm according to an embodiment of the present disclosure;

[0031] Figure 6 is a schematic diagram of a target change curve according to an embodiment of the present disclosure Figure 2 ;

[0032] Figure 7 is a schematic diagram of a target change curve according to an embodiment of the present disclosure Figure 3 ;

[0033] Figure 8 is a schematic diagram of the structure of a device for determining a cavity frequency according to an embodiment of the present disclosure;

[0034] Fig. 9 The block diagram is a computing device used to implement the method for determining the cavity frequency according to the embodiment of the present disclosure. DETAILED DESCRIPTION

[0035] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope of the present disclosure. Similarly, for the sake of clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0036] The term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there may be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. The term "at least one" in this article means any combination of at least two of any one or more of a plurality of. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C. The terms "first" and "second" in this article refer to multiple similar technical terms and distinguish them. They do not mean to limit the order or to limit them to only two. For example, the first feature and the second feature refer to two types / two features. The first feature can be one or more, and the second feature can also be one or more.

[0037] In addition, in order to better illustrate the present disclosure, numerous specific details are given in the following specific embodiments. It should be understood by those skilled in the art that the present disclosure can also be implemented without certain specific details. In some examples, methods, means, components and circuits well known to those skilled in the art are not described in detail in order to highlight the main purpose of the present disclosure.

[0038] Quantum computers have powerful information processing capabilities and have received great attention. In quantum computing, quantum measurement is an indispensable part. In order to measure the quantum state of a quantum bit (i.e., the quantum bit state), the quantum chip in a quantum computer can use a device called a read resonant cavity (hereinafter referred to as the read cavity) to read the quantum bit state. An essential step before the actual use of quantum devices such as quantum computers is characterization. Here, characterization refers to obtaining the basic information of a quantum device through specific technical means. Only after obtaining this basic information can quantum operations and quantum measurements of the quantum device be realized.

[0039] It is understandable that since all the information of the quantum bit can be obtained through the reading cavity, the reading cavity needs to be characterized first. In order to achieve the characterization of the reading cavity, it is necessary to first achieve the characterization of the reading cavity frequency, because only when the reading cavity frequency is obtained can the control and measurement of the reading cavity be achieved. Therefore, obtaining the reading cavity frequency is generally the most basic step in the characterization of quantum devices, and the characterization quality of the reading cavity will directly affect the quality of the subsequent entire quantum device characterization process.

[0040] In the related art, after obtaining the cavity frequency experimental value (i.e., the cavity frequency read obtained by measurement), in the case where the cavity frequency experimental value is not necessarily the true cavity frequency read, it is assumed that all cavity frequency experimental values ​​are the true cavity frequency read, and then the cavity frequency experimental value is matched with the design frequency value (i.e., the theoretical value of the cavity frequency read) one by one in ascending order, so as to achieve the matching of the cavity frequency experimental value with the reading cavity corresponding to the design frequency value. The robustness of this approach is obviously not ideal. For example, if there are abnormal values ​​or even missing values ​​in the cavity frequency experimental value, the sorting will be greatly affected, which will greatly affect the matching of the cavity frequency experimental value with the design frequency value. In addition, the abnormal values ​​in the cavity frequency experimental value can be manually removed, and then the corresponding design frequency value can be matched according to the cavity frequency experimental value, but this approach is too dependent on the experience of personnel and cannot achieve automated characterization. As the scale of quantum chips increases, automated characterization is an inevitable trend.

[0041] Based on this, the present disclosure proposes a method for determining the reading cavity frequency to solve the above technical problems.

[0042] Specifically, Figure 1 The following is a schematic diagram of the implementation process of the method for determining the cavity frequency according to the embodiment of the present disclosure. Figure 1 ; The method can be optionally applied to a quantum computing device that has both classical computing capabilities, or can be applied to a classical computing device that has both quantum computing capabilities, or directly applied to a classical computing device, such as a personal computer, server, server cluster, or other electronic device with classical computing capabilities, or directly applied to a quantum computer, and the present disclosure does not impose any restrictions on this.

[0043] Further, the method includes at least part of the following contents. Figure 1 As shown, including:

[0044] Step S101: Obtain N cavity frequency experimental values.

[0045] Here, the N cavity frequency experimental values ​​represent frequency experimental values ​​obtained after measuring the frequencies of the N target reading cavities on the target reading line in the quantum chip layout.

[0046] It should be noted that the cavity frequency experimental value obtained after measuring the frequencies of the N target reading cavities on the target reading line may be the real frequency experimental value of the target reading cavity, or it may be an unreal frequency experimental value. For example, due to the influence of factors such as experimental errors in actual operation and failure in reading cavity processing, the cavity frequency experimental value of one or several target reading cavities may be wrong or even missing. At this time, even in the case of missing, the disclosed solution still needs to obtain N cavity frequency experimental values, so as to facilitate the determination of the corresponding relationship between each cavity frequency experimental value and the design frequency value.

[0047] Further, in one example, at least some of the N cavity frequency experimental values ​​are true frequency experimental values ​​of the target reading cavity on the target reading line; or, in another example, some of the N cavity frequency experimental values ​​are abnormal experimental values, for example, the cavity frequency experimental values ​​are abnormal due to errors or processing failures.

[0048] Step S102: Matching the N cavity frequency experimental values ​​with the N design frequency values ​​to obtain a target mapping relationship between the N cavity frequency experimental values ​​and the N design frequency values.

[0049] Here, the N design frequency values ​​represent the frequency values ​​that each target reading cavity in the N target reading cavities needs to achieve in theory and are determined in the design stage of the quantum chip layout.

[0050] In this way, the disclosed solution can obtain N cavity frequency experimental values ​​and obtain the target mapping relationship when it is necessary to characterize the N target reading cavities on the target reading line, so that the cavity frequency experimental value corresponding to the target reading cavity in the N target reading cavities can always be automatically obtained, thereby achieving the correct matching of the N cavity frequency experimental values ​​with the N target reading cavities. Moreover, since the above process does not require manual participation, it effectively saves manual resources. At the same time, as the scale of quantum chips increases, the efficiency of quantum chip characterization will be greatly improved.

[0051] In addition, the disclosed solution can be applied to the characterization of a reading cavity on any reading line, and therefore, it also has practicality, applicability, and robustness.

[0052] In a specific example of the disclosed scheme, the target mapping relationship can characterize the one-to-one correspondence between the cavity frequency experimental values ​​in N cavity frequency experimental values ​​and the design frequency values ​​in N design frequency values. In this way, the characterization of each target reading cavity on the target reading line can be achieved, and the process is automated and intelligent. At the same time, it is simple and efficient. Compared with the existing scheme, the characterization efficiency of the reading cavity is greatly improved.

[0053] In a specific example of the disclosed solution, when there are abnormal values ​​in the N cavity frequency experimental values, for example, when the cavity frequency experimental values ​​are abnormal due to errors or processing failures, at least one abnormal experimental value among the N cavity frequency experimental values ​​corresponding to the target mapping relationship can also be obtained. In other words, the disclosed solution can not only automatically and intelligently obtain the corresponding relationship between the cavity frequency experimental value and the design frequency value, for example, find the correct cavity frequency experimental value for each reading cavity on the target reading line. At the same time, in the scenario where there are abnormal conditions, the disclosed solution can also effectively identify the abnormal experimental values ​​in the N cavity frequency experimental values, thereby facilitating the locking of the target reading cavity with the abnormality. In this way, while achieving automation and intelligence, the scope of application of the disclosed solution is also effectively improved, so that the disclosed solution has robustness, further improves the characterization efficiency of the reading cavity, and at the same time, further improves the characterization efficiency of the quantum chip.

[0054] In a specific example of the disclosed solution, N cavity frequency experimental values ​​may be obtained in the following manner. Specifically, the above-mentioned obtaining of N cavity frequency experimental values ​​(such as step S101) may specifically include:

[0055] Step S101 - 1 : Obtain a target change curve.

[0056] Here, the target change curve is used to characterize the mapping relationship between the frequency of the driving microwave input to the target read line and the IQ value output by the target read line. Here, the IQ value may specifically refer to the signal value obtained by demodulating the voltage value of the output microwave, which is used to represent the intensity of the output microwave output by the target read line.

[0057] Step S101 - 2 : Determine N target IQ values ​​with the smallest IQ values ​​from the target change curve.

[0058] It should be noted that, in one example, there may be multiple troughs in the target change curve. At this time, the same number of troughs as the number of target reading cavities on the target reading line can be determined from these troughs. For example, the N deepest troughs are selected, and the trough IQ values ​​corresponding to the selected N deepest troughs are used as the N target IQ values.

[0059] Step S101 - 3 : Based on the N target IQ values, N cavity frequency experimental values ​​are obtained.

[0060] For example, by performing relevant calculations based on the target IQ value, the corresponding cavity frequency experimental value can be obtained, and a total of N cavity frequency experimental values ​​can be obtained.

[0061] It is understandable that there may be non-real cavity frequency experimental values ​​among the N cavity frequency experimental values ​​determined by the disclosed solution based on N target IQ values. For example, anomalies caused by the above-mentioned experimental process, or anomalies caused by excessive experimental errors, or frequency loss caused by reading cavity processing errors, etc. However, regardless of whether it is a real cavity frequency experimental value or not, the disclosed solution can automatically and intelligently obtain the corresponding relationship between the design frequency value and the cavity frequency experimental value. At the same time, in the case of non-real cavity frequency experimental values, abnormal experimental values ​​can also be identified.

[0062] In this way, the embodiment of the present disclosure provides a specific scheme for obtaining N cavity frequency experimental values, which is simple and highly interpretable, thereby laying a foundation for improving characterization efficiency. At the same time, it also ensures to a certain extent that the obtained N cavity frequency experimental values ​​can represent the true frequency values ​​of the N target reading cavities.

[0063] Figure 2 The following is a schematic diagram of the implementation process of the method for determining the cavity frequency according to the embodiment of the present disclosure. Figure 2 ; This method can be optionally applied to a quantum computing device with classical computing capabilities, or can be applied to a classical computing device with quantum computing capabilities, or directly applied to a classical computing device, such as a personal computer, server, server cluster, or other electronic device with classical computing capabilities, or directly applied to a quantum computer, and the present disclosure does not limit this. It is understandable that the above Figure 1 The relevant contents of the method shown can also be applied to this example, and the relevant contents will not be described in detail in this example.

[0064] Further, the method includes at least part of the following contents. Figure 2 As shown, including:

[0065] Step S201: Obtain N cavity frequency experimental values.

[0066] Here, the N cavity frequency experimental values ​​represent frequency experimental values ​​obtained after measuring the frequencies of N target reading cavities on the target reading line in the quantum chip layout.

[0067] Here, the relevant contents about the N cavity frequency experimental values ​​can be referred to the above description, which will not be repeated here.

[0068] Step S202: Based on the N cavity frequency experimental values ​​and the N design frequency values, M initial mapping relationships are obtained.

[0069] Here, M is a positive integer greater than or equal to 2; the initial mapping relationship among the M initial mapping relationships can characterize the corresponding relationship between the cavity frequency experimental value and the design frequency value.

[0070] In a specific example, the initial mapping relationship may be obtained in the following manner. Specifically, the above-described method of obtaining M initial mapping relationships (such as step S202) based on the N cavity frequency experimental values ​​and the N design frequency values ​​may specifically include:

[0071] S202-1, determine N1 first ordered sequences and N2 second ordered sequences

[0072] Here, the N1 first ordered sequences are obtained by sorting the N cavity frequency experimental values; correspondingly, the N2 second ordered sequences are obtained by sorting the N design frequency values.

[0073] Furthermore, the values ​​of N1 and N2 are related to N. For example, N1 is a natural number greater than or equal to 1 and less than or equal to N!; and / or N2 is a natural number greater than or equal to 1 and less than or equal to N!.

[0074] S202-2: Based on the N1 first ordered sequences and the N2 second ordered sequences, a one-to-one correspondence between the cavity frequency experimental values ​​and the design frequency values ​​is established to obtain M different initial mapping relationships.

[0075] Here, the value of M is related to N. For example, M is a natural number greater than or equal to 1 and less than or equal to N!.

[0076] For example, in one example, the order of N cavity frequency experimental values ​​can be fixed to obtain a first ordered sequence, and at the same time, the N design frequency values ​​are fully arranged to obtain N! second ordered sequences. At this time, based on a first ordered sequence and N! second ordered sequences, N! different initial mapping relationships can be obtained, for example, based on a one-to-one correspondence between elements in the first ordered sequence and elements at corresponding positions in each second ordered sequence, N! different initial mapping relationships are obtained.

[0077] Alternatively, in another example, the order of the N design frequency values ​​may be fixed to obtain a second ordered sequence, and at the same time, the N cavity frequency experimental values ​​are fully arranged to obtain N! first ordered sequences. In this case, based on a second ordered sequence and N! first ordered sequences, N! different initial mapping relationships are obtained, for example, based on a one-to-one correspondence between elements in the second ordered sequence and elements at corresponding positions in each first ordered sequence, N! different initial mapping relationships are obtained.

[0078] Alternatively, in another example, the N cavity frequency experimental values ​​and the N design frequency values ​​may be sorted respectively, for example, to obtain multiple first ordered sequences and multiple second ordered sequences, and then multiple different initial mapping relationships may be obtained based on the multiple first ordered sequences and multiple second ordered sequences.

[0079] In this way, the disclosed solution provides a specific solution for obtaining the initial mapping relationship, which is simple, efficient and highly interpretable, thereby providing effective support for the subsequent automatic and intelligent acquisition of the correspondence between the design frequency value and the cavity frequency experimental value.

[0080] Step S203: determining an initial frequency difference value set corresponding to each initial mapping relationship in the M initial mapping relationships.

[0081] Here, the initial frequency difference value set corresponding to the initial mapping relationship is obtained based on the difference between the cavity frequency experimental value and the frequency design value having a corresponding relationship in the initial mapping relationship. For example, the N cavity frequency experimental values ​​are: y1, y2, y3...y N , and record the N design frequency values ​​as: x1, x2, x3...x N ; At this time, the initial mapping relationship can be recorded as: {(y i , x i )}, i ranges from 1 to N. Here, the element (y i , x i ) represents y i With x i There is a one-to-one correspondence. At this time, the initial frequency difference set can be recorded as: {z i}, where z i =|x i -y i |.

[0082] It can be understood that the element z in the above initial frequency difference set i The calculation method of is only an example. In practical applications, it can also be z i =x i -y i The present disclosure does not limit this.

[0083] Step S204: determining the target mapping relationship from the M initial mapping relationships based on the initial frequency difference value sets corresponding to the initial mapping relationships.

[0084] In this way, the disclosed scheme provides an automated and intelligent solution for automatically matching the cavity frequency experimental value with the design frequency value. The scheme is simple, efficient, and highly interpretable. It effectively achieves the correct matching of the target reading cavity and the cavity frequency experimental value, improves the matching efficiency, and is also robust.

[0085] Further, in a specific example, the target mapping relationship can be determined from multiple initial mapping relationships in the following manner, thereby effectively improving the accuracy of the determined target mapping relationship, thereby effectively achieving the correct matching of the cavity frequency experimental value with the target reading cavity; and in this process, abnormal situations can also be effectively identified, thereby further improving the robustness of the disclosed solution. Specifically, the above-mentioned determination of the target mapping relationship from the M initial mapping relationships based on the initial frequency difference value set corresponding to each initial mapping relationship (for example, the above-mentioned step S204) can specifically include:

[0086] Step S204-1: Based on the initial frequency difference value set corresponding to the initial mapping relationship, identify the abnormal difference value in the initial frequency difference value set. Here, the method for identifying the abnormal difference value can refer to the relevant description in the following specific example, which will not be repeated here.

[0087] Step S204 - 2 : based on the number of abnormal difference values ​​in the initial frequency difference value set corresponding to the initial mapping relationship, at least one candidate mapping relationship is selected from the M initial mapping relationships.

[0088] Step S204-3: Determine the target mapping relationship based on at least one candidate mapping relationship.

[0089] For example, in a specific example, at least one candidate mapping relationship can be obtained in the following manner, which provides effective support for further improving the accuracy of the target mapping relationship, and the solution is simple and efficient, and at the same time, further improves the robustness. Specifically, the above-mentioned selection of at least one candidate mapping relationship from the M initial mapping relationships based on the number of abnormal differences in the initial frequency difference value set corresponding to the initial mapping relationship (for example, the above-mentioned step S204-2) can specifically include:

[0090] Step S204-2-1: Select at least one initial mapping relationship with the least number of abnormal differences from the M initial mapping relationships.

[0091] Step S204-2-2: taking the selected initial mapping relationship as a candidate mapping relationship.

[0092] Here, in a specific example, for example, in the above step S204-3, when the number of selected candidate mapping relationships is one, the selected candidate mapping relationship can be directly determined as the target mapping relationship. At this time, the target mapping relationship determined in this example can characterize the correct correspondence between the N design frequency values ​​and the N cavity frequency experimental values ​​with the greatest probability.

[0093] Alternatively, in another specific example, for example, in step S204-3 described above, when two or more candidate mapping relationships are selected, the target mapping relationship may be obtained in the following manner, specifically including:

[0094] When two or more candidate mapping relationships are selected, first, abnormal differences are removed from the initial frequency difference set corresponding to the candidate mapping relationships; secondly, the target difference corresponding to each candidate mapping relationship is obtained; here, the target difference corresponding to the candidate mapping relationship is based on other differences in the initial frequency difference set corresponding to the candidate mapping relationship, excluding the abnormal differences; finally, the target mapping relationship is determined based on the target difference corresponding to each candidate mapping relationship.

[0095] In this way, the disclosed solution further refines the application scenarios, provides effective support for further improving the accuracy of the target mapping relationship, and at the same time, further improves the robustness.

[0096] Furthermore, in a specific example, after obtaining the target difference corresponding to each candidate mapping relationship, the target mapping relationship is determined in the following manner, that is, the target difference corresponding to each candidate mapping relationship described above is used to determine the target mapping relationship, specifically including: selecting the candidate mapping relationship with the smallest target difference as the target mapping relationship. In this way, the accuracy of the determined target mapping relationship is further improved, so that the target mapping relationship can characterize the correct correspondence between N design frequency values ​​and N cavity frequency experimental values ​​with the maximum probability, and in the scenario where there are abnormal conditions, the real abnormal experimental value can also be identified with the maximum probability, thus further improving the robustness of the disclosed solution.

[0097] Furthermore, in a specific example, the target difference corresponding to the candidate mapping relationship can be obtained by one of the following methods, so as to further improve the accuracy of the obtained target mapping relationship and further improve the robustness. Specifically, the target difference corresponding to each candidate mapping relationship described above can specifically include:

[0098] Method 1: calculate the standard deviation of the other differences except the abnormal differences in the initial frequency difference set corresponding to the candidate mapping relationship; here, the target difference corresponding to the candidate mapping relationship is the calculated standard deviation.

[0099] Method 2: Calculate the standard deviation of the other differences except the abnormal differences in the initial frequency difference set corresponding to the candidate mapping relationship; and obtain the target difference corresponding to the candidate mapping relationship based on the calculated standard deviation and the number of abnormal differences.

[0100] It should be noted that in actual applications, the above methods can be selected and executed, and the present disclosure does not limit this. Further, it can be understood that the above target difference calculation method is only an exemplary description, and in actual applications, other methods that can distinguish abnormal situations can also be used, and the present disclosure does not limit this.

[0101] The following is a further detailed description of the disclosed solution with reference to specific examples; the disclosed solution proposes a method for automatically and robustly characterizing the reading cavity frequency. The method is firstly fully automated and does not require any human intervention; secondly, the method is robust and can achieve the correct matching of other cavity frequency experimental values ​​with the reading cavity (such as the design frequency value) when some cavity frequency experimental values ​​are missing, and identify abnormal values ​​in the cavity frequency experimental values. Therefore, the disclosed solution is suitable for the characterization of the reading cavity frequency, and the characterization of the reading cavity frequency is an indispensable basic link in the characterization of quantum devices, so the disclosed solution has broad application prospects.

[0102] A quantum chip may have multiple read lines. The operating strategy for each read line in the disclosed embodiment is the same, and one read line is used as an example for description.

[0103] The core idea and complete technical implementation of the disclosed solution are explained in two parts below.

[0104] Part 1: Overall Process

[0105] In a quantum chip, there are often multiple reading cavities on each reading line. Based on this, the disclosed solution has a known theoretical value of the reading cavity frequency, that is, the design frequency values ​​of all target reading cavities on the target reading line are known, and the goal is to identify abnormal values ​​(if any) in the cavity frequency experimental values ​​of the target reading cavity, and match the target reading cavity with its corresponding real cavity frequency experimental value, that is, the output of the disclosed solution is the correct cavity frequency experimental value of the target reading cavity on the target reading line, and in the case of abnormal values, the abnormal experimental value is identified. In this way, the correct cavity frequency experimental value is found for each target reading cavity on the target reading line, and at the same time, the target reading cavity with abnormal conditions is identified.

[0106] like Figure 3 As shown, the overall implementation steps of the embodiment of the present disclosure are as follows:

[0107] Step S301, obtaining a target change curve.

[0108] Theoretically, after the driving microwave is input into the target reading line, if the frequency of the driving microwave is equal to the frequency of the target reading cavity on the target reading line (i.e., resonance occurs), the target reading cavity absorbs a part of the driving microwave and generates a resonance signal. At this time, the frequency value of the target reading cavity that resonates can be identified by changing the microwave parameters of the input driving microwave. For example, the frequency value at the resonance signal can be used as the frequency value of the target reading cavity. Furthermore, all the resonance signals on the target reading line can be obtained by frequency scanning (also called frequency sweeping), and the frequency values ​​at these resonance signals can be used as the cavity frequency experimental values ​​of each target reading cavity on the target reading line.

[0109] In one example, frequency scanning refers to scanning near each target reading cavity. In this way, fine fitting through frequency scanning is beneficial to obtain more information about the target reading cavity, making the acquired cavity frequency experimental value more realistic, thereby laying the foundation for further improving the accuracy of the cavity frequency experimental value and further improving the accuracy of the target mapping relationship.

[0110] It should be noted that due to the influence of factors such as experimental errors in actual operations and failures in reading cavity processing, the reading cavity frequency obtained by identifying the resonance signal is not necessarily the true reading cavity frequency and may also be an erroneous reading cavity frequency.

[0111] Based on the above theory, the target change curve can be obtained in the following way:

[0112] The driving microwave is input into the target read line, and the intensity of the output microwave outputted by the target read line is obtained by scanning the frequency of the driving microwave. The intensity of the output microwave can be represented by an IQ value (i.e., a signal value obtained by demodulating the voltage value of the output microwave). Figure 4 As shown, the frequency of the driving microwave input to the target read line is used as the horizontal axis, and the IQ value of the output microwave is used as the vertical axis to obtain a target change curve. Here, the IQ value corresponding to the trough in the target change curve (for example, it can be called the trough IQ value) may correspond to the cavity frequency experimental value of the target read cavity.

[0113] Step S302, assuming that there are N target reading cavities on the target reading line, determine the N deepest troughs on the obtained target change curve, and based on the trough IQ values ​​corresponding to the N deepest troughs, obtain the cavity frequency experimental values ​​of the disclosed scheme, a total of N cavity frequency experimental values.

[0114] It should be noted that, in some embodiments, ideally, if there are N target reading cavities on the target reading line, there are N deepest valleys in the target variation curve, for example, Figure 4As shown, there are 10 target reading cavities on the target reading line. At this time, there are 10 deepest troughs in the target change curve, and the trough IQ values ​​corresponding to the 10 deepest troughs correspond to the real cavity frequency experimental values ​​of the target reading cavity on the target reading line, a total of 10 cavity frequency experimental values.

[0115] However, in actual scenarios, there may be a situation where the deepest trough does not correspond to the frequency of the target reading cavity on the target reading line. In other words, the reading cavity frequency determined based on the trough IQ value on the target change curve may not be the frequency corresponding to any of the N target reading cavities. For example, due to experimental errors in actual operations, reading cavity processing failures and other factors, the cavity frequency experimental values ​​of one or several target reading cavities may be wrong or even missing. In other words, there may be abnormal cavity frequency experimental values ​​among the obtained N cavity frequency experimental values. In this case, the abnormal values ​​in the cavity frequency experimental values ​​can be effectively identified through the disclosed solution, and then the problematic target reading cavity can be locked.

[0116] It should be noted that in the disclosed solution, regardless of whether the target change curve obtained is a curve under ideal conditions, it is necessary to determine the IQ trough values ​​that are the same as the number of target reading cavities on the target reading line, that is, N IQ trough values. At this time, if the target change curve is a curve under ideal conditions, the disclosed solution can efficiently obtain the corresponding relationship between the cavity frequency experimental value and the design frequency value, thereby realizing the characterization of the target reading cavity; conversely, if the target change curve is a curve under non-ideal conditions, the disclosed solution can efficiently obtain the corresponding relationship between the cavity frequency experimental value and the design frequency value, while also identifying abnormal experimental values, that is, identifying the target reading cavity storing abnormalities. Therefore, the disclosed solution is robust, and even in the case of the lack of the cavity frequency experimental value of the target reading cavity, the cavity frequency experimental value can be correctly matched with the target reading cavity (such as the design frequency value), and at the same time, the target reading cavity with abnormal cavity frequency experimental value can be identified, further improving the practicality.

[0117] Step S303, matching the N cavity frequency experimental values ​​with the N design frequency values, and obtaining a target mapping relationship that can characterize the one-to-one correspondence between the cavity frequency experimental values ​​and the design frequency values. Further, in the case of an abnormal value, the abnormal experimental value can also be output.

[0118] In some embodiments, the cavity frequency matching algorithm of the disclosed solution can be used to achieve the matching of N cavity frequency experimental values ​​with N design frequency values. For example, the design frequency values ​​of N target reading cavities on the target reading line and the N cavity frequency experimental values ​​determined from the target change curve can be used as the input of the cavity frequency matching algorithm, and the output of the cavity frequency matching algorithm is the correct correspondence between each target reading cavity on the target reading line and the cavity frequency experimental value, for example, the target mapping relationship representing the one-to-one correspondence between the cavity frequency experimental value and the design frequency value is output, and at the same time, in the case of an abnormal value, the abnormal experimental value can also be output. In this way, the correct cavity frequency experimental value is found for each target reading cavity on the target reading line, and at the same time, the target reading cavity with an abnormal situation is identified.

[0119] Part II: Cavity Frequency Matching Algorithm

[0120] It should be pointed out that the core idea of ​​the cavity frequency matching algorithm of the disclosed solution is as follows:

[0121] First, an initial frequency difference value set is obtained. For example, when the arrangement order of the N design frequency values ​​is fixed, the N cavity frequency experimental values ​​are fully arranged to obtain all possible mapping modes of the N design frequency values ​​and the N cavity frequency experimental values, a total of N! mapping relationships (corresponding to the initial mapping relationships described above), and the N cavity frequency experimental values ​​in the initial mapping relationships are subtracted from the N design frequency values ​​to obtain an initial frequency difference value set containing N difference values, a total of N! initial frequency difference value sets.

[0122] Secondly, an abnormal point identification algorithm is applied to identify abnormal differences in the initial frequency difference set corresponding to the initial mapping relationship; here, the cavity frequency experimental value corresponding to the abnormal difference can be considered as a suspected abnormal experimental value.

[0123] Here, the goal of the disclosed solution is to find an optimal mapping relationship (that is, the target mapping relationship described above) among all mapping relationships between N cavity frequency experimental values ​​and N design frequency values ​​(for example, the N! mapping relationships described above), satisfying one of the following conditions:

[0124] Condition 1: The number of abnormal difference values ​​in the optimal mapping relationship is the smallest, that is, after the optimal mapping relationship is judged by the abnormal point identification algorithm, the number of abnormal experimental values ​​in the N cavity frequency experimental values ​​determined is the smallest.

[0125] Condition 2: The number of abnormal differences in the optimal mapping relationship is the smallest, and after removing the abnormal differences, the standard deviation obtained from other differences in the initial frequency difference set corresponding to the optimal mapping relationship is the smallest.

[0126] At this time, the correspondence between the N cavity frequency experimental values ​​and the N design frequency values ​​under the optimal mapping relationship found can be considered as the correct correspondence; further, if there is an abnormal difference under the optimal mapping relationship, then the cavity frequency experimental value corresponding to the abnormal difference under the optimal mapping relationship is the abnormal experimental value identified by the disclosed scheme.

[0127] Finally, the optimal mapping relationship is output, and further, if there is an abnormal experimental value under the optimal mapping relationship, the abnormal experimental value is output. In this way, the correct cavity frequency experimental value can be found for each reading cavity on the target reading line, and the target reading cavity with abnormal conditions can be identified.

[0128] In the disclosed solution, one of the following methods can be used to construct the objective function to achieve the above-mentioned objectives; specifically, the following methods include:

[0129] Method 1: constructing the objective function directly based on the number of abnormal difference values ​​corresponding to the mapping relationship. For example, the number of abnormal difference values ​​corresponding to the mapping relationship can be directly used as the function value of the constructed objective function.

[0130] Method 2: Construct a first function based on the number of abnormal differences corresponding to the mapping relationship; for example, the number of abnormal differences corresponding to the mapping relationship can be directly used as the function value of the constructed first function; construct a second function based on the standard deviation of other differences corresponding to the mapping relationship after removing the abnormal differences; construct an objective function based on the first function and the second function.

[0131] For example, in one example, during the optimization process, the function value of the first function can be calculated first, and the mapping relationship with the smallest function value of the first function can be found, and then the function value of the second function corresponding to the mapping relationship with the smallest function value of the first function can be calculated, and the mapping relationship with the smallest function value of the second function can be found. At this time, the mapping relationship with the smallest function value of the second function can be used as the optimal mapping relationship. In other words, in this example, if the number of abnormal difference values ​​under two or more mapping relationships is the same, the mapping relationships with the same number of abnormal difference values ​​can be screened based on the second function, so as to find the optimal mapping relationship.

[0132] Method 3: construct the objective function based on the number of abnormal differences corresponding to the mapping relationship and the standard deviation of other differences corresponding to the mapping relationship after removing the abnormal differences.

[0133] For example, the objective function = the number of abnormal differences corresponding to the mapping relationship + the standard deviation obtained from other differences. Or, the objective function = the number of abnormal differences corresponding to the mapping relationship + the standard deviation obtained from other differences after normalization and dimensionless processing.

[0134] It is understandable that any one of the above three methods can be selected based on actual needs, such as accuracy requirements, and the present disclosure does not limit this.

[0135] On this basis, the optimal mapping relationship can be determined as long as the function value of the objective function is minimized.

[0136] It should be noted that in the process of finding the optimal mapping relationship according to the objective function, you can use, for example, the optimization method in the optimize of Python's SciPy library, or you can use a brute force search method according to the scale of the problem, and the present disclosure does not limit this.

[0137] In some embodiments, the following method can be used to identify abnormal points:

[0138] First, assume that the difference in the initial frequency difference set corresponding to the initial mapping relationship conforms to the normal distribution, which can be recorded as the normal distribution X~N(μ,σ 2 ), where μ represents the normal distribution N(μ,σ 2 ), σ represents the average value of the normal distribution N(μ,σ 2 )’s standard deviation.

[0139] Secondly, traverse the differences in the initial frequency difference set corresponding to the initial mapping relationship, and for any difference x, calculate (x-μ) / σ; if (x-μ) / σ is greater than the abnormal threshold, then the difference x where (x-μ) / σ is greater than the abnormal threshold can be considered to be an abnormal difference. Here, it can be understood that the abnormal threshold can be an empirical value, such as 1, 2 or 3, etc., and the disclosed solution does not limit this. The above-mentioned abnormal point identification algorithm can be called an empirical rule.

[0140] It should be pointed out that in practical applications, for example, an isolation forest algorithm, a K-Means clustering algorithm, etc. may also be used as an outlier identification algorithm of the disclosed solution, and the disclosed solution does not limit this.

[0141] In some embodiments, Figure 5 As shown in the figure, the specific steps of the cavity frequency matching algorithm between the cavity frequency experimental value and the design frequency value are as follows:

[0142] Step S501, set global variables, initialize global variables, and construct target functions.

[0143] For example, set the optimal mapping relationship list (which can be used to store the mapping relationship between N design frequency values ​​and N cavity frequency experimental values), the abnormal difference list (which can be used to store abnormal differences), and the optimal target value (which is used to store the function of the objective function), a total of three global variables. Initialize the optimal mapping relationship list and the abnormal difference list to be empty, and initialize the optimal target value to infinity.

[0144] Here, the objective function can be constructed based on any of the above three methods.

[0145] Step S502: if the update condition is met, the optimal mapping relationship list is updated, and the number of updates is counted. The optimal mapping relationship list records the mapping relationship that needs to be processed currently.

[0146] Here, the update condition can be set based on actual conditions, for example, the current update number is less than N!, etc., and the disclosed solution does not limit this.

[0147] In one example, the optimal mapping relationship list may be updated based on one of the following methods:

[0148] Method 1: obtain a first ordered sequence of N cavity frequency experimental values ​​(i.e., any sequence after sorting the N cavity frequency experimental values), and fully arrange the N design frequency values ​​to obtain N! second ordered sequences; at this time, the optimal mapping relationship list can be updated based on the mapping relationship between the first ordered sequence and different second ordered sequences.

[0149] Method 2: Obtain a second ordered sequence of N design frequency values ​​(i.e., any sequence after sorting the N design frequency values), fully arrange the N cavity frequency experimental values, and obtain N! first ordered sequences; at this time, the optimal mapping relationship list can be updated based on the mapping relationship between the second ordered sequence and different first ordered sequences.

[0150] Method three: arrange N cavity frequency experimental values ​​to obtain multiple different first ordered sequences, and at the same time, arrange N design frequency values ​​to obtain multiple different second ordered sequences; based on the mapping relationship between the multiple different first ordered sequences and the multiple different second ordered sequences, update the optimal mapping relationship list.

[0151] It is understandable that in the third method, the mapping relationship generated after sorting the N cavity frequency experimental values ​​and the N design frequency values ​​at the same time may be repeated. At this time, before updating the optimal mapping relationship list, it can also be determined whether the mapping relationship used to update the optimal mapping relationship list is the same as the mapping relationship currently recorded in the optimal mapping relationship list. If not, the optimal mapping relationship list can continue to be updated. Otherwise, a new mapping relationship is re-selected, and it is continued to be determined whether the re-selected new mapping relationship is the same as the mapping relationship currently recorded in the optimal mapping relationship list until the update process is completed.

[0152] Step S503: applying an outlier recognition algorithm to identify outlier differences in the current optimal mapping relationship list, and updating the outlier difference list based on the identified outlier differences.

[0153] For example, the original data in the abnormal difference value list is deleted, and the currently identified abnormal difference value is stored in the abnormal difference value list to update the abnormal difference value list.

[0154] Step S504: Calculate the function value of the objective function based on the abnormal difference in the current optimal mapping relationship list.

[0155] Step S505, determine whether the calculated function value of the objective function is less than or equal to the optimal objective value; if so, that is, when the calculated function value of the objective function is less than or equal to the optimal objective value, execute step S506. Otherwise, that is, when the calculated function value of the objective function is greater than the optimal objective value, return to step S502 to update the optimal mapping relationship list.

[0156] Step S506, determine whether the preset update times have been reached; if so, execute step S508; otherwise, execute step S507.

[0157] Step S507: Update the optimal target value based on the calculated function value of the target function, and return to step S502.

[0158] For example, based on identifying abnormal differences in the current optimal mapping relationship list and the objective function constructed above, the function value of the objective function is obtained, and the function value of the currently obtained objective function replaces the original data in the optimal target value to update the optimal target value.

[0159] Step S508: output an optimal mapping relationship list and an abnormal difference value list.

[0160] Here, the mapping relationship stored in the output optimal mapping relationship list is the optimal mapping relationship, and correspondingly, the cavity frequency experimental value corresponding to the abnormal difference recorded in the abnormal difference list is the abnormal experimental value.

[0161] In order to better understand the disclosed solution, the following validity verification is performed:

[0162] For example, if Figure 6 As shown, there are three target reading cavities on the target reading line, which can be recorded as q01 to q03 respectively. After sorting the design frequency values ​​from small to large, {q02, q03, q01} are obtained; by sweeping the target reading line, three cavity frequency experimental values ​​can be identified, such as Figure 6As shown in the figure, they are 4.3GHz, 4.359GHz, and 4.456GHz respectively; however, in the actual scenario, the target reading cavity corresponding to q01 is not processed well, resulting in the loss of the cavity frequency experimental value of q01, and the cavity frequency signal at the cavity frequency experimental value of 4.3GHz is abnormal. At this time, if the existing scheme is followed and the design frequency value is directly matched with the cavity frequency experimental value based on the order of size, the following mapping relationship can be obtained: q02 = 4.3GHz, q03 = 4.359GHz and q01 = 4.456GHz. Obviously, this mapping relationship is wrong.

[0163] In this scenario, the disclosed solution can be applied to obtain the target mapping relationship, that is, q02 = 4.359 GHz, q03 = 4.456 GHz, q01 = 4.3 GHz. At the same time, it can be identified that the experimental value of the cavity frequency of q01 is abnormal.

[0164] For example, Figure 7 As shown, there are 8 target reading cavities on the target reading line, which can be recorded as q09, q11, q13, q15, q10, q12, q14, and q16 respectively after being sorted from small to large according to the design frequency value. Among them, the target reading cavity q14 did not produce a cavity frequency experimental value, that is, the cavity frequency experimental value at 4.413GHz was misidentified. At this time, if matching is performed according to the existing scheme, the cavity frequency experimental values ​​corresponding to q10, q12, and q14 will all be wrong. However, by applying the disclosed scheme, in addition to being able to identify that the cavity frequency experimental value corresponding to the target reading cavity q14 is an abnormal experimental value, the remaining target reading cavities and cavity frequency experimental values ​​will obtain a correct correspondence.

[0165] In summary, the disclosed scheme proposes a method for automatically and robustly characterizing the reading cavity frequency. Even if the cavity frequency experimental values ​​of one or several target reading cavities are wrong or even missing, it is possible to automatically match the correct cavity frequency experimental values ​​for other target reading cavities and obtain abnormal experimental values.

[0166] Furthermore, the scope of application of the disclosed solution is: any scenario in which the reading cavity frequencies of multiple reading cavities on a reading line need to be characterized in any quantum device, so it has a wide scope of application; and the disclosed solution also has the following effects:

[0167] (1) When it is necessary to match N cavity frequency experimental values ​​with N design frequency values, the method for determining the reading cavity frequency proposed in the disclosed scheme can effectively identify abnormal experimental values ​​among the N cavity frequency experimental values, and obtain the correct matching relationship between the N cavity frequency experimental values ​​and the N design frequency values.

[0168] (2) The disclosed solution proposes a specific method for achieving effective matching and provides a method for constructing an objective function, thereby improving the accuracy of the target mapping relationship of the disclosed solution, and the constructed objective function is highly interpretable.

[0169] (3) The disclosed solution can find the optimal mapping relationship based on the objective function constructed above and adopt the cavity frequency matching algorithm. This method is simple and efficient. It should be noted that the disclosed solution does not limit the optimization algorithm used in the process of finding the optimal mapping relationship, so it has strong versatility.

[0170] (4) The disclosed solution also specifically provides an outlier recognition algorithm, which calculates (x―μ) / σ based on the initial frequency difference set and compares it with the abnormal threshold to determine whether the difference in the initial frequency difference set is an abnormal difference. This method is simple and efficient. It should be noted that this solution does not limit the outlier recognition algorithm used, so it has strong versatility.

[0171] Here, it should be pointed out that, firstly, the application field of the scheme of the present disclosure is not limited to the field of quantum computing, and the reading cavity is only a kind of device commonly present in quantum devices; secondly, the scheme of the present disclosure is not limited to a specific outlier recognition algorithm, that is, all general outlier recognition algorithms are compatible with the scheme of the present disclosure; furthermore, the objective function of the scheme of the present disclosure can be obtained by the number of outlier differences corresponding to the mapping relationship, or by combining the number of outlier differences corresponding to the mapping relationship and the standard deviation of the difference after removing the outlier differences from the initial frequency difference set, without limiting the specific combination method, for example, the number of outlier differences can be used as the first parameter, and the standard deviation of the difference can be obtained by combining the number of outlier differences corresponding to the mapping relationship and the standard deviation of the difference after removing the outlier differences from the initial frequency difference set. as part of the objective function; when the number of abnormal differences is equal, the standard deviation is used as another part of the objective function for further judgment, or the sum of the number of abnormal differences and the standard deviation is used as the objective function; or after removing the abnormal differences, the standard deviations of other differences are normalized and dimensionless so that the standard deviation is in the interval [0,1]. At this time, the sum of the normalized dimensionless standard deviation and the number of abnormal differences can be used as the objective function, and so on; finally, the process of finding the optimal mapping relationship according to the objective function is not limited to specific algorithms, such as brute force search methods, optimization algorithms, and so on.

[0172] The disclosed solution also provides a device for determining the cavity frequency, such as Figure 8 As shown, including:

[0173] An acquisition unit 801 is used to acquire N cavity frequency experimental values, wherein the N cavity frequency experimental values ​​represent frequency experimental values ​​obtained after measuring the frequencies of N target reading cavities on the target reading line in the quantum chip layout;

[0174] The matching unit 802 is used to match the N cavity frequency experimental values ​​with the N design frequency values ​​to obtain a target mapping relationship between the N cavity frequency experimental values ​​and the N design frequency values; wherein the N design frequency values ​​represent the frequency values ​​that each target reading cavity in the N target reading cavities theoretically needs to achieve, which are determined in the design stage of the quantum chip layout.

[0175] In a specific example of the disclosed solution, the target mapping relationship can characterize a one-to-one correspondence between the cavity frequency experimental values ​​in the N cavity frequency experimental values ​​and the design frequency values ​​in the N design frequency values.

[0176] In a specific example of the disclosed solution, the matching unit is further used to obtain at least one abnormal experimental value among the N cavity frequency experimental values ​​corresponding to the target mapping relationship when there are abnormal values ​​among the N cavity frequency experimental values.

[0177] In a specific example of the disclosed solution, the matching unit 802 is specifically configured to:

[0178] Based on the N cavity frequency experimental values ​​and the N design frequency values, M initial mapping relationships are obtained; M is a positive integer greater than or equal to 2; an initial mapping relationship among the M initial mapping relationships can represent a corresponding relationship between the cavity frequency experimental value and the design frequency value;

[0179] Determine an initial frequency difference value set corresponding to each initial mapping relationship in the M initial mapping relationships; the initial frequency difference value set corresponding to the initial mapping relationship is obtained based on the difference between the frequency experimental value and the frequency design frequency value having a corresponding relationship in the initial mapping relationship;

[0180] The target mapping relationship is determined from the M initial mapping relationships based on the initial frequency difference value sets corresponding to the initial mapping relationships.

[0181] In a specific example of the disclosed solution, the matching unit 802 is specifically configured to:

[0182] Determine N1 first ordered sequences and N2 second ordered sequences, wherein the N1 first ordered sequences are obtained by sorting the N cavity frequency experimental values; the N2 second ordered sequences are obtained by sorting the N design frequency values; and the values ​​of N1 and N2 are related to N;

[0183] Based on the N1 first ordered sequences and the N2 second ordered sequences, a one-to-one correspondence between the cavity frequency experimental values ​​and the design frequency values ​​is established to obtain M different initial mapping relationships.

[0184] In a specific example of the disclosed solution, the matching unit 802 is specifically configured to:

[0185] Based on the initial frequency difference value set corresponding to the initial mapping relationship, an abnormal difference value in the initial frequency difference value set is identified;

[0186] Selecting at least one candidate mapping relationship from the M initial mapping relationships based on the number of abnormal difference values ​​in the initial frequency difference value set corresponding to the initial mapping relationship;

[0187] Based on at least one candidate mapping relationship, the target mapping relationship is determined.

[0188] In a specific example of the disclosed solution, the matching unit 802 is specifically configured to:

[0189] Selecting at least one initial mapping relationship with the least number of abnormal differences from the M initial mapping relationships;

[0190] The selected initial mapping relationship is used as a candidate mapping relationship.

[0191] In a specific example of the disclosed solution, the matching unit 802 is specifically configured to:

[0192] When the number of the selected candidate mapping relationship is one, the selected candidate mapping relationship is determined as the target mapping relationship.

[0193] In a specific example of the disclosed solution, the matching unit 802 is specifically configured to:

[0194] When two or more candidate mapping relationships are selected, removing abnormal differences from the initial frequency difference value set corresponding to the candidate mapping relationships;

[0195] Obtaining a target difference value corresponding to each candidate mapping relationship; wherein the target difference value corresponding to the candidate mapping relationship is obtained based on other differences in the initial frequency difference value set corresponding to the candidate mapping relationship, excluding abnormal differences;

[0196] The target mapping relationship is determined based on the target difference values ​​corresponding to each candidate mapping relationship.

[0197] In a specific example of the disclosed solution, the matching unit 802 is specifically configured to:

[0198] The candidate mapping relationship with the smallest target difference is selected as the target mapping relationship.

[0199] In a specific example of the disclosed solution, the matching unit 802 is specifically configured to:

[0200] Calculate the standard deviation of other frequency differences except abnormal differences in the initial frequency difference value set corresponding to the candidate mapping relationship; wherein the target difference value corresponding to the candidate mapping relationship is the calculated standard deviation;

[0201] or,

[0202] The standard deviation of the other differences except the abnormal differences in the initial frequency difference set corresponding to the candidate mapping relationship is calculated; based on the calculated standard deviation and the number of abnormal differences, the target difference corresponding to the candidate mapping relationship is obtained.

[0203] In a specific example of the disclosed solution, the acquisition unit 801 is specifically used to:

[0204] Acquire a target change curve; wherein the target change curve is used to characterize the mapping relationship between the frequency of the driving microwave input to the target read line and the IQ value output by the target read line;

[0205] Determining N target IQ values ​​with the smallest IQ values ​​from the target change curve;

[0206] Based on the N target IQ values, N cavity frequency experimental values ​​are obtained.

[0207] For the description of the specific functions and examples of each unit of the device in the embodiment of the present disclosure, reference can be made to the relevant description of the corresponding steps in the above method embodiment, which will not be repeated here.

[0208] The disclosed solution also provides a non-transitory computer-readable storage medium storing computer instructions, which, when executed by at least one quantum processing unit, causes the at least one quantum processing unit to execute the method of applying the quantum computing device above.

[0209] The disclosed solution also provides a computer program product, including a computer program, which, when executed by a processor, implements the method described above for application to a classical computing device;

[0210] Alternatively, the computer program, when executed by at least one quantum processing unit, implements the method described for application to a quantum computing device.

[0211] The disclosed solution also provides a quantum computing device, the quantum computing device comprising:

[0212] at least one quantum processing unit;

[0213] a memory coupled to the at least one QPU and configured to store executable instructions,

[0214] The instructions are executed by the at least one quantum processing unit to enable the at least one quantum processing unit to perform the method described for application to a quantum computing device.

[0215] It can be understood that the quantum processing unit (QPU) used in the present disclosure, which may also be called a quantum processor or a quantum chip, may involve a physical chip including a plurality of quantum bits interconnected in a specific manner.

[0216] Moreover, it is understood that the quantum bit described in the present disclosure may refer to the basic information unit of a quantum computing device. The quantum bit is contained in the QPU and generalizes the concept of the classical digital bit.

[0217] According to an embodiment of the present disclosure, the present disclosure also provides a computing device, a readable storage medium, and a computer program product.

[0218] Fig. 9 A schematic block diagram of an example computing device 900 that can be used to implement an embodiment of the present disclosure is shown. The computing device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The computing device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0219] like Fig. 9 As shown, the device 900 includes a computing unit 901, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 902 or a computer program loaded from a storage unit 908 into a random access memory (RAM) 903. In the RAM 903, various programs and data required for the operation of the device 900 can also be stored. The computing unit 901, the ROM 902, and the RAM 903 are connected to each other via a bus 904. An input / output (I / O) interface 905 is also connected to the bus 904.

[0220] A number of components in the device 900 are connected to the I / O interface 905, including: an input unit 906, such as a keyboard, a mouse, etc.; an output unit 907, such as various types of displays, speakers, etc.; a storage unit 908, such as a disk, an optical disk, etc.; and a communication unit 909, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 909 allows the device 900 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0221] The computing unit 901 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 901 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 901 performs the various methods and processes described above, such as a determination method for reading a cavity frequency. For example, in some embodiments, the determination method for reading a cavity frequency may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 908. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 900 via the ROM 902 and / or the communication unit 909. When the computer program is loaded into the RAM 903 and executed by the computing unit 901, one or more steps of the determination method for reading a cavity frequency described above may be performed. Alternatively, in other embodiments, the computing unit 901 may be configured to perform a determination method for reading a cavity frequency in any other appropriate manner (e.g., by means of firmware).

[0222] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0223] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0224] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0225] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0226] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0227] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0228] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0229] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the principles of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A method for determining a reading cavity frequency, comprising: Obtaining N cavity frequency experimental values, wherein the N cavity frequency experimental values ​​represent frequency experimental values ​​obtained after measuring the frequencies of N target reading cavities on the target reading line in the quantum chip layout; Matching N cavity frequency experimental values ​​with N design frequency values ​​to obtain a target mapping relationship between the N cavity frequency experimental values ​​and the N design frequency values; wherein the N design frequency values ​​represent the frequency values ​​that each target reading cavity in the N target reading cavities theoretically needs to achieve, which are determined in the design stage of the quantum chip layout; The matching of the N cavity frequency experimental values ​​with the N design frequency values ​​to obtain a target mapping relationship between the N cavity frequency experimental values ​​and the N design frequency values ​​includes: Based on the N cavity frequency experimental values ​​and the N design frequency values, M initial mapping relationships are obtained; M is a positive integer greater than or equal to 2; an initial mapping relationship among the M initial mapping relationships can represent a corresponding relationship between the cavity frequency experimental value and the design frequency value; Determine an initial frequency difference value set corresponding to each initial mapping relationship in the M initial mapping relationships; the initial frequency difference value set corresponding to the initial mapping relationship is obtained based on the difference between the frequency experimental value and the frequency design frequency value having a corresponding relationship in the initial mapping relationship; The target mapping relationship is determined from the M initial mapping relationships based on the initial frequency difference value sets corresponding to the initial mapping relationships.

2. The method according to claim 1, wherein: The target mapping relationship can represent a one-to-one correspondence between the cavity frequency experimental values ​​in the N cavity frequency experimental values ​​and the design frequency values ​​in the N design frequency values.

3. The method according to claim 1 or 2, further comprising: In the case where there are abnormal values ​​in the N cavity frequency experimental values, at least one abnormal experimental value in the N cavity frequency experimental values ​​corresponding to the target mapping relationship is obtained.

4. The method according to claim 1, wherein: The obtaining of M initial mapping relationships based on the N cavity frequency experimental values ​​and the N design frequency values ​​includes: Determine N1 first ordered sequences and N2 second ordered sequences, wherein the N1 first ordered sequences are obtained by sorting the N cavity frequency experimental values; the N2 second ordered sequences are obtained by sorting the N design frequency values; and the values ​​of N1 and N2 are related to N; Based on the N1 first ordered sequences and the N2 second ordered sequences, a one-to-one correspondence between the cavity frequency experimental values ​​and the design frequency values ​​is established to obtain M different initial mapping relationships.

5. The method according to claim 1, 2 or 4, wherein: The determining the target mapping relationship from the M initial mapping relationships based on the initial frequency difference value sets corresponding to the initial mapping relationships includes: Based on the initial frequency difference value set corresponding to the initial mapping relationship, an abnormal difference value in the initial frequency difference value set is identified; Selecting at least one candidate mapping relationship from the M initial mapping relationships based on the number of abnormal difference values ​​in the initial frequency difference value set corresponding to the initial mapping relationship; Based on at least one candidate mapping relationship, the target mapping relationship is determined.

6. The method according to claim 5, wherein: The selecting at least one candidate mapping relationship from the M initial mapping relationships based on the number of abnormal difference values ​​in the initial frequency difference value set corresponding to the initial mapping relationship includes: Selecting at least one initial mapping relationship with the least number of abnormal differences from the M initial mapping relationships; The selected initial mapping relationship is used as a candidate mapping relationship.

7. The method according to claim 5, wherein: The determining the target mapping relationship based on at least one candidate mapping relationship includes: When the number of the selected candidate mapping relationship is one, the selected candidate mapping relationship is determined as the target mapping relationship.

8. The method according to claim 5, wherein: The determining the target mapping relationship based on at least one candidate mapping relationship includes: When two or more candidate mapping relationships are selected, removing abnormal differences from the initial frequency difference value set corresponding to the candidate mapping relationships; Obtaining a target difference value corresponding to each candidate mapping relationship; wherein the target difference value corresponding to the candidate mapping relationship is obtained based on other differences in the initial frequency difference value set corresponding to the candidate mapping relationship, excluding abnormal differences; The target mapping relationship is determined based on the target difference values ​​corresponding to each candidate mapping relationship.

9. The method according to claim 8, wherein: The determining the target mapping relationship based on the target difference corresponding to each candidate mapping relationship includes: The candidate mapping relationship with the smallest target difference is selected as the target mapping relationship.

10. The method according to claim 9, wherein: The obtaining of the target difference corresponding to each candidate mapping relationship includes: Calculate the standard deviation of other frequency differences except abnormal differences in the initial frequency difference value set corresponding to the candidate mapping relationship; wherein the target difference value corresponding to the candidate mapping relationship is the calculated standard deviation; or, The standard deviation of the other differences except the abnormal differences in the initial frequency difference value set corresponding to the candidate mapping relationship is calculated; based on the calculated standard deviation and the number of abnormal differences, the target difference corresponding to the candidate mapping relationship is obtained.

11. The method according to claim 1, 2 or 4, wherein: The obtaining of N cavity frequency experimental values ​​comprises: Acquire a target change curve; wherein the target change curve is used to characterize the mapping relationship between the frequency of the driving microwave input to the target read line and the IQ value output by the target read line; Determining N target IQ values ​​with the smallest IQ values ​​from the target change curve; Based on the N target IQ values, N cavity frequency experimental values ​​are obtained.

12. A device for determining a reading cavity frequency, comprising: An acquisition unit, used to acquire N cavity frequency experimental values, wherein the N cavity frequency experimental values ​​represent frequency experimental values ​​obtained after measuring the frequencies of N target reading cavities on the target reading line in the quantum chip layout; A matching unit, used to match N cavity frequency experimental values ​​with N design frequency values ​​to obtain a target mapping relationship between the N cavity frequency experimental values ​​and the N design frequency values; wherein the N design frequency values ​​represent the frequency values ​​that each target reading cavity in the N target reading cavities theoretically needs to achieve, which are determined in the design stage of the quantum chip layout; Wherein, the matching unit is specifically used for: Based on the N cavity frequency experimental values ​​and the N design frequency values, M initial mapping relationships are obtained; M is a positive integer greater than or equal to 2; an initial mapping relationship among the M initial mapping relationships can represent a corresponding relationship between the cavity frequency experimental value and the design frequency value; Determine an initial frequency difference value set corresponding to each initial mapping relationship in the M initial mapping relationships; the initial frequency difference value set corresponding to the initial mapping relationship is obtained based on the difference between the frequency experimental value and the frequency design frequency value having a corresponding relationship in the initial mapping relationship; The target mapping relationship is determined from the M initial mapping relationships based on the initial frequency difference value sets corresponding to the initial mapping relationships.

13. The device according to claim 12, wherein: The target mapping relationship can represent a one-to-one correspondence between the cavity frequency experimental values ​​in the N cavity frequency experimental values ​​and the design frequency values ​​in the N design frequency values.

14. The device according to claim 12 or 13, wherein: The matching unit is further configured to obtain, when there is an abnormal value in the N cavity frequency experimental values, at least one abnormal experimental value among the N cavity frequency experimental values ​​corresponding to the target mapping relationship.

15. The device according to claim 12, wherein: The matching unit is specifically used for: Determine N1 first ordered sequences and N2 second ordered sequences, wherein the N1 first ordered sequences are obtained by sorting the N cavity frequency experimental values; the N2 second ordered sequences are obtained by sorting the N design frequency values; and the values ​​of N1 and N2 are related to N; Based on the N1 first ordered sequences and the N2 second ordered sequences, a one-to-one correspondence between the cavity frequency experimental values ​​and the design frequency values ​​is established to obtain M different initial mapping relationships.

16. The device according to claim 12, 13 or 15, wherein: The matching unit is specifically used for: Based on the initial frequency difference value set corresponding to the initial mapping relationship, an abnormal difference value in the initial frequency difference value set is identified; Selecting at least one candidate mapping relationship from the M initial mapping relationships based on the number of abnormal difference values ​​in the initial frequency difference value set corresponding to the initial mapping relationship; Based on at least one candidate mapping relationship, the target mapping relationship is determined.

17. The device according to claim 16, wherein: The matching unit is specifically used for: Selecting at least one initial mapping relationship with the least number of abnormal differences from the M initial mapping relationships; The selected initial mapping relationship is used as a candidate mapping relationship.

18. The device according to claim 16, wherein: The matching unit is specifically used for: When the number of the selected candidate mapping relationship is one, the selected candidate mapping relationship is determined as the target mapping relationship.

19. The device according to claim 16, wherein: The matching unit is specifically used for: When two or more candidate mapping relationships are selected, removing abnormal differences from the initial frequency difference value set corresponding to the candidate mapping relationships; Obtaining a target difference value corresponding to each candidate mapping relationship; wherein the target difference value corresponding to the candidate mapping relationship is obtained based on other differences in the initial frequency difference value set corresponding to the candidate mapping relationship, excluding abnormal differences; The target mapping relationship is determined based on the target difference values ​​corresponding to each candidate mapping relationship.

20. The device according to claim 19, wherein The matching unit is specifically used for: The candidate mapping relationship with the smallest target difference is selected as the target mapping relationship.

21. The device according to claim 20, wherein: The matching unit is specifically used for: Calculate the standard deviation of other frequency differences except abnormal differences in the initial frequency difference value set corresponding to the candidate mapping relationship; wherein the target difference value corresponding to the candidate mapping relationship is the calculated standard deviation; or, The standard deviation of the other differences except the abnormal differences in the initial frequency difference value set corresponding to the candidate mapping relationship is calculated; based on the calculated standard deviation and the number of abnormal differences, the target difference corresponding to the candidate mapping relationship is obtained.

22. The device according to claim 12, 13 or 15, wherein: The acquisition unit is specifically used for: Acquire a target change curve; wherein the target change curve is used to characterize the mapping relationship between the frequency of the driving microwave input to the target read line and the IQ value output by the target read line; Determining N target IQ values ​​with the smallest IQ values ​​from the target change curve; Based on the N target IQ values, N cavity frequency experimental values ​​are obtained.

23. A computing device comprising: At least one quantum processing unit QPU; a memory coupled to the at least one QPU and configured to store executable instructions, The instructions are executed by the at least one QPU so that the at least one QPU can perform the method of any one of claims 1-11; Or, include: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 11.

24. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: When executed by at least one quantum processing unit, the computer instructions cause the at least one quantum processing unit to perform the method according to any one of claims 1 to 11; Alternatively, the computer instructions are used to cause the computer to execute the method according to any one of claims 1-11.

25. A computer program product comprising a computer program which, when executed by at least one quantum processing unit, implements the method according to any one of claims 1 to 11; Or the computer program implements the method according to any one of claims 1 to 11 when executed by a processor.

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