Quantum measurement and control parameter processing method and device, equipment and storage medium

Abstract

The present disclosure provides a quantum control parameter processing method and device, equipment and a storage medium thereof, relates to the field of data processing, and in particular to the technical field of quantum computing, superconducting quantum computing, quantum control and the like. The specific implementation scheme is as follows: a first initial mapping relationship B2F is determined, wherein the first initial mapping relationship B2F is obtained by fitting processing of a binary sampling group contained in a target set, and can represent a mapping relationship from a bias value of a quantum bit to a bit frequency of the quantum bit; in a case where the first initial mapping relationship B2F contains a preset bias value, and a target set used for fitting reaches a sampling termination condition, a target mapping relationship B2F is fitted based on each binary sampling group contained in the target set * ; wherein the target mapping relationship B2F * is used to represent a mapping relationship between a bias value of a quantum bit and a bit frequency of the quantum bit.

Description

Technical Field

[0001] This disclosure relates to the field of data processing technology, and in particular to the fields of quantum computing, superconducting quantum computing, and quantum control. Background Technology

[0002] The construction of superconducting quantum computers relies on complex experimental equipment and intricate measurement and control parameters. Furthermore, ensuring the efficient and stable operation of a quantum computer requires the gradual calibration of these parameters through a complex process. With advancements in chip manufacturing technology, the number of qubits that can be integrated onto a single quantum chip is increasing dramatically, posing a significant challenge to the calibration of these parameters. Traditional manual calibration methods are no longer sufficient to meet the time and efficiency requirements. Summary of the Invention

[0003] This disclosure provides a method, apparatus, device, and storage medium for processing quantum measurement and control parameters.

[0004] According to one aspect of this disclosure, a method for processing quantum measurement and control parameters is provided, comprising:

[0005] A first initial mapping relationship B2F is determined, wherein the first initial mapping relationship B2F is obtained by fitting the binary sampling group contained in the target set, and can characterize the mapping relationship from the bias value of the qubit to the bit frequency of the qubit; the binary sampling group contains the sampled bias value and the sampled bit frequency estimated based on the sampled bias value.

[0006] Given that the first initial mapping relationship B2F contains a preset bias value, and that the target set used for fitting reaches the sampling termination condition, the target mapping relationship B2F from the bias value to the bit frequency is fitted based on each binary sampling group contained in the target set. * Wherein, the target mapping relationship B2F * This is used to characterize the mapping relationship between the bias value of a qubit and the bit frequency of the qubit, based on the target mapping relationship B2F. * The accuracy of the determined bias value is greater than the accuracy of the bias value determined based on the first initial mapping relationship B2F.

[0007] According to another aspect of this disclosure, a processing apparatus for quantum measurement and control parameters is provided, comprising:

[0008] An initial mapping relationship processing unit is used to determine a first initial mapping relationship B2F, wherein the first initial mapping relationship B2F is obtained by fitting the binary sampling group contained in the target set, and can characterize the mapping relationship between the bias value of the qubit and the bit frequency of the qubit; the binary sampling group includes the sampled bias value and the sampled bit frequency estimated based on the sampled bias value.

[0009] The target mapping relationship processing unit is used to, under the condition that the first initial mapping relationship B2F contains a preset bias value, and under the condition that the target set used for fitting reaches the sampling termination condition, fit the target mapping relationship B2F from the bias value to the bit frequency based on each binary sampling group contained in the target set. * Wherein, the target mapping relationship B2F * This is used to characterize the mapping relationship between the bias value of a qubit and the bit frequency of the qubit, based on the target mapping relationship B2F. * The accuracy of the determined bias value is greater than the accuracy of the bias value determined based on the first initial mapping relationship B2F.

[0010] According to another aspect of this disclosure, a computing device is provided, comprising:

[0011] At least one quantum processing unit (QPU);

[0012] A memory, coupled to the at least one QPU and used to store executable instructions,

[0013] The instruction is executed by the at least one QPU to enable the at least one QPU to perform the method described above;

[0014] Or, including:

[0015] At least one processor; and

[0016] A memory communicatively connected to the at least one processor; wherein,

[0017] The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the method described above.

[0018] According to another aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions that, when executed by at least one quantum processing unit, cause the at least one quantum processing unit to perform the method described above.

[0019] Alternatively, the computer instructions may be used to cause the computer to perform the methods described above.

[0020] According to another aspect of this disclosure, a computer program product is provided, comprising a computer program that, when executed by at least one quantum processing unit, implements the methods described above.

[0021] Alternatively, the computer program may implement the above-described method when executed by a processor.

[0022] Thus, the disclosed scheme can efficiently obtain the target mapping relationship B2F from the bias value to the bit frequency. * Furthermore, this process requires no human intervention and can be automated. Compared to existing methods of manually calibrating the bias value (or frequency) of quantum bits, the present invention can significantly improve processing efficiency and accuracy. Moreover, the present invention is applicable to any quantum chip, thus it has strong applicability and practicality, providing support for the precise control of quantum bits in quantum chips.

[0023] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0024] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein:

[0025] Figure 1 This is a schematic diagram of the implementation flow of the quantum measurement and control parameter processing method according to the embodiments of this disclosure. Figure 1 ;

[0026] Figure 2 This is a schematic diagram of the implementation flow of the quantum measurement and control parameter processing method according to the embodiments of this disclosure. Figure 2 ;

[0027] Figure 3 This is a schematic diagram of the implementation flow of the quantum measurement and control parameter processing method according to the embodiments of this disclosure. Figure 3 ;

[0028] Figure 4 This is a schematic diagram of the implementation flow of the quantum measurement and control parameter processing method according to the embodiments of this disclosure. Figure 4 ;

[0029] Figure 5 This is a schematic diagram of the implementation process of the quantum measurement and control parameter processing method according to the embodiments of this disclosure in a specific example;

[0030] Figure 6(a) is a schematic diagram of a pulse sequence in a specific example of the method for processing quantum measurement and control parameters according to an embodiment of the present disclosure;

[0031] Figure 6(b) is a schematic diagram of the maximum bit frequency and target mapping relationship obtained by the quantum measurement and control parameter processing method according to the embodiments of the present disclosure;

[0032] Figure 6(c) is a schematic diagram of the maximum bit frequency and the mapping relationship between bit frequency and offset value obtained by the traditional scheme;

[0033] Figure 7 This is a schematic diagram of the structure of a quantum measurement and control parameter processing device according to an embodiment of the present disclosure;

[0034] Figure 8 This is a block diagram of a computing device used to implement the quantum measurement and control parameter processing method of the embodiments of this disclosure. Detailed Implementation

[0035] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0036] Quantum computing is a computational model that follows quantum mechanics and manipulates quantum information units to perform calculations. Compared to traditional computers, quantum computing outperforms conventional general-purpose computers in solving certain problems. Among them, superconducting quantum computers, with their advantages of ease of control and good scalability, have become one of the mainstream quantum computing implementation schemes in the industry.

[0037] The construction of superconducting quantum computers relies on complex experimental equipment and intricate measurement and control parameters. Furthermore, ensuring the efficient and stable operation of these parameters requires a complex process of gradual calibration. With advancements in chip manufacturing technology, the number of qubits that can be integrated onto a single quantum chip is increasing, posing a significant challenge to the calibration of these parameters. Traditional manual calibration methods are no longer sufficient to meet the time and efficiency requirements.

[0038] In one scenario, during a superconducting quantum computing experiment, the frequency of a qubit can be controlled by the Z-bias. To facilitate this control, the mapping relationship between the qubit frequency and the Z-bias can be known in advance; that is, it is desirable to know how much Z-bias needs to be added to modulate the qubit to a specific target frequency.

[0039] Based on this, the present invention proposes an experimental implementation scheme that can quickly determine the target mapping relationship between bit frequency and Z offset. This scheme is fast and highly accurate. At the same time, the determined target mapping relationship can also characterize: the maximum bit frequency and its corresponding offset value, and the zero offset and its corresponding bit frequency.

[0040] Specifically, Figure 1 This is a schematic diagram of the implementation flow of the quantum measurement and control parameter processing method according to the embodiments of this disclosure. Figure 1 This method can be optionally applied to quantum computing devices that also have classical computing capabilities, or it can be applied to classical computing devices that also have quantum computing capabilities, or it can be directly applied to classical computing devices, such as personal computers, servers, server clusters and other electronic devices with classical computing capabilities, or it can be directly applied to quantum computers. This disclosure does not impose any restrictions on this method.

[0041] Furthermore, the method includes at least a portion of the following: (e.g.) Figure 1 As shown, it includes:

[0042] Step S101: Determine the first initial mapping relationship B2F (Bias to Frequency).

[0043] Here, the first initial mapping relationship B2F is obtained by fitting the binary sampling group contained in the target set, and can characterize the mapping relationship between the bias value of the qubit and the bit frequency of the qubit; the binary sampling group contains the sampled bias value and the sampled bit frequency estimated based on the sampled bias value.

[0044] In a specific example, the first initial mapping relationship B2F can be represented as: B2F: A Z →F q Among them, A Z F represents the bias value, and more specifically, the Z bias value; q Indicates bit frequency.

[0045] Step S102: Given that the first initial mapping relationship B2F contains a preset bias value, and the target set used for fitting reaches the sampling termination condition, based on each binary sampling group contained in the target set, fit the target mapping relationship B2F from the bias value to the bit frequency. * .

[0046] Here, the target mapping relationship B2F * This is used to characterize the mapping relationship between the bias value of a qubit and the bit frequency of the qubit, based on the target mapping relationship B2F. *The accuracy of the determined bias value is greater than the accuracy of the bias value determined based on the first initial mapping relationship B2F.

[0047] Thus, the disclosed scheme can efficiently obtain the target mapping relationship B2F from the bias value to the bit frequency. * Furthermore, this process requires no human intervention and can be automated. Compared to existing methods of manually calibrating the bias value (or frequency) of quantum bits, the present invention can significantly improve processing efficiency and accuracy. Moreover, the present invention is applicable to any quantum chip, thus it has strong applicability and practicality, providing support for the precise control of quantum bits in quantum chips.

[0048] In one example, the preset bias value can be specifically the zero bias of the qubit. Here, zero bias refers to the bias value of the smallest magnetic flux value among the multiple bias values ​​corresponding to the maximum bit frequency.

[0049] Furthermore, in one example, the scheme of this disclosure is based on the fact that the first initial mapping relationship B2F fitted to the target set contains a zero bias (also referred to as a zero bias value). Therefore, on the one hand, it can ensure that the target mapping relationship B2F is zero. * It contains a high-precision zero bias, and on the other hand, it can effectively improve the accuracy of the first initial mapping relationship B2F, thereby further improving the target mapping relationship B2F. * The accuracy.

[0050] Furthermore, in a specific example, the target mapping relationship B2F * This can be expressed by the following target mapping function:

[0051]

[0052] Here, A z Indicates the bias value, B2F * (A) z The value of ) represents the bias value A. z The corresponding bit frequency; k * , l * m * n * e * f * g * h * This represents the fitted parameter values ​​obtained after the fitting process.

[0053] Thus, this disclosed solution provides a specific target mapping relationship B2F. * The expression is simple and highly interpretable; moreover, it utilizes the target mapping relationship B2F described in this disclosure. *This can significantly improve fitting accuracy and prediction accuracy, thereby providing further support for precise control of qubits.

[0054] Furthermore, in a specific example, the target mapping relationship B2F * It can also be specifically expressed as: B2F * : Based on this, in a specific example, after obtaining the target mapping relationship B2F described above... * Then, the target mapping relationship B2F can also be used. * To obtain the bias value corresponding to any specified bit frequency, specifically, the method further includes:

[0055] Obtain the target bit frequency F q-target ;

[0056] Based on the target mapping relationship B2F * The target bit frequency F is obtained. q-target The corresponding target bias value

[0057] Here, the target bias value This refers to the high-precision bias value obtained using the scheme disclosed herein. At this point, the target bias value is used... When qubits are controlled, it is possible to make the frequency of the qubit equal to the target bit frequency F. q-target .

[0058] Thus, this disclosed scheme can utilize the target mapping relationship B2F * This allows you to obtain any specified bit frequency, such as the target bit frequency F. a-target The corresponding target bias value is used to control the qubits with high precision, providing effective support for the efficient and stable operation of quantum computers.

[0059] Specifically, Figure 2 This is a schematic diagram of the implementation flow of the quantum measurement and control parameter processing method according to the embodiments of this disclosure. Figure 2 This method can be optionally applied to quantum computing devices that also possess classical computing capabilities, or it can be applied to classical computing devices that also possess quantum computing capabilities, or it can be directly applied to classical computing devices, such as personal computers, servers, server clusters, and other electronic devices with classical computing capabilities, or it can be directly applied to quantum computers. This disclosure does not impose any limitations on these applications. It is understood that the above... Figure 1 The methods shown can also be applied to this example, and the related content will not be elaborated further in this example.

[0060] Furthermore, the method includes at least a portion of the following: (e.g.) Figure 2As shown, it includes:

[0061] Step S201: Based on the initial sampling process, obtain the target set containing N binary sampling groups.

[0062] Step S202: Based on each binary sampling group contained in the target set, fit the first initial mapping relationship B2F.

[0063] Here, N is a positive integer greater than or equal to 2. Furthermore, in practical applications, the value of N can be set based on precision requirements, and this disclosure does not impose any restrictions on this.

[0064] Furthermore, in a specific example, the first initial mapping relationship B2F is expressed by the following first initial mapping function:

[0065] B2F(A z ) = a * cos(b * A z +c * )+d * ;

[0066] Among them, A z Indicates the bias value, B2F(A) z The value of ) represents the bias value A. z The corresponding bit frequency; a * b * c * d * This represents the fitted parameter values ​​obtained after the fitting process.

[0067] Thus, this disclosure provides a specific expression for a first initial mapping relationship, which is simple, highly interpretable, and easy to fit quickly, thereby laying the foundation for improving the overall processing efficiency. Furthermore, this disclosure can utilize this first initial mapping relationship (B2F) to determine the next sampling strategy, thus providing strong support for balancing accuracy and efficiency requirements through different sampling strategies.

[0068] Step S203: Given that the first initial mapping relationship B2F contains a preset bias value, and the target set used for fitting reaches the sampling termination condition, based on each binary sampling group contained in the target set, fit the target mapping relationship B2F from the bias value to the bit frequency. * Wherein, the target mapping relationship B2F * This is used to characterize the mapping relationship between the bias value of a qubit and the bit frequency of the qubit, based on the target mapping relationship B2F. *The accuracy of the determined bias value is greater than the accuracy of the bias value determined based on the first initial mapping relationship B2F.

[0069] In other words, this disclosed solution can pre-sample multiple binary sampling groups, and based on these sampling groups, an initial first initial mapping relationship B2F is fitted. The next sampling strategy is then determined using this first initial mapping relationship B2F, for example, by determining whether the first initial mapping relationship B2F contains a preset bias value. This allows for balancing accuracy and efficiency requirements through different sampling strategies. Furthermore, this disclosed solution is applicable to any quantum chip, thus possessing strong applicability and practicality, providing support for the precise control of qubits in quantum chips.

[0070] Specifically, Figure 3 This is a schematic diagram of the implementation flow of the quantum measurement and control parameter processing method according to the embodiments of this disclosure. Figure 3 This method can be optionally applied to quantum computing devices that also possess classical computing capabilities, or it can be applied to classical computing devices that also possess quantum computing capabilities, or it can be directly applied to classical computing devices, such as personal computers, servers, server clusters, and other electronic devices with classical computing capabilities, or it can be directly applied to quantum computers. This disclosure does not impose any limitations on these applications. It is understood that the above... Figure 1 and Figure 2 The methods shown can also be applied to this example, and the related content will not be elaborated further in this example.

[0071] Furthermore, the method includes at least a portion of the following: (e.g.) Figure 3 As shown, it includes:

[0072] Step S301: Based on the initial sampling process, obtain a target set containing N binary sampling groups. Here, N is an integer greater than or equal to 2.

[0073] Step S302: Based on each binary sampling group contained in the target set, fit the first initial mapping relationship B2F.

[0074] In other words, in this disclosed scheme, multiple binary sampling groups can be pre-sampled, and an initial first initial mapping relationship B2F can be fitted based on the sampled binary sampling groups. Then, the next sampling strategy can be determined through the first initial mapping relationship B2F, for example, the next sampling strategy can be determined based on whether the first initial mapping relationship B2F contains a preset bias value.

[0075] Step S303: Determine whether the first initial mapping relationship B2F contains a preset bias value; if it is determined that it does not contain a preset bias value, proceed to step S304; otherwise, if it is determined that it contains a preset bias value, proceed to step S307.

[0076] Step S304: If the first initial mapping relationship B2F does not contain a preset bias value, execute the first type of sampling process to obtain a new set of binary sampling groups. Execute step S305.

[0077] Here, the new binary sample set obtained from the first type of sampling procedure includes: the sampling bias value A obtained from the sampling. z (next1) and based on the sampling bias value A z (next1) The obtained sampling bit frequency F q (next1) .

[0078] It should be noted that this first type of sampling process can be understood as small-range sampling, which effectively improves the accuracy of the fitting.

[0079] Furthermore, in a specific example, the method for determining whether the first initial mapping relationship B2F contains a preset bias value can be as follows:

[0080] Determine the fitted parameter value c * (For example, the fitting parameter value c in the first initial mapping relationship B2F mentioned above) * Whether it is within the bias interval. The bias interval is obtained based on the maximum and minimum bias values ​​in the target set, and the fitting parameter value c... * The preset bias value estimated by the first initial mapping relationship B2F;

[0081] Based on the judgment result, determine whether the first initial mapping relationship B2F contains a preset bias value.

[0082] For example, in one example, if the fitted parameter value c * If the initial mapping relationship B2F falls within the bias interval determined based on the target set, then it can be considered that the first initial mapping relationship contains a preset bias value. Otherwise, if the fitted parameter value c * If the first initial mapping relationship B2F is not within the bias interval determined based on the target set, it can be considered that it does not contain a preset bias value.

[0083] Thus, by fitting the parameter value c *Whether the bias interval falls within the current target set is used to determine whether the accuracy of the first initial mapping relationship B2F obtained by fitting meets the standard, thereby providing strong support for further improving the fitting accuracy and the accuracy of zero bias.

[0084] Furthermore, in a specific example, the execution of the first type of sampling process described above to obtain a new set of binary samples (e.g., step S304 described above) includes:

[0085] Step S304-1a: Fit the parameter value c * If the sampled bias value is less than the minimum bias value within the bias interval, the sampled bias value A is obtained based on the minimum bias value within the bias interval. z (next1) .

[0086] For example, in one instance, the sampling bias value A is obtained based on the minimum bias value within the bias interval, as described above. z (next1) ,include:

[0087] Subtract the first preset value from the minimum bias value within the bias interval to obtain the sampled bias value A. z (next1) The first preset value is obtained based on the modulation period of the quantum bit.

[0088] It is understood that the first preset value can be set based on specific accuracy requirements, such as 0.01, 0.02 or 0.03 times the modulation period, etc., and this disclosure does not limit it.

[0089] In other words, in this example, the position of the zero bias estimated based on the first initial mapping relationship B2F is the fitted parameter value c. * Since the value is to the left of the bias interval [minimum bias value, maximum bias value], the difference between the minimum bias value and the first preset value (such as 0.01 times the modulation period) can be used as the next sampling point to obtain the next sampling bias value, i.e., A. z (next1) Thus, a specific sampling scheme is provided, which is simple and effectively reduces unnecessary scanning and sampling, greatly reducing the number of sampling points and thus significantly improving processing efficiency.

[0090] Step S304-2a: Fit the sampling bias value A z (next1) The corresponding sampling bit frequency F q (next1) This is to obtain a new set of binary sampling groups.

[0091] Furthermore, in another specific example, the execution of the first type of sampling procedure described above to obtain a new set of binary samples (e.g., step S304 described above) includes:

[0092] Step S304-1b: Fit the parameter value c * If the bias value is greater than the maximum bias value within the bias interval, the sampled bias value A is obtained based on the maximum bias value within the bias interval. z (next1) .

[0093] For example, in one instance, the sampling bias value A is obtained based on the maximum bias value within the bias interval, as described above. z (next1) Specifically, it includes:

[0094] Add the maximum bias value within the bias interval to the second preset value to obtain the sampling bias value A. z (next1) The second preset value is obtained based on the modulation period of the quantum bit.

[0095] It is understood that the second preset value can also be set based on specific accuracy requirements, such as 0.01, 0.02 or 0.03 times the modulation period, etc., and this disclosure does not limit it.

[0096] Furthermore, the first preset value and the second preset value can be the same, for example, both can be 0.01, or they can be different. This disclosure does not impose any specific restrictions on this.

[0097] In other words, in this example, the position of the zero bias estimated based on the first initial mapping relationship B2F is the fitted parameter value c. * Since the value is to the right of the bias interval [minimum bias value, maximum bias value], the sum of the maximum bias value and the second preset value (such as 0.01 times the modulation period) can be used as the next sampling point to obtain the next sampling bias value, i.e., A. z (next1) Thus, a specific sampling scheme is provided, which is simple and effectively reduces unnecessary scanning and sampling, greatly reducing the number of sampling points and thus significantly improving processing efficiency.

[0098] It should be noted that the maximum bias value mentioned above can be specifically the maximum value among all bias values ​​in the current target set; correspondingly, the minimum bias value mentioned above can be specifically the minimum value among all bias values ​​in the current target set.

[0099] Step S304-2b: Fit the sampling bias value A z (next1) The corresponding sampling bit frequency Fq (next1) This is to obtain a new set of binary sampling groups.

[0100] Step S305: Update the target set. Proceed to step S306.

[0101] Step S306: Based on the updated target set, update the first initial mapping relationship B2F and return to step S303 to determine whether the updated first initial mapping relationship B2F contains a preset bias value.

[0102] Step S307: Given that the first initial mapping relationship B2F contains a preset bias value, and the target set used for fitting (i.e., the current target set) reaches the sampling termination condition, the target mapping relationship B2F from the bias value to the bit frequency is fitted based on each binary sampling group contained in the target set. * .

[0103] Here, the target mapping relationship B2F * This is used to characterize the mapping relationship between the bias value of a qubit and the bit frequency of the qubit, based on the target mapping relationship B2F. * The accuracy of the determined bias value is greater than the accuracy of the bias value determined based on the first initial mapping relationship B2F.

[0104] Thus, this disclosure provides a specific sampling strategy: when the first initial mapping relationship B2F does not contain a preset bias value, a first type of sampling procedure is executed. This first type of sampling procedure balances accuracy requirements to improve the fitting accuracy of the first initial mapping relationship B2F, thereby improving the target mapping relationship B2F. * The prediction accuracy is high. Moreover, the sampling scheme is simple, effectively reducing unnecessary scanning and sampling, greatly reducing the number of sampling points, and thus significantly improving processing efficiency.

[0105] Specifically, Figure 4 This is a schematic diagram of the implementation flow of the quantum measurement and control parameter processing method according to the embodiments of this disclosure. Figure 4 This method can be optionally applied to quantum computing devices that also possess classical computing capabilities, or it can be applied to classical computing devices that also possess quantum computing capabilities, or it can be directly applied to classical computing devices, such as personal computers, servers, server clusters, and other electronic devices with classical computing capabilities, or it can be directly applied to quantum computers. This disclosure does not impose any limitations on these applications. It is understood that the above... Figure 1 , Figure 2 and Figure 3 The methods shown can also be applied to this example, and the related content will not be elaborated further in this example.

[0106] Furthermore, the method includes at least a portion of the following: (e.g.) Figure 4 As shown, it includes:

[0107] Step S401: Based on the initial sampling process, obtain a target set containing N binary sampling groups. Here, N is an integer greater than or equal to 2.

[0108] Step S402: Based on each binary sampling group contained in the target set, fit the first initial mapping relationship B2F.

[0109] Step S403: Determine whether the first initial mapping relationship B2F contains a preset bias value; if it is determined that it does not contain a preset bias value, proceed to step S404; otherwise, if it is determined that it contains a preset bias value, proceed to step S407.

[0110] Step S404: If the first initial mapping relationship B2F does not contain a preset bias value, execute the first type of sampling process to obtain a new set of binary sampling groups. Execute step S405.

[0111] For details regarding the first type of sampling process, please refer to the description above; it will not be repeated here.

[0112] Step S405: Update the target set.

[0113] Step S406: Based on the updated target set, update the first initial mapping relationship B2F and return to step S403 to determine whether the updated first initial mapping relationship B2F contains a preset bias value.

[0114] Step S407: Determine whether the target set used for fitting (i.e., the current target set) has reached the sampling termination condition. If the sampling termination condition has been reached, proceed to step S411; otherwise, if the sampling termination condition has not been reached, proceed to step S408.

[0115] For example, in a specific instance, the sampling termination condition includes one of the following:

[0116] The number of binary sample groups contained in the target set exceeds the number threshold;

[0117] The difference between the maximum and minimum bit frequencies in the target set exceeds the frequency threshold.

[0118] Thus, this disclosed solution provides a detailed scheme for specific sampling termination conditions, thereby supporting automation and laying the foundation for improving processing efficiency while ensuring accuracy.

[0119] Step S408: If it is determined that the first initial mapping relationship B2F contains a preset bias value, but the target set used for fitting has not reached the sampling termination condition, execute the second type of sampling process to obtain a new set of binary sampling groups. Execute step S409.

[0120] Here, the second type of sampling process differs from the first type. It should be noted that this second type of sampling process can be understood as large-scale sampling, thereby effectively improving processing efficiency.

[0121] Furthermore, the new binary sample group obtained from the second type of sampling procedure includes: the sampled bit frequency F. q (next2) and based on the sampling bit frequency F q (next2) The obtained sampling bias value A z (next2) .

[0122] It should be noted that the "1" in next1 and the "2" in next2 are only used to distinguish the sampling results obtained from different sampling processes and have no real meaning.

[0123] Furthermore, in a specific example, the execution of the second type of sampling process described above to obtain a new set of binary samples (step S408) specifically includes:

[0124] Step S408-1: Determine the sampling bit frequency F based on the sampling bit frequency contained in the target set. q (next2) .

[0125] For example, in one instance, the sampling bit frequency F can be obtained based on the sampling bit frequency in the target set. q (next2) For example, F q (next2) =F q (i) -Specified value, which can be set based on actual accuracy requirements, and this disclosure does not impose any restrictions on it.

[0126] Here, F q (i) It can be one of the sampling bit frequencies contained in the target set, or the sampling bit frequency obtained from the previous sampling.

[0127] Step S408-2: Based on the second initial mapping relationship F2B, obtain the sampling bit frequency F. q (next2) The corresponding sampling bias value A z (next2) This is to obtain a new set of binary sampling groups.

[0128] Here, the second initial mapping relationship F2B is obtained based on the first initial mapping relationship and can characterize the mapping relationship between the bit frequency of the qubit and the bias value of the qubit.

[0129] Furthermore, in one example, the second initial mapping relationship F2B can be obtained in the following manner:

[0130] The fitted parameter value c in the first initial mapping relationship B2F * When the value is less than the first value (e.g., 0), the expression for the second initial mapping function is: F2B(F q ) = arccos((F q -d * ) / a * -c * ) / b * ;

[0131] or,

[0132] Fitting parameter value c * If the value is greater than or equal to the first value (e.g., 0), the expression for the second initial mapping function is: F2B(F q )=-arccos((F q -d * ) / a * -c * ) / b * ;

[0133] Among them, F q F2B represents the bit frequency. q The value of ) represents the bit frequency F. q The corresponding bias value; a * b * c * d * This represents the fitting parameter value obtained after fitting processing; the fitting parameter value c * The preset bias value estimated by the first initial mapping relationship B2F is used to characterize the first initial mapping relationship.

[0134] Thus, this disclosed solution provides a specific sampling strategy: when the first initial mapping relationship B2F contains a preset bias value, but the target set used for fitting has not reached the sampling termination condition, a second type of sampling process is executed, thereby improving processing efficiency while maintaining accuracy requirements. Furthermore, this solution is simple and efficient, thus providing support for further improving overall processing efficiency.

[0135] Step S409: Update the target set; proceed to step S410.

[0136] Step S410: Based on the updated target set, update the first initial mapping relationship B2F, and return to step S403 to determine whether the updated first initial mapping relationship B2F contains a preset bias value.

[0137] Step S411: Given that the first initial mapping relationship B2F contains a preset bias value, and the target set used for fitting reaches the sampling termination condition, based on each binary sampling group contained in the target set, fit the target mapping relationship B2F from the bias value to the bit frequency. * .

[0138] Here, the target mapping relationship B2F * This is used to characterize the mapping relationship between the bias value of a qubit and the bit frequency of the qubit, based on the target mapping relationship B2F. * The accuracy of the determined bias value is greater than the accuracy of the bias value determined based on the first initial mapping relationship B2F.

[0139] Thus, this disclosed solution provides a specific sampling strategy: when the first initial mapping relationship B2F contains a preset bias value, but the target set used for fitting has not reached the sampling termination condition, a second type of sampling process is executed. This second type of sampling process balances efficiency requirements, thereby effectively improving the fitting accuracy of the first initial mapping relationship B2F, and consequently improving the target mapping relationship B2F. * Based on the accuracy of predictions, we can improve the overall processing efficiency.

[0140] In addition, this sampling scheme is simple and effectively reduces unnecessary scanning and sampling, greatly reducing the number of sampling points, which can further improve processing efficiency significantly.

[0141] The following detailed description, with specific examples, further illustrates the present invention. Specifically, the present invention proposes an experimental implementation scheme that can quickly determine the mapping relationship between bit frequency and Z-bias (corresponding to the bias value described above). The present invention can simultaneously, quickly, and with high precision determine:

[0142] The target mapping relationship between bit frequency and Z-bias value;

[0143] The maximum bit frequency and its corresponding Z-bias value;

[0144] Zero bias and its corresponding bit frequency.

[0145] Specifically, this publicly disclosed plan mainly includes two stages:

[0146] The first stage is the calibration stage.

[0147] The specific process for this calibration phase is as follows:

[0148] First, the sampled data obtained from the experiment (e.g., the sampling bias value and the sampling bit frequency corresponding to the sampling bias value) is fitted to obtain the bit frequency f. q With Z bias value A Z The initial mapping relationship (corresponding to the first initial mapping relationship), for example, can be expressed by a function, which can be denoted as the first initial mapping function B2F: A Z →F q .

[0149] Secondly, multiple rounds of small-range sampling are performed. For example, new Z-bias values ​​are obtained by continuously sampling according to certain rules, and the bit frequencies corresponding to the new Z-bias values ​​are determined by one-dimensional spectral scanning. Here, each sampling can obtain a set of data containing bit frequencies f. q With Z bias value A Z The tuples (corresponding to the tuples mentioned above) can be used to update the first initial mapping function B2F based on the new tuples; after multiple rounds of small-range sampling, the updated first initial mapping function B2F can be obtained while ensuring that the first initial mapping function B2F (for example, containing zero bias) is in good condition.

[0150] Finally, multiple rounds of large-scale sampling are performed. For example, based on the first initial mapping function B2F, a second initial mapping relationship is constructed (which can be expressed by a function, denoted as the second initial mapping function F2B: F q →A Z Based on the second initial mapping function F2B, a wide range of sampling is performed to obtain a set of data containing bit frequencies F. q With Z bias value A Z The binary tuples (corresponding to the binary sampling groups mentioned above) are used to ensure that the final sampling termination condition is met. Then, after the sampling termination condition is met, the target mapping relationship is fitted based on all the binary sampling groups. For example, it can be expressed by a function, which can be denoted as the target mapping function B2F. * :

[0151] The second stage is the prediction stage.

[0152] In this prediction phase, the target mapping function B2F obtained in the calibration phase can be used directly. * The frequency F at which the qubit is modulated to the target bit frequency is calculated. q-target The required Z-bias value, i.e.

[0153] Specifically, the following detailed explanation of the calibration phase process in this disclosed scheme, in conjunction with the accompanying drawings, is provided in further detail, such as... Figure 5 As shown, the calibration phase specifically includes:

[0154] Step 1: Preliminary experiments to obtain estimates of the modulation period of the qubit, the zero bias of the qubit, and the maximum bit frequency of the qubit.

[0155] Here, the zero bias of a quantum bit refers to the bias value of the smallest magnetic flux value among the multiple bias values ​​corresponding to the maximum bit frequency.

[0156] Furthermore, in one example, the qubits in the quantum chip are coupled with readout cavities, and the readout cavities correspond one-to-one with the qubits. In this case, the modulation period of the readout cavity (also called the readout cavity) of the qubit can be obtained, and the modulation period of the readout cavity of the qubit can be used as the modulation period of the qubit.

[0157] It should be noted that the modulation period of the readout cavity, the estimated value of the zero bias, and the maximum bit frequency can be obtained through preliminary experiments, such as through dispersive modulation experiments. This example does not impose specific restrictions on this.

[0158] It should be noted that the scanning of the pre-experiment is usually not accurate enough and other bit frequencies cannot be obtained. Therefore, the estimated value of the zero bias obtained from the pre-experiment can be used as the initial value of the Z bias value of the present invention. In this way, the present invention can be used to obtain a highly accurate zero bias, and at the same time, the target mapping relationship between the Z bias value and the bit frequency can be obtained.

[0159] Step 2: Initialize the bias list (e.g., it can be denoted as bias_list) It can be simply remembered as ), a list of bit frequencies (e.g., freq_list) It can be simply remembered as Then input the estimated value of the zero bias obtained in step 1 into the bias list.

[0160] Step 3: Determine the one-dimensional spectral scanning experiment, for example, based on the estimated maximum bit frequency obtained in the previous step; and, from the bias list... Retrieve the bias value that needs to be processed, such as retrieving the bias list. The last bias value in the value, denoted as A. z (i) The bias value of the Z channel of the qubit (i.e., the Z bias value) is set to A. z (i) The bias for the Z channel is the bias value A. z (i) The qubits were used to perform a determined one-dimensional spectral scanning experiment.

[0161] Here, the bias value A z(i) The superscript 'i' indicates that the element is in the bias list. The position in the middle.

[0162] Furthermore, it should be noted that newly sampled bias values ​​can be entered into the bias list sequentially according to the sampling order.

[0163] Step 4: Fit the bias value A z (i) The corresponding bit frequency F q (i) and the resulting bit frequency F q (i) Add to bit frequency list middle.

[0164] Specifically, the one-dimensional frequency scanning experiment set in step 4 is run to obtain the one-dimensional spectrum experimental results. Based on the one-dimensional spectrum experimental results, one-dimensional spectrum fitting is performed to obtain the bias value A. z (i) The corresponding bit frequency F q (i) .

[0165] In one example, a one-dimensional frequency scanning experiment may include three pulse configuration settings for configuring the pulses in the X-channel, Z-channel, and RO-channel; here, the X-channel, Z-channel, and RO-channel represent three control channels for a qubit (e.g., the same qubit). Specifically, the X-channel is used to control the qubit along the X-axis by applying a microwave pulse signal, the Z-channel is used to modulate the qubit frequency by applying a DC bias voltage, and the RO-channel is used to read the reflected signal by applying a readout pulse to measure the qubit's state. Thus, a one-dimensional spectral experiment is performed on the qubit using the pulse configuration settings of these three channels.

[0166] For example, as shown in Figure 6(a), the readout pulse starts at 5500 ns. At this time, the Z-channel pulse (also called the Z-pulse) from 0 ns to 5500 ns can bias the qubit to a certain bit frequency. Simultaneously, a frequency sweep along the X-axis can be performed. Here, the pulses on the X-channel are spaced at intervals to allow the Z-pulse to stabilize, thus improving the modulation accuracy of the bit frequency. The sudden change in amplitude of the Z-pulse after 5500 ns is to ensure that the qubit is modulated to the appropriate frequency during readout. In this way, the bit frequency corresponding to the bias value can be obtained through pulses similar to those shown in Figure 6(a).

[0167] It should be noted that the bit frequency list Each bit frequency in the offset list can be found in the offset list. The corresponding bias value is determined in the middle, and accordingly, the bias list is generated. Each bias value in the bit frequency column can also be found in the bit frequency column. The corresponding bit frequency is determined in the sample; furthermore, in one example, the correspondence between bit features and bias values ​​can be stored in the form of binary sampling groups.

[0168] Step 5: Determine if the current number of sampling points meets the requirements, that is, check the bias list. The question asks whether the number of bias values ​​meets the requirements, or whether the number of binary sampling groups meets the requirements. Specifically, it checks if the number of current sampling points, i.e., the bias list, meets the requirements. When the number of bias values ​​is small, for example, less than the preset threshold N sample (min) If the result obtained in step 4 is found to have a large error, further sampling is required, i.e., proceed to step 6 to optimize the fitting result of step 4. Alternatively, if the current number of sampling points, i.e., the bias list... The number of bias values ​​in the data is greater than the preset threshold N. sample (min) If the result obtained in step 4 is deemed to meet the accuracy requirements, then proceed to step 7.

[0169] Step 6, based on the current processing bias value A z (i) Perform small-scale sampling, for example, taking the current bias value A... z (i) Subtracting a specified value (e.g., 0.01 times the modulation period) yields the next new bias value to be fitted (e.g., denoted as A). z (next) And add the new bias value to the bias list. In (e.g., added to the bias list) The last digit, for example, as A z (i+1) ), to update the bias list Then return to step 3.

[0170] Step 7, based on the current bias list and the current list of bit frequencies That is, based on the current binary sampling group, the first initial mapping relationship is obtained by fitting, that is, the first initial mapping relationship from the bias value to the bit frequency (also called the first initial mapping function), which can be denoted as B2F: A Z →f q .

[0171] Specifically, in this step, the expression for the first initial mapping function B2F is:

[0172] B2F(Az ) = acos(bA z +c)+d.

[0173] At this point, the current bias list can be used in this step. and the current list of bit frequencies The sampling points stored in the database, that is, the binary sampling group based on the currently obtained bias value and the bit frequency corresponding to that bias value, are fitted to obtain the first initial mapping function B2F. The values ​​of each fitting parameter in the first initial mapping function B2F (i.e., the fitting parameter values) can be denoted as: Accordingly, the expression for the first initial mapping function B2F can be specifically defined as follows:

[0174] B2F(A z ) = a * cos(b * A z +c * )+d * .

[0175] Furthermore, in practical applications, the results obtained after fitting can be cached.

[0176] Step 8: Determine whether the first initial mapping function B2F obtained in step 7 covers the position of zero bias.

[0177] It should be noted that in practical applications, it is desirable to have a sampling point (bias list). The range of the bias (in the range) can cover zero bias, with the following advantages:

[0178] (1) It can increase the accuracy of the fitting;

[0179] (2) It can obtain a more accurate zero-bias position.

[0180] It should be noted that the above The fitting parameter value c in * This represents the zero bias estimated based on the first initial mapping function B2F; further, if The fitting parameter value c in * In the current bias list Within the corresponding bias interval, such as the fitted parameter values If the current bias value sampling range has covered the zero bias position, proceed to step 10; otherwise, proceed to step 9.

[0181] Step 9: Perform small-range sampling, that is, search for the position of zero bias within a small range to obtain the next sampling point that needs to be processed, that is, to obtain the next new bias value that needs to be fitted (for example, it can be denoted as A). z (next) And add the new bias value to the bias list. In (e.g., added to the bias list) The last digit, for example, as A z (i +1) ), to update the bias list Then return to step 3.

[0182] Step 9-1, after determining the parameter values If the parameter value is correct, proceed to step 9-2; otherwise, proceed to the step 9-2 after determining the parameter value. Then proceed to step 8-3.

[0183] In other words, for the fitted parameter values In such cases, it is necessary to continue searching for the location of zero bias. Therefore, this step can be used to obtain the location of the next sampling point (i.e., the next bias value).

[0184] Step 9-2, based on the bias list The minimum bias value is used to obtain the next sampling point, which is also the new bias value that needs to be fitted.

[0185] For example, in one example, the bias list can be... The minimum bias value is subtracted from a first preset value to obtain the next sampling point. Further, in one example, the first preset value is related to the modulation period of the qubit, for example, it is 0.01, 0.02 or 0.03 times the modulation period, etc.

[0186] In other words, in one example, due to the estimated location of the zero bias, i.e., the fitted parameter value c * exist The left side, so the bias list can be... The difference between the minimum bias value and the first preset value (such as 0.01 times the modulation period) is used as the next sampling point.

[0187] Step 9-3, based on the bias list The maximum bias value is used to obtain the next sampling point, which is the next new bias value that needs to be fitted.

[0188] For example, in one example, the bias list can be... The maximum bias value is added to a second preset value to obtain the next sampling point. Further, in one example, the second preset value is related to the modulation period of the qubit, for example, it is 0.01, 0.02 or 0.03 times the modulation period, etc.

[0189] In other words, in one example, due to the estimated location of the zero bias, i.e., the fitted parameter value c * exist On the right side, therefore, the bias list can be... The maximum bias value is added to the second preset value (e.g., 0.01 times the modulation period) and used as the next sampling point.

[0190] Step 10: Determine whether the current sampling has reached at least one of the following sampling termination conditions. If the sampling termination condition is reached, proceed to step 13; otherwise, if the sampling termination condition has not been reached, proceed to step 11.

[0191] The number of sampling points exceeds the threshold N. sample (max) That is, the current bias list The number of bias values ​​in the data exceeds the threshold N. sample (max) ;

[0192] Current bit frequency list The difference between the maximum and minimum bit frequencies, i.e. Exceeding the frequency threshold

[0193] Step 11: Based on the first initial mapping function B2F, construct a second initial mapping relationship (also called the second initial mapping function) from the bit frequency to the bias value, which can be denoted as F2B: F q →A Z Then proceed to step 12.

[0194] Here, the second initial mapping function F2B is:

[0195] When the fitted parameter value c * When <0, F2B(F q ) = arccos((F q -d * ) / a * -c * ) / b * ;

[0196] When the fitted parameter value c * When ≤0, F2B(F q )=-arccos((F q -d * ) / a* -c * ) / b * ;

[0197] Here, the second initial mapping function F2B is similar to the inverse function of the first initial mapping function B2F. Thus, the second initial mapping function F2B can be used to predict the bias value required for a specified bit frequency.

[0198] Step 12: Perform large-scale sampling, that is, based on the second initial mapping function F2B constructed in step 11, obtain the next sampling point that needs to be processed, that is, obtain the next new bias value that needs to be fitted, and add the new bias value to the bias column. In (e.g., added to the bias list) (last one) to update the bias list Then return to step 3.

[0199] Specifically, in one example, the next new bias value to be fitted can be denoted as A. z (next) At this point, the new bias value A z (next) The corresponding bit frequency can be denoted as F. q (next) At this point, it can be based on the bit frequency list. The bit frequency F is obtained from the bit frequency in the data. q (next) For example, F q (next) =F q (i) -0.5GHz, where 0.5GHz is merely an illustrative example; in practical applications, other values ​​are possible, and this disclosure does not impose any limitations on them. Furthermore, the bit frequency F can be obtained using the second initial mapping function F2B. q (next) The corresponding bias value A z (next) At this point, the obtained bias value A can be... z (next) Add to bias list In (e.g., added to the bias list) The last digit, such as A z (i+1) ), to update the bias list Then return to step 3.

[0200] Step 13: Construct the target mapping relationship (also called the target mapping function) from the bias value to the bit frequency, which can be denoted as the target mapping function B2F. * And B2F * :

[0201]

[0202] in, These are the parameters to be fitted.

[0203] Step 14, based on the current bias list and the current list of bit frequencies That is, based on the currently obtained bias value and the binary sampling group of the bit frequency corresponding to that bias value, the target mapping function B2F is applied. * Perform a fitting operation to obtain the target fitting result, and output the target fitting result.

[0204] For example, in one example, the target fitting result can be recorded. at this time

[0205]

[0206] Furthermore, the target mapping function B2F * (A) z ):

[0207]

[0208] The second stage is the prediction stage.

[0209] It should be noted that, after obtaining the target mapping function B2F * Then, the objective mapping function B2F can be solved. * The inverse function is used to obtain the mapping relationship between bit frequency and offset value (which can be denoted as F2B). * However, in practical applications, due to the difficulty in solving the objective mapping function B2F... * The inverse function is quite complex; therefore, this disclosed scheme uses an optimization method to solve for the target offset value of the target bit frequency. Specifically,

[0210] Step 1, determine the desired target bit frequency as F. q-target ;

[0211] Step 2, construct the loss function. For example, the expression for the loss function is:

[0212] Loss(A z )=|B2F * (A z )-F q-target |;

[0213] Step 3: Based on the loss function, obtain the target bit frequency F. a-target The target bias value of the required Z bias can be denoted as: At this point, the target bias value

[0214] As shown in Figure 6(b), experiments show that using the proposed scheme to dynamically determine the maximum bit frequency and the mapping relationship between the bias value and the bit frequency (i.e., the target mapping relationship mentioned above) takes 73 minutes and covers a range of approximately 2.3 GHz (3.15 GHz - 5.45 GHz). As shown in Figure 6(c), compared to the existing conventional method which takes 59 minutes but only covers a range of 0.3 GHz (6.4 GHz - 6.65 GHz), the proposed scheme significantly improves efficiency.

[0215] In summary, the disclosed solution has the following advantages:

[0216] First, this disclosed solution designs a sampling strategy that dynamically adjusts the sampling density at the vertices based on the latest sampling data to achieve a balance between the "accuracy" of the bit frequency (small-range sampling steps, effectively balancing accuracy) and the "efficiency" of processing (large-range sampling steps, effectively balancing efficiency), thus meeting the measurement accuracy requirements for the maximum bit frequency in the experiment. Furthermore, the method of dynamically adjusting the scanning range in this disclosed solution, for example, can dynamically determine the next scanning position and scanning range based on the modulation period of the bit frequency and the latest sampling data. Compared to existing solutions that obtain the above mapping relationship through two-dimensional scanning, this disclosed solution effectively reduces unnecessary scanning sampling, significantly reduces the number of sampling points, and thus greatly improves processing efficiency.

[0217] Second, the disclosed solution can be processed according to the physical relationship between the bias value and the bit frequency to predict the bias value and scanning range of the next sampling point. This process does not require manual intervention to set relevant parameters, thus achieving automation.

[0218] Third, this disclosed scheme designs a new target mapping function B2F. * Compared to traditional mapping functions (which are greatly affected by the environment and have large errors), this method significantly improves the fitting accuracy and prediction precision.

[0219] This disclosure also provides a device for processing quantum measurement and control parameters, such as... Figure 7 As shown, it includes:

[0220] The initial mapping relationship processing unit 701 is used to determine a first initial mapping relationship B2F, wherein the first initial mapping relationship B2F is obtained by fitting the binary sampling group contained in the target set, and can characterize the mapping relationship from the bias value of the qubit to the bit frequency of the qubit; the binary sampling group includes the sampled bias value and the sampled bit frequency estimated based on the sampled bias value.

[0221] The target mapping relationship processing unit 702 is used to fit a target mapping relationship B2F from the bias value to the bit frequency based on each binary sampling group contained in the target set, when a preset bias value is determined to be included in the first initial mapping relationship B2F, and when the target set used for fitting reaches the sampling termination condition. * Wherein, the target mapping relationship B2F * This is used to characterize the mapping relationship between the bias value of a qubit and the bit frequency of the qubit, based on the target mapping relationship B2F. * The accuracy of the determined bias value is greater than the accuracy of the bias value determined based on the first initial mapping relationship B2F.

[0222] In a specific example of the scheme disclosed herein, the target mapping relationship processing unit is further configured to:

[0223] Obtain the target bit frequency F q-target ;

[0224] Based on the target mapping relationship B2F * The target bit frequency F is obtained. a-target The corresponding target bias value Among them, using the target bias value When qubits are controlled, it is possible to make the frequency of the qubit equal to the target bit frequency F. a-target .

[0225] In a specific example of the scheme disclosed herein, the target mapping relationship is expressed by the following target mapping function:

[0226]

[0227] Among them, A z Indicates the bias value, B2F * (A) z The value of ) represents the bias value A. z The corresponding bit frequency; k * , l * m * n * e * f * g * h * This represents the fitted parameter values ​​obtained after the fitting process.

[0228] In a specific example of the scheme disclosed herein, the initial mapping relationship processing unit is specifically used for:

[0229] Based on the initial sampling process, a target set containing N binary sampling groups is obtained;

[0230] Based on each binary sampling group contained in the target set, the first initial mapping relationship B2F is obtained by fitting.

[0231] In a specific example of the scheme disclosed herein, the first initial mapping relationship B2F is expressed by the following first initial mapping function:

[0232] B2F(A z ) = a * cos(b * A z +c * )+d * ;

[0233] Among them, A z Indicates the bias value, B2F(A) z The value of ) represents the bias value A. z The corresponding bit frequency; a * b * c * d * This represents the fitted parameter values ​​obtained after the fitting process.

[0234] In a specific example of the scheme disclosed herein, the initial mapping relationship processing unit is further configured to:

[0235] If the first initial mapping relationship B2F does not contain a preset bias value, a first type of sampling procedure is executed to obtain a new set of binary samples. The new binary samples obtained from the first type of sampling procedure include: the sampled bias value A. z (next1) and based on the sampling bias value A z (next1) The obtained sampling bit frequency F q (next1) ;

[0236] Update the target set;

[0237] Based on the updated target set, update the first initial mapping relationship B2F to determine whether the updated first initial mapping relationship B2F contains a preset bias value.

[0238] In a specific example of the scheme disclosed herein, the initial mapping relationship processing unit is further configured to:

[0239] Determine the fitted parameter value c * Whether it is within the bias interval, wherein the bias interval is obtained based on the maximum and minimum bias values ​​in the target set, and the fitting parameter value c * The preset bias value estimated by the first initial mapping relationship B2F;

[0240] Based on the judgment result, determine whether the first initial mapping relationship B2F contains a preset bias value.

[0241] In a specific example of the scheme disclosed herein, the initial mapping relationship processing unit is specifically used for:

[0242] Fitting parameter value c * If the sampled bias value is less than the minimum bias value within the bias interval, the sampled bias value A is obtained based on the minimum bias value within the bias interval. z (next1) ;

[0243] The sampling bias value A was obtained by fitting. z (next1) The corresponding sampling bit frequency F q (nexti) This is to obtain a new set of binary sampling groups.

[0244] In a specific example of the scheme disclosed herein, the initial mapping relationship processing unit is specifically used for:

[0245] Subtract the first preset value from the minimum bias value within the bias interval to obtain the sampled bias value A. z (next1) The first preset value is obtained based on the modulation period of the quantum bit.

[0246] In a specific example of the scheme disclosed herein, the initial mapping relationship processing unit is specifically used for:

[0247] Fitting parameter value c * If the bias value is greater than the maximum bias value within the bias interval, the sampled bias value A is obtained based on the maximum bias value within the bias interval. z (next1) ;

[0248] The sampling bias value A was obtained by fitting. z (next1) The corresponding sampling bit frequency F q (next1) This is to obtain a new set of binary sampling groups.

[0249] In a specific example of the scheme disclosed herein, the initial mapping relationship processing unit is specifically used for:

[0250] Add the maximum bias value within the bias interval to the second preset value to obtain the sampling bias value A. z (next1) The second preset value is obtained based on the modulation period of the quantum bit.

[0251] In a specific example of the scheme disclosed herein, the initial mapping relationship processing unit is further configured to:

[0252] If the first initial mapping relationship B2F contains a preset bias value and the target set used for fitting does not meet the sampling termination condition, a second type of sampling process is executed to obtain a new set of binary sampling groups. The new binary sampling groups obtained by the second type of sampling process include: the sampled bit frequency F. q (next2) and based on the sampling bit frequency F q (next2) The obtained sampling bias value A z (next2) The second type of sampling procedure differs from the first type of sampling procedure.

[0253] Update the target set;

[0254] Based on the updated target set, update the first initial mapping relationship B2F to determine whether the updated first initial mapping relationship B2F contains a preset bias value.

[0255] In a specific example of the scheme disclosed herein, the sampling termination condition includes one of the following:

[0256] The number of binary sample groups contained in the target set exceeds the number threshold;

[0257] The difference between the maximum and minimum bit frequencies in the target set exceeds the frequency threshold.

[0258] In a specific example of the scheme disclosed herein, the initial mapping relationship processing unit is specifically used for:

[0259] Based on the sampling bit frequencies contained in the target set, the sampling bit frequency F is determined. q (next2) ;

[0260] Based on the second initial mapping relationship F2B, the sampling bit frequency F is obtained. q (next2) The corresponding sampling bias value A z (next2) To obtain a new set of binary sampling groups; wherein, the second initial mapping relationship F2B is obtained based on the first initial mapping relationship and can characterize the mapping relationship between the bit frequency of the qubit and the bias value of the qubit.

[0261] In a specific example of the scheme disclosed herein, the fitting parameter value c in the first initial mapping relationship B2F * When the value is less than the first value, the expression for the second initial mapping function is: F2B(F q ) = arccos((F q -d * ) / a * -c* ) / b * ;

[0262] or,

[0263] Fitting parameter value c * If the value is greater than or equal to the first value, the expression for the second initial mapping function is: F2B(F q )=-arccos((F q -d * ) / a * -c * ) / b * ;

[0264] Among them, F q F2B represents the bit frequency. q The value of ) represents the bit frequency F. q The corresponding bias value; a * b * c * d * This represents the fitting parameter value obtained after fitting processing; the fitting parameter value c * The preset bias value estimated by the first initial mapping relationship B2F is used to characterize the first initial mapping relationship.

[0265] For a description of the specific functions and examples of each unit of the apparatus in this disclosure embodiment, please refer to the relevant descriptions of the corresponding steps in the above method embodiments, which will not be repeated here.

[0266] The acquisition, storage, and application of user personal information involved in the technical solution disclosed herein comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0267] This disclosure also provides a non-transitory computer-readable storage medium storing computer instructions that, when executed by at least one quantum processing unit, cause the at least one quantum processing unit to perform the method described above using a quantum computing device.

[0268] This disclosure also provides a computer program product, including a computer program that, when executed by a processor, implements the methods described above for use in classical computing devices.

[0269] Alternatively, the computer program, when executed by at least one quantum processing unit, implements the method applied to a quantum computing device.

[0270] This disclosure also provides a quantum computing device, the quantum computing device comprising:

[0271] At least one quantum processing unit;

[0272] A memory, coupled to the at least one QPU and used to store executable instructions,

[0273] 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 applied to the quantum computing device.

[0274] It is understood that the quantum processing unit (QPU) used in the present disclosure may also be referred to as a quantum processor or quantum chip, and may involve a physical chip comprising multiple qubits interconnected in a specific manner.

[0275] Furthermore, it is understood that the qubit described in this disclosure can refer to the basic information unit of a quantum computing device. The qubit is contained within the QPU and extends the concept of the classical digital bit.

[0276] According to embodiments of this disclosure, this disclosure also provides a computing device, a readable storage medium, and a computer program product.

[0277] Figure 8 A schematic block diagram of an example computing device 800 that can be used to implement embodiments 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 may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0278] like Figure 8 As shown, device 800 includes a computing unit 801, which can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) 802 or a computer program loaded from storage unit 808 into random access memory (RAM) 803. RAM 803 may also store various programs and data required for the operation of device 800. The computing unit 801, ROM 802, and RAM 803 are interconnected via bus 804. Input / output (I / O) interface 805 is also connected to bus 804.

[0279] Multiple components in device 800 are connected to I / O interface 805, including: input unit 806, such as keyboard, mouse, etc.; output unit 807, such as various types of monitors, speakers, etc.; storage unit 808, such as disk, optical disk, etc.; and communication unit 809, such as network card, modem, wireless transceiver, etc. Communication unit 809 allows device 800 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0280] The computing unit 801 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 801 performs the various methods and processes described above, such as the method for processing quantum measurement and control parameters. For example, in some embodiments, the method for processing quantum measurement and control parameters can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as storage unit 808. In some embodiments, part or all of the computer program can be loaded and / or installed on device 800 via ROM 802 and / or communication unit 809. When the computer program is loaded into RAM 803 and executed by the computing unit 801, one or more steps of the method for processing quantum measurement and control parameters described above can be performed. Alternatively, in other embodiments, computing unit 801 may be configured to perform a processing method for quantum measurement and control parameters by any other suitable means (e.g., by means of firmware).

[0281] Various embodiments 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-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0282] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0283] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0284] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, 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 sound input, voice input, or tactile input).

[0285] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0286] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0287] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0288] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for processing quantum measurement and control parameters, comprising: Determine the first initial mapping relationship Wherein, the first initial mapping relationship It is obtained by fitting the binary sampling groups contained in the target set, and can characterize the mapping relationship between the bias value of the qubit and the bit frequency of the qubit; the binary sampling group contains the sampled bias value and the sampled bit frequency estimated based on the sampled bias value; the first initial mapping relationship This can be expressed by the following first initial mapping function: ; Indicates the bias value. The value represents the bias value. The corresponding bit frequency; This represents the estimated fitting parameter values ​​after fitting processing; Determining the first initial mapping relationship When the preset bias value is covered and the target set used for fitting reaches the sampling termination condition, the target mapping relationship from the bias value to the bit frequency is fitted based on each binary sampling group contained in the target set. Wherein, the preset bias value is the zero bias of the qubit; zero bias refers to the bias value of the smallest magnetic flux value among multiple bias values ​​corresponding to the maximum bit frequency of the qubit; the target mapping relationship The mapping relationship between the bias value and the bit frequency of a qubit is used to characterize the target mapping relationship. The accuracy of the determined bias value is greater than that based on the first initial mapping relationship. The accuracy of the determined bias value; Wherein, the determination of the first initial mapping relationship Covers the location of the preset bias value, including: Fitting parameter values Determine the first initial mapping relationship when the bias interval is within the bias interval. The location of the preset bias value is covered; the bias interval is obtained based on the maximum and minimum bias values ​​in the target set.

2. The method according to claim 1, further comprising: Obtain target bit frequency ; Based on the target mapping relationship To obtain the target bit frequency The corresponding target bias value Among them, the target bias value is used. With control over qubits, it is possible to make the frequency of a qubit equal to the target bit frequency. .

3. The method according to claim 1, wherein, The target mapping relationship This can be expressed by the following target mapping function: in, Indicates the bias value. The value represents the bias value. The corresponding bit frequency; This represents the fitted parameter values ​​obtained after the fitting process.

4. The method according to claim 3, wherein, The determination of the first initial mapping relationship ,include: Based on the initial sampling process, a target set containing N binary sampling groups is obtained; where N is an integer greater than or equal to 2. Based on each binary sampling group contained in the target set, the first initial mapping relationship is obtained by fitting. .

5. The method according to claim 4, in determining the first initial mapping relationship Subsequently, the method further includes: Determining the first initial mapping relationship If the sample does not contain a preset bias value, the first type of sampling procedure is executed to obtain a new binary sample set. The new binary sample set obtained by the first type of sampling procedure includes: the sampled bias value. and based on sampling bias value The obtained sampling bit frequency ; Update the target set; Based on the updated target set, update the first initial mapping relationship. To determine the updated first initial mapping relationship Does it include a preset bias value? 6. The method according to claim 5, wherein, After determining the first initial mapping relationship Before a preset bias value is included, the method further includes: Determine the fitted parameter values Is it within the bias interval? Based on the judgment result, the first initial mapping relationship is determined. Does it include a preset bias value? 7. The method according to claim 6, wherein, The execution of the first type of sampling procedure to obtain a new set of binary samples includes: Fitting parameter values If the value is less than the minimum bias value within the bias interval, the sampled bias value is obtained based on the minimum bias value within the bias interval. ; The sampling bias value was obtained by fitting. Corresponding sampling bit frequency This is to obtain a new set of binary sampling groups.

8. The method according to claim 7, wherein, The sampled bias value is obtained based on the minimum bias value within the bias interval. ,include: Subtract the first preset value from the minimum bias value within the bias interval to obtain the sampled bias value. The first preset value is obtained based on the modulation period of the quantum bit.

9. The method according to any one of claims 6-8, wherein, The execution of the first type of sampling procedure to obtain a new set of binary samples includes: Fitting parameter values If the bias value is greater than the maximum bias value within the bias interval, the sampled bias value is obtained based on the maximum bias value within the bias interval. ; The sampling bias value was obtained by fitting. Corresponding sampling bit frequency This is to obtain a new set of binary sampling groups.

10. The method according to claim 9, wherein, The sampled bias value is obtained based on the maximum bias value within the bias interval. ,include: The maximum bias value within the bias interval is added to the second preset value to obtain the sampled bias value. The second preset value is obtained based on the modulation period of the quantum bit.

11. The method according to any one of claims 4-8, wherein the first initial mapping relationship is determined... Subsequently, the method further includes: After determining the first initial mapping relationship If the target set containing the preset bias value used for fitting does not meet the sampling termination condition, a second type of sampling procedure is executed to obtain a new set of binary sampling groups. The new binary sampling groups obtained by the second type of sampling procedure include: the sampled bit frequency... and based on sampling bit frequency The obtained sampling bias value The second type of sampling procedure differs from the first type of sampling procedure. Update the target set; Based on the updated target set, update the first initial mapping relationship. To determine the updated first initial mapping relationship Does it include a preset bias value? 12. The method according to claim 11, wherein, Sampling termination conditions include one of the following: The number of binary sample groups contained in the target set exceeds the number threshold; The difference between the maximum and minimum bit frequencies in the target set exceeds the frequency threshold.

13. The method according to claim 11, wherein, The second type of sampling process is performed to obtain a new set of binary samples, including: Based on the sampling bit frequencies contained in the target set, the sampling bit frequencies are determined. ; Based on the second initial mapping relationship The sampling bit frequency is obtained. The corresponding sampling bias value To obtain a new set of binary sampling groups; wherein, the second initial mapping relationship It is a mapping relationship obtained based on the first initial mapping relationship, which can characterize the bit frequency of a quantum bit to the bias value of the quantum bit.

14. The method according to claim 13, wherein, First initial mapping relationship Fitting parameter values ​​in When the value is less than the first value, the expression for the second initial mapping function is: ; or, Fitting parameter values If the value is greater than or equal to the first value, the expression for the second initial mapping function is: ; in, Indicates bit frequency. The value represents the bit frequency. The corresponding bias value; This represents the fitting parameter values ​​obtained after fitting processing; the fitting parameter values Characterizing the first initial mapping relationship The estimated preset bias value.

15. A device for processing quantum measurement and control parameters, comprising: The initial mapping relationship processing unit is used to determine the first initial mapping relationship. Wherein, the first initial mapping relationship It is obtained by fitting the binary sampling groups contained in the target set, and can characterize the mapping relationship between the bias value of the qubit and the bit frequency of the qubit; the binary sampling group contains the sampled bias value and the sampled bit frequency estimated based on the sampled bias value; the first initial mapping relationship This can be expressed by the following first initial mapping function: ; Indicates the bias value. The value represents the bias value. The corresponding bit frequency; This represents the estimated fitting parameter values ​​after fitting processing; The target mapping relationship processing unit is used to determine the first initial mapping relationship. When the preset bias value is covered and the target set used for fitting reaches the sampling termination condition, the target mapping relationship from the bias value to the bit frequency is fitted based on each binary sampling group contained in the target set. Wherein, the preset bias value is the zero bias of the qubit; zero bias refers to the bias value of the smallest magnetic flux value among multiple bias values ​​corresponding to the maximum bit frequency of the qubit; the target mapping relationship The mapping relationship between the bias value and the bit frequency of a qubit is used to characterize the target mapping relationship. The accuracy of the determined bias value is greater than that based on the first initial mapping relationship. The accuracy of the determined bias value; The target mapping relationship processing unit is further configured to fit parameter values. Determine the first initial mapping relationship when the bias interval is within the bias interval. The location of the preset bias value is covered; the bias interval is obtained based on the maximum and minimum bias values ​​in the target set.

16. The apparatus according to claim 15, wherein, The target mapping relationship processing unit is further configured to: Obtain target bit frequency ; Based on the target mapping relationship To obtain the target bit frequency The corresponding target bias value Among them, the target bias value is used. With control over qubits, it is possible to make the frequency of a qubit equal to the target bit frequency. .

17. The apparatus according to claim 15, wherein, The target mapping relationship This can be expressed by the following target mapping function: in, Indicates the bias value. The value represents the bias value. The corresponding bit frequency; This represents the fitted parameter values ​​obtained after the fitting process.

18. The apparatus according to claim 17, wherein, The initial mapping relationship processing unit is specifically used for: Based on the initial sampling process, a target set containing N binary sampling groups is obtained; Based on each binary sampling group contained in the target set, the first initial mapping relationship is obtained by fitting. .

19. The apparatus according to claim 18, wherein, The initial mapping relationship processing unit is further configured to: Determining the first initial mapping relationship If the sample does not contain a preset bias value, the first type of sampling procedure is executed to obtain a new binary sample set. The new binary sample set obtained by the first type of sampling procedure includes: the sampled bias value. and based on sampling bias value The obtained sampling bit frequency ; Update the target set; Based on the updated target set, update the first initial mapping relationship. To determine the updated first initial mapping relationship Does it include a preset bias value? 20. The apparatus according to claim 19, wherein, The initial mapping relationship processing unit is further configured to: Determine the fitted parameter values Is it within the bias interval? Based on the judgment result, the first initial mapping relationship is determined. Does it include a preset bias value? 21. The apparatus according to claim 20, wherein, The initial mapping relationship processing unit is specifically used for: Fitting parameter values If the value is less than the minimum bias value within the bias interval, the sampled bias value is obtained based on the minimum bias value within the bias interval. ; The sampling bias value was obtained by fitting. Corresponding sampling bit frequency This is to obtain a new set of binary sampling groups.

22. The apparatus according to claim 21, wherein, The initial mapping relationship processing unit is specifically used for: Subtract the first preset value from the minimum bias value within the bias interval to obtain the sampled bias value. The first preset value is obtained based on the modulation period of the quantum bit.

23. The apparatus according to any one of claims 20-22, wherein, The initial mapping relationship processing unit is specifically used for: Fitting parameter values If the bias value is greater than the maximum bias value within the bias interval, the sampled bias value is obtained based on the maximum bias value within the bias interval. ; The sampling bias value was obtained by fitting. Corresponding sampling bit frequency This is to obtain a new set of binary sampling groups.

24. The apparatus according to claim 23, wherein, The initial mapping relationship processing unit is specifically used for: The maximum bias value within the bias interval is added to the second preset value to obtain the sampled bias value. The second preset value is obtained based on the modulation period of the quantum bit.

25. The apparatus according to any one of claims 18-22, wherein, The initial mapping relationship processing unit is further configured to: After determining the first initial mapping relationship If the target set containing the preset bias value used for fitting does not meet the sampling termination condition, a second type of sampling procedure is executed to obtain a new set of binary sampling groups. The new binary sampling groups obtained by the second type of sampling procedure include: the sampled bit frequency... and based on sampling bit frequency The obtained sampling bias value The second type of sampling procedure differs from the first type of sampling procedure. Update the target set; Based on the updated target set, update the first initial mapping relationship. To determine the updated first initial mapping relationship Does it include a preset bias value? 26. The apparatus according to claim 25, wherein, Sampling termination conditions include one of the following: The number of binary sample groups contained in the target set exceeds the number threshold; The difference between the maximum and minimum bit frequencies in the target set exceeds the frequency threshold.

27. The apparatus according to claim 25, wherein, The initial mapping relationship processing unit is specifically used for: Based on the sampling bit frequencies contained in the target set, the sampling bit frequencies are determined. ; Based on the second initial mapping relationship The sampling bit frequency is obtained. The corresponding sampling bias value To obtain a new set of binary sampling groups; wherein, the second initial mapping relationship It is a mapping relationship obtained based on the first initial mapping relationship, which can characterize the bit frequency of a quantum bit to the bias value of the quantum bit.

28. The apparatus according to claim 27, wherein, First initial mapping relationship Fitting parameter values ​​in When the value is less than the first value, the expression for the second initial mapping function is: ; or, Fitting parameter values If the value is greater than or equal to the first value, the expression for the second initial mapping function is: ; in, Indicates bit frequency. The value represents the bit frequency. The corresponding bias value; This represents the fitting parameter values ​​obtained after fitting processing; the fitting parameter values Characterizing the first initial mapping relationship The estimated preset bias value.

29. A computing device, comprising: At least one quantum processing unit (QPU); A memory, coupled to the at least one QPU and used to store executable instructions, The instruction is executed by the at least one QPU to enable the at least one QPU to perform the method of any one of claims 1-14; Or, including: 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 to enable the at least one processor to perform the method of any one of claims 1-14.

30. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, When at least one quantum processing unit is executed, the computer instructions cause the at least one quantum processing unit to perform the method according to any one of claims 1-14; Alternatively, the computer instructions are used to cause the computer to perform the method according to any one of claims 1-14.

31. A computer program product comprising a computer program that, when executed by at least one quantum processing unit, implements the method according to any one of claims 1-14; Alternatively, the computer program, when executed by a processor, implements the method according to any one of claims 1-14.

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