Data classification anomaly judgment method and device, storage medium and quantum computer

Abstract

The invention discloses a data classification anomaly judgment method and device, a storage medium and a quantum computer, a representation data set is obtained by executing multiple quantum state reading measurement experiments on a single quantum bit, two Gaussian round spot distributions are determined according to the representation data set, and a data classification anomaly judgment result is obtained. The method comprises the following steps: determining a distribution ratio difference value of the quantity of measurement data on two sides of a circle center straight line, comparing the distribution ratio difference value with a first threshold value, and when the distribution ratio difference value is greater than the first threshold value, quickly judging that data classification is abnormal, thereby deducing that abnormal data exists in a plurality of read data, and further, judging that the data classification is abnormal. According to the method, the experimental parameters in the quantum state reading and measuring experiment are abnormal, so that an experimenter can know that the experimental parameters need to be adjusted for the experiment, the problem of low measurement efficiency caused by continuous operation under wrong experimental parameters is avoided, and meanwhile, under the condition, judgment of classification fidelity is not needed, and the judgment speed is increased.

Description

Technical Field

[0001] The present invention relates to the field of quantum computer technology, and in particular to a method, device, storage medium and quantum computer for determining abnormal data classification. Background Art

[0002] Quantum computing relies on quantum processing units, which include coupled quantum bits and reading cavities. The state of the quantum bits is read by reading the cavity information. As a physical measurement method for superconducting quantum bits, reading technology has its own unique physical properties. It mainly reads the state of the quantum bit through the reading cavity. The signal carrying the quantum bit state information is transmitted to the measuring device through the reading line. The measuring device processes the collected signal data to obtain the state of the quantum bit, that is, the quantum state.

[0003] At present, the classification and identification of quantum states is mainly binary identification of 0 state and 1 state. Generally, the in-phase and orthogonal components mapped on the two-dimensional plane after digital signal demodulation are used as representations, and K-means (K-means clustering algorithm) or GMM (Gaussian mixture model algorithm) is selected to classify and distinguish the representation set. Due to factors such as reading reaction and experimental environment noise, compared with 0 state and 1 state, 2 state or even higher energy level bit states may also be excited and appear in the collected representation set, so that the data in the representation set contains abnormal data classification. If it is impossible to quickly determine that there is data classification anomaly, continue the experiment, which will make the subsequent measurement results inaccurate and meaningless, and require repeated measurements, which reduces the measurement efficiency. Therefore, it is urgent to provide a method that can quickly determine data classification anomalies.

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

[0005] The purpose of the present invention is to provide a method, device, storage medium and quantum computer for determining data classification anomalies, which can quickly determine data classification anomalies.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] A first aspect of the present invention provides a method for determining abnormal data classification, comprising:

[0008] Perform multiple quantum state reading measurement experiments on a single quantum bit to obtain multiple measurement data, wherein the measurement data is scattered point data in an IQ coordinate system, and the scattered point data in multiple IQ coordinate systems constitute a characterization data set;

[0009] Determine two Gaussian circular spot distributions on the plane of the IQ coordinate system according to the characterization data set, wherein the two Gaussian circular spots are Gaussian circular spots whose quantum state measurement results are in state 1 and state 0 respectively;

[0010] According to the distribution of the two Gaussian circular spots, the distribution ratio difference of the number of measurement data located on both sides of the center straight line is determined, wherein the center straight line is a straight line connecting the two centers of the two Gaussian circular spots;

[0011] If the distribution ratio difference is greater than the first threshold limit, the data classification is judged to be abnormal; if the distribution ratio difference is less than or equal to the first threshold limit, the data classification abnormality is judged based on the classification fidelity and the second threshold limit.

[0012] In the above judgment method, further, the step of judging the classification anomaly of the data based on the classification fidelity and the second threshold limit includes:

[0013] If the classification fidelity is less than a second threshold limit, determining that the data classification is abnormal;

[0014] If the classification fidelity is greater than or equal to the second threshold, the classification anomaly of the data is judged based on the outlier distribution ratio and the third threshold.

[0015] In the above-mentioned judgment method, further, the step of judging the classification abnormality of the read data based on the abnormal value distribution ratio and the third threshold limit includes:

[0016] Determine the outlier distribution ratio based on two Gaussian spot distributions;

[0017] If the outlier distribution ratio is greater than the third threshold limit, the data classification is judged to be abnormal.

[0018] The judgment method as described above, further, if the abnormal value distribution ratio is greater than the third threshold limit, after the step of judging that the data classification is abnormal, further includes:

[0019] The power parameter of the measurement signal applied in the measurement experiment is abnormal according to the judgment result feedback.

[0020] In the above-mentioned determination method, further, the step of determining the outlier distribution ratio based on the two Gaussian spot distributions includes:

[0021] According to the distribution of two Gaussian circular spots, the coordinate data of the centers of the two Gaussian circular spots are determined;

[0022] According to the coordinate data of the two circle centers, determine the equation of the center line and the distance from the 3σ outer data point to the center line, which is recorded as the first distance;

[0023] According to the Gaussian spot radius in state 1 of the measurement result of the first distance and the quantum state, the distribution ratio of the number of data points outside 3σ whose first distance is greater than the radius is determined, that is, the outlier distribution ratio.

[0024] According to the above judgment method, further, the outlier distribution ratio is determined as:

[0025]

[0026] in,

[0027] r1 represents the radius of the Gaussian spot when the measurement result of the quantum state is in state 1; N represents the number of data points outside 3σ of the Gaussian model; Distance represents the distance from the calculated data point to the equation of the center line of the circle; Bool represents the Boolean value; Outlier represents the outlier distribution ratio; a and b represent the constants in the equation of the center line of the two Gaussian spots y=ax+b.

[0028] In the above-mentioned determination method, further, the step of determining the distribution ratio difference of the number of measurement data located on both sides of the center straight line according to the two Gaussian spot distributions includes:

[0029] According to the two Gaussian circular spot distributions, the number of measurement data on both sides of the center straight line is counted respectively;

[0030] Determine, based on the number of measurement data on both sides of the straight line, a first distribution ratio of the number of measurement data on one side of the straight line and a second distribution ratio of the number of measurement data on the other side of the straight line;

[0031] A distribution ratio difference is determined according to a difference between the first distribution ratio and the second distribution ratio.

[0032] A second aspect of the present invention provides a device for determining abnormal data classification, comprising:

[0033] Acquisition module: used to perform multiple quantum state reading measurement experiments on a single quantum bit to obtain multiple measurement data, wherein the measurement data is scattered point data of an IQ coordinate system, and the scattered point data of multiple IQ coordinate systems constitute a characterization data set;

[0034] A first determination module is used to determine two Gaussian circular spot distributions on the plane of the IQ coordinate system according to the characterization data set, wherein the two Gaussian circular spots are Gaussian circular spots whose quantum state measurement results are in the 1 state and the 0 state respectively;

[0035] The second determination module is used to determine the distribution ratio difference of the number of measurement data located on both sides of the center straight line according to the distribution of two Gaussian circular spots, wherein the center straight line is a straight line connecting the two centers of the two Gaussian circular spots;

[0036] Judgment module: used to judge that the data classification is abnormal if the distribution ratio difference is greater than the first threshold limit; if the distribution ratio difference is less than or equal to the first threshold limit, judge the data classification abnormality based on the classification fidelity and the second threshold limit.

[0037] A third aspect of the present invention provides a computer-readable storage medium, wherein the storage medium stores a computer program, wherein the computer program is configured to execute the above-mentioned determination method when running.

[0038] A fourth aspect of the present invention provides a quantum computer, characterized in that it includes the above-mentioned judgment device, or uses the above-mentioned judgment method to judge whether the measurement data classification of quantum bits is abnormal.

[0039] The beneficial effects of the present invention are:

[0040] The present application performs multiple quantum state reading measurement experiments on a single quantum bit to obtain a characterization data set, and determines two Gaussian circular spot distributions based on the characterization data set, thereby determining the distribution ratio difference of the number of measurement data located on both sides of the center straight line, and then compares the distribution ratio difference with the first threshold limit. When the distribution ratio difference is greater than the first threshold limit, the data classification anomaly can be quickly judged, thereby inferring that there are abnormal data in multiple read data, and further indicating that the experimental parameters in the quantum state reading measurement experiment are abnormal, so that the experimenter knows that the experimental parameters need to be adjusted to conduct the experiment, avoiding the problem of slow measurement efficiency caused by continuing to operate under incorrect experimental parameters. At the same time, in this case, there is no need to judge the classification fidelity, which improves the speed of judgment. When the distribution ratio difference is less than or equal to the first threshold limit, the classification anomaly of the data is further judged based on the classification fidelity and the second threshold limit.

[0041] The judgment device, storage medium and quantum computer provided by the present invention include the steps of the above judgment method, and therefore have the same beneficial effects, which will not be described in detail herein. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 A first flow chart of a method for determining abnormal data classification provided in an embodiment of the present application;

[0043] Figure 2 A second flow chart of the method for determining abnormal data classification provided in an embodiment of the present application;

[0044] Figure 3 A third flow chart of the method for determining abnormal data classification provided in an embodiment of the present application;

[0045] Figure 4 A fourth flow chart of the method for determining abnormal data classification provided in an embodiment of the present application;

[0046] Figure 5 A fifth flow chart of the method for determining abnormal data classification provided in an embodiment of the present application;

[0047] Figure 6 A schematic diagram of a structure of a device for determining abnormal data classification provided in an embodiment of the present application;

[0048] Figure 7 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0049] In order to enable those skilled in the art to better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this application. The embodiments described below with reference to the drawings are exemplary and are only used to explain the present application, and cannot be interpreted as limitations on the present application.

[0050] Figure 1 The first flow chart of the method for determining abnormal data classification provided by the embodiment of the present application is provided in the embodiment of the present invention. Figure 1 As shown: This embodiment of the application discloses a method for determining abnormal data classification, including:

[0051] Step S1, performing multiple quantum state reading measurement experiments on a single quantum bit to obtain multiple measurement data, wherein the measurement data is scattered point data in an IQ coordinate system, and the scattered point data in multiple IQ coordinate systems constitute a characterization data set;

[0052] Step S2, determining two Gaussian circular spot distributions on the plane of the IQ coordinate system according to the characterization data set, wherein the two Gaussian circular spots are Gaussian circular spots whose quantum state measurement results are in state 1 and state 0 respectively;

[0053] Step S3, determining the distribution ratio difference of the number of measurement data located on both sides of the center straight line according to the distribution of the two Gaussian spots, wherein the center straight line is a straight line connecting the two centers of the two Gaussian spots;

[0054] Step S4: if the distribution ratio difference is greater than the first threshold limit, the data classification is judged to be abnormal; if the distribution ratio difference is less than or equal to the first threshold limit, the data classification is judged to be abnormal based on the classification fidelity and the second threshold limit.

[0055] pass Figure 1The method shown is to obtain a characterization data set by performing multiple quantum state reading measurement experiments on a single quantum bit, and then determine two Gaussian circular spot distributions based on the characterization data set, thereby determining the distribution ratio difference of the number of measurement data located on both sides of the center straight line, and then compare the distribution ratio difference with the first threshold limit. When the distribution ratio difference is greater than the first threshold limit, the data classification abnormality can be quickly judged, thereby inferring that there are abnormal data in multiple read data, and further indicating that the experimental parameters in the quantum state reading measurement experiment are abnormal, so that the experimenter knows that the experimental parameters need to be adjusted to conduct the experiment, avoiding the problem of slow measurement efficiency caused by continuing to operate under incorrect experimental parameters. At the same time, in this case, there is no need to judge the classification fidelity, which improves the speed of judgment. When the distribution ratio difference is less than or equal to the first threshold limit, the classification abnormality of the data is further judged based on the classification fidelity and the second threshold limit.

[0056] Specifically, multiple quantum state reading and measurement experiments are performed on a single quantum bit, usually by applying a driving signal on the driving line of the quantum bit, and the driving signal is used to drive the quantum state of the quantum bit to a target state, such as a 0 state, a 1 state, or other superposition state; then a measurement signal is applied to the reading bus of the quantum bit to read the current quantum state information of the quantum bit; and the quantum state is usually a probability distribution, so several repeated measurements are performed.

[0057] The embodiments of the present application are further described below through specific examples.

[0058] For the above step S1, multiple quantum state reading measurement experiments are performed on a single quantum bit to obtain multiple measurement data, wherein the measurement data is scattered point data in the IQ coordinate system, and the scattered point data in multiple IQ coordinate systems constitute a characterization data set. Specifically, the signal collected by the measuring device each time is an analog signal carrying quantum state information, and a scattered point data of the corresponding IQ coordinate system is obtained by demodulating the analog signal; the number of scattered point data corresponds to the number of measurements.

[0059] Specifically, in step S1, the plurality of measurement data may include measurement data of quantum states being 1 state and 0 state.

[0060] Figure 2 A second flow chart of the method for determining abnormal data classification provided by an embodiment of the present invention is shown in FIG. Figure 2 As shown, in an optional implementation of this embodiment, the above step S1 is refined into the following steps, namely step S11 to step S13, which are specifically as follows:

[0061] Step S11, performing multiple quantum state reading measurement experiments on a single quantum bit to obtain multiple quantum bit original reading signals when the quantum bit is in the 0 state and the 1 state.

[0062] Exemplarily, a single quantum bit is subjected to 2000 quantum state reading measurement experiments to obtain 2000 original quantum bit reading signals when the quantum bit is in state 0 and state 1. Specifically, under the first experimental parameter, the single quantum bit is subjected to 1000 quantum state reading measurements to obtain 1000 quantum bit original reading signals when the quantum bit is in state 0; under the second experimental parameter, the single quantum bit is subjected to 1000 quantum state reading measurements to obtain 1000 quantum bit original reading signals when the quantum bit is in state 1. The first experimental parameter and the second experimental parameter include parameters such as the power, amplitude, pulse width, and frequency of the driving signal.

[0063] Step S12, performing digital signal processing on the original quantum bit read signal to obtain a corresponding complex signal containing quantum bit information.

[0064] It should be noted that the original qubit read signal is an analog signal, and the form of the analog signal includes but is not limited to: It is understood by those skilled in the art that this form is a general representation of an analog signal, so the parameters in this representation are not described here. Digital signal processing is performed on the signal to obtain a corresponding complex signal containing quantum bit state information, including but not limited to mixing and / or integrating the signal. Optionally, this embodiment performs mixing and integrating processing on the signal in sequence, and the mixing process is The integration process is The integration result signal is a complex signal containing the quantum bit state information.

[0065] Step S13, performing complex number decomposition on the complex signal to obtain scattered point data in an IQ coordinate system, wherein the scattered point data in multiple IQ coordinate systems constitute a characterization data set.

[0066] Specifically, the complex decomposition of the complex signal can be expressed as: Then, I and Q are used as the horizontal coordinate and vertical coordinate of the IQ coordinate system respectively, and a corresponding scattered point data is obtained.

[0067] For the above step S2, that is, based on the characterization data set, two Gaussian circular spot distributions on the plane are determined, wherein the two Gaussian circular spots are Gaussian circular spots whose quantum state measurement results are in the 1 state and the 0 state respectively.

[0068] For the convenience of the following description, the Gaussian circular spot whose quantum state measurement result is in the 0 state is recorded as the first Gaussian circular spot, and the Gaussian circular spot whose quantum state measurement result is in the 1 state is recorded as the second Gaussian circular spot.

[0069] For the above step S3, that is, based on the distribution of the two Gaussian spots, the distribution ratio difference of the number of measurement data located on both sides of the center line is determined, wherein the center line is a line connecting the two centers of the two Gaussian spots.

[0070] Figure 3 A third flow chart of the method for determining abnormal data classification provided by an embodiment of the present invention is shown in FIG. Figure 3 As shown, in an optional implementation of this embodiment, the above step S3 is refined into the following steps, namely step S31-step S33, which are specifically as follows:

[0071] Step S31, according to the distribution of two Gaussian spots, respectively counting the number of measurement data on both sides of the center line, wherein the center line is a line connecting the two centers of the two Gaussian spots.

[0072] Specifically, according to the distribution of two Gaussian circular spots, the coordinate data of the centers of the two Gaussian circular spots are determined; then, according to the coordinate data of the two centers, the position of the center straight line is determined; finally, the number of measurement data on both sides of the center straight line is counted.

[0073] Step S32, determining a first distribution ratio of the number of measurement data on one side of the center line and a second distribution ratio of the number of measurement data on the other side of the center line according to the number of measurement data on both sides of the center line.

[0074] Exemplarily, when the number of measurement data on one side of the straight line at the center of the circle is A and the number of measurement data on the other side of the straight line at the center of the circle is B, the first distribution ratio is A / (A+B) and the second distribution ratio is B / (A+B). In a specific example, when the number of measurement data on one side of the straight line at the center of the circle is 1000 and the number of measurement data on the other side of the straight line at the center of the circle is also 1000, the first distribution ratio is 50% and the second distribution ratio is also 50%.

[0075] Step S33, determining a distribution ratio difference according to a difference between the first distribution ratio and the second distribution ratio;

[0076] The first distribution ratio and the second distribution ratio are determined by the following formula:

[0077]

[0078] Among them, k represents the quantum state of the bit, I represents the in-phase component of the IQ coordinate, Q represents the orthogonal component of the IQ coordinate, M represents the number of times the measurement experiment is read, Bool represents the Boolean value, O(k|I,Q) represents the distribution ratio, and a and b represent the constants in the equation of the straight line between the centers of the two Gaussian spots y=ax+b.

[0079] Exemplarily, the first distribution ratio is recorded as O1 and the second distribution ratio is recorded as O2, then the distribution ratio difference is the absolute value of O1-O2. In a specific example, when the first distribution ratio is 51% and the second distribution ratio is 49%, the distribution ratio difference is 2%.

[0080] For step S4, if the distribution ratio difference is greater than the first threshold limit, the read data classification is judged to be abnormal; if the distribution ratio difference is less than or equal to the first threshold limit, the data classification abnormality is judged based on the classification fidelity and the second threshold limit.

[0081] Specifically, the first threshold limit is set according to the actual experience of technical personnel in this field. For example, it is set to 2.5%. Then, when the distribution ratio difference is greater than 2.5%, it is judged that the read data classification is abnormal, and it is necessary to adjust the experimental parameters and re-conduct the quantum state reading measurement experiment.

[0082] Figure 4 A fourth flow chart of the method for determining abnormal data classification provided by an embodiment of the present invention is shown in FIG. Figure 4 As shown, in an optional implementation of this embodiment, the above step S4 is refined into the following steps, namely step S41-step S43, which are specifically as follows:

[0083] Step S41, if the distribution ratio difference is greater than a first threshold, it is determined that the data classification is abnormal;

[0084] Step S42, if the distribution ratio difference is less than or equal to the first threshold limit, and the classification fidelity is less than the second threshold limit, then it is determined that the data classification is abnormal;

[0085] Step S43: If the distribution ratio difference is less than or equal to the first threshold limit, and the classification fidelity is greater than or equal to the second threshold limit, the classification anomaly of the data is judged based on the outlier distribution ratio and the third threshold limit.

[0086] Specifically, the second threshold limit is set based on the actual experience of technical personnel in this field. For example, if it is set to 85%, then when the classification fidelity is less than 85%, it is judged that the read data classification is abnormal, and it is necessary to adjust the experimental parameters and re-conduct the quantum state reading measurement experiment. If the following experiment is then carried out, the progress of the entire experiment will be slow.

[0087] Specifically, the classification fidelity here includes the classification fidelity when the measurement result of the quantum state is in the 1 state and the classification fidelity when the measurement result of the quantum state is in the 0 state.

[0088] Figure 5 A fifth flow chart of the method for determining abnormal data classification provided by an embodiment of the present invention is shown in FIG. Figure 5 As shown, in an optional implementation of this embodiment, the above step S43 is refined into the following steps, namely step S431-step S432, which are specifically as follows:

[0089] Step S431, determining the outlier distribution ratio according to two Gaussian spot distributions.

[0090] Step S432: If the distribution ratio difference is less than or equal to the first threshold limit, and the classification fidelity is greater than or equal to the second threshold limit, and the outlier distribution ratio is greater than the third threshold limit, then the data classification is judged to be abnormal.

[0091] Further, if the abnormal value distribution ratio is greater than a third threshold limit, the data classification is judged to be abnormal, and the power parameter of the measurement signal applied in the measurement experiment is abnormal according to the judgment result feedback.

[0092] Furthermore, if the distribution ratio difference is less than or equal to a first threshold limit, and the classification fidelity is greater than or equal to a second threshold limit, and the outlier distribution ratio is less than or equal to a third threshold limit, then the data classification is judged to be normal.

[0093] Specifically, the setting of the third threshold limit is based on the actual experience of technical personnel in this field. For example, if it is set to 1%, then when the outlier distribution ratio is greater than 1%, the read data classification is judged to be abnormal. Furthermore, the power parameter of the feedback reading measurement experiment is abnormal, and it is necessary to adjust the power in the experimental parameters to re-perform the quantum state reading measurement experiment. If the following experiment is then carried out, the progress of the entire experiment will be slow. If the outlier distribution ratio is less than or equal to 1%, the read data classification is judged to be normal.

[0094] In an optional implementation of this embodiment, step S431, determining the outlier distribution ratio according to two Gaussian spot distributions, is refined into the following steps:

[0095] According to the distribution of two Gaussian circular spots, the coordinate data of the centers of the two Gaussian circular spots are determined;

[0096] According to the coordinate data of the two circle centers, determine the equation of the center line and the distance from the 3σ outer data point to the center line, which is recorded as the first distance;

[0097] According to the Gaussian spot radius in state 1 of the measurement result of the first distance and the quantum state, the distribution ratio of the number of data points outside 3σ whose first distance is greater than the radius is determined, that is, the outlier distribution ratio.

[0098] The reason for selecting data points outside 3σ in this application is that according to the three sigma criterion (3σ criterion) of statistics, if a data distribution is approximately normal, then about 68% of the data values ​​are within one standard deviation of the mean, about 95% are within two standard deviations, and about 99.7% are within three standard deviations. Therefore, if any data point exceeds 3 times the standard deviation, then these points are basically outliers.

[0099] Furthermore, the outlier distribution ratio is determined as:

[0100]

[0101] in,

[0102] r1 represents the radius of the Gaussian spot when the measurement result of the quantum state is in state 1; N represents the number of data points outside 3σ of the Gaussian model; Distance represents the distance from the calculated data point to the equation of the center line of the circle; Bool represents the Boolean value; Outlier represents the outlier distribution ratio; a and b represent the constants in the equation of the center line of the two Gaussian spots y=ax+b.

[0103] It should be noted that in the present application, if any one of the three data, distribution ratio difference, classification fidelity and outlier distribution ratio, does not meet the requirements of the corresponding preset threshold limit, the data classification can be judged to be abnormal. In the present application, the distribution ratio difference, classification fidelity and outlier distribution ratio are compared with the corresponding preset threshold limits in turn to determine whether the data classification is abnormal. This can more quickly determine the data classification abnormality. For example, when the distribution ratio difference does not meet the corresponding preset threshold limit, it is directly judged to be abnormal without determining the classification fidelity and the outlier distribution ratio.

[0104] Exemplarily, when the judgment method of the present invention is used to judge the abnormal classification data read by the superconducting quantum bit, the first threshold limit is pre-set to 2.5%, the second threshold limit is 85%, and the third threshold limit is 1%; in a specific example, when the distribution ratio difference obtained by the method of the present invention is 2.6%, the data classification is directly judged to be abnormal, and there is no need to determine the classification fidelity and the outlier distribution ratio; in a specific example, when the distribution ratio difference obtained by the method of the present invention is 2.4%, it is necessary to determine the classification fidelity for further judgment, and when the classification fidelity is 80%, the data classification is directly judged to be abnormal, and there is no need to determine the outlier distribution ratio; in a specific example, when the distribution ratio difference obtained by the method of the present invention is 2.4%, the classification fidelity is 88%, the outlier distribution ratio needs to be determined for further judgment, and when the outlier distribution ratio is 2%, the data classification is judged to be abnormal, and the feedback power parameter is abnormal; in a specific example, when the distribution ratio difference obtained by the method of the present invention is 2.4%, the classification fidelity is 88%, and the outlier distribution ratio is 0.05%, the data classification is judged to be normal.

[0105] Based on the same inventive concept, according to the data classification abnormality judgment method provided in the above embodiment of the present application, the embodiment of the present application also provides a data classification abnormality judgment device. Figure 6 As shown, Figure 6 A schematic diagram of a structure of a device for determining abnormal data classification provided in an embodiment of the present application, the device includes the following modules:

[0106] Acquisition module 10: used to perform multiple quantum state reading measurement experiments on a single quantum bit to obtain multiple reading data, wherein the reading data is scattered point data of an IQ coordinate system, and the scattered point data of multiple IQ coordinate systems constitute a characterization data set;

[0107] The first determination module 20 is used to determine two Gaussian circular spot distributions on the plane of the IQ coordinate system according to the characterization data set, wherein the two Gaussian circular spots are Gaussian circular spots whose quantum state measurement results are in the 1 state and the 0 state respectively;

[0108] The second determination module 30 is used to determine the distribution ratio difference of the number of measurement data located on both sides of the center straight line according to the distribution of the two Gaussian spots, wherein the center straight line is a straight line connecting the two centers of the two Gaussian spots;

[0109] Judgment module 40: used to judge that the classification of the read data is abnormal if the distribution ratio difference is greater than the first threshold limit; if the distribution ratio difference is less than or equal to the first threshold limit, judge the classification abnormality of the read data based on the classification fidelity and the second threshold limit.

[0110] Optionally, the above-mentioned determination module 40 specifically includes:

[0111] A first judgment unit is used to judge that the classification of the read data is abnormal if the distribution ratio difference is greater than a first threshold limit; if the distribution ratio difference is less than or equal to the first threshold limit, a second judgment unit is used to perform a second judgment;

[0112] A second judgment unit is used to judge that the classification of the read data is abnormal if the classification fidelity is less than a second threshold limit; if the classification fidelity is greater than or equal to the second threshold limit, a third judgment unit is used to perform a third judgment;

[0113] The third judgment unit is used to judge that the read data classification is abnormal and feedback that the power parameter of the reading measurement experiment is abnormal if the abnormal value distribution ratio is greater than a third threshold limit, and judge that the read data classification is normal if the abnormal value distribution ratio is less than or equal to the third threshold limit.

[0114] The above-mentioned judgment module 40 further includes a first determination unit, which is used to determine the outlier distribution ratio according to the two Gaussian spot distributions. Furthermore, the first determination unit is specifically used to:

[0115] According to the distribution of two Gaussian circular spots, the coordinate data of the centers of the two Gaussian circular spots are determined;

[0116] According to the coordinate data of the two circle centers, determine the equation of the center line and the distance from the 3σ outer data point to the center line, which is recorded as the first distance;

[0117] According to the Gaussian spot radius in state 1 of the measurement result of the first distance and the quantum state, the distribution ratio of the number of data points outside 3σ whose first distance is greater than the radius is determined, that is, the outlier distribution ratio.

[0118] Specifically, the formula for determining the outlier distribution ratio is as follows:

[0119]

[0120] in,

[0121] r1 represents the radius of the Gaussian spot when the measurement result of the quantum state is in state 1; N represents the number of data points outside 3σ of the Gaussian model; Distance represents the distance from the calculated data point to the equation of the center line of the circle; Bool represents the Boolean value; Outlier represents the outlier distribution ratio; a and b represent the constants in the equation of the center line of the two Gaussian spots y=ax+b.

[0122] Optionally, the second determining module 30 specifically includes:

[0123] The first statistical unit is used to count the number of measurement data on both sides of the center straight line according to the two Gaussian circular spot distributions;

[0124] A second determining unit is used to determine a first distribution ratio of the number of measurement data on one side of the center line and a second distribution ratio of the number of measurement data on the other side of the center line according to the number of measurement data on both sides of the center line;

[0125] The third determining unit is used to determine the distribution ratio difference according to the difference between the first distribution ratio and the second distribution ratio.

[0126] Specifically, the first distribution ratio and the second distribution ratio are determined by the following formula:

[0127]

[0128] Wherein, k is the quantum state of the bit, I is the in-phase component of the IQ coordinate, Q is the orthogonal component of the IQ coordinate, M is the number of times the measurement experiment is read, Bool represents the Boolean value, O(k|I,Q) is the distribution ratio, and a and b are constants in the equation of the straight line between the centers of the two Gaussian spots y=ax+b.

[0129] Based on the same inventive concept, according to the method for determining abnormal data classification provided by the above-mentioned embodiment of the present application, the embodiment of the present application further provides an electronic device, such as Figure 7 As shown, Figure 7A schematic diagram of the structure of an electronic device provided in an embodiment of the present application includes a processor 101, a communication interface 102, a memory 103 and a communication bus 104, wherein the processor 101, the communication interface 102, and the memory 103 communicate with each other through the communication bus 104.

[0130] Memory 103, used for storing computer programs;

[0131] The processor 101 is used to execute the program stored in the memory 103, and implements the following steps:

[0132] Perform multiple quantum state reading measurement experiments on a single quantum bit to obtain multiple measurement data, wherein the measurement data is scattered point data in an IQ coordinate system, and the scattered point data in multiple IQ coordinate systems constitute a characterization data set;

[0133] Determine two Gaussian circular spot distributions on the plane of the IQ coordinate system according to the characterization data set, wherein the two Gaussian circular spots are Gaussian circular spots whose quantum state measurement results are in state 1 and state 0 respectively;

[0134] According to the distribution of the two Gaussian circular spots, the distribution ratio difference of the number of measurement data located on both sides of the center straight line is determined, wherein the center straight line is a straight line connecting the two centers of the two Gaussian circular spots;

[0135] If the distribution ratio difference is greater than the first threshold limit, the data classification is judged to be abnormal; if the distribution ratio difference is less than or equal to the first threshold limit, the data classification abnormality is judged based on the classification fidelity and the second threshold limit.

[0136] The communication bus mentioned in the above electronic device can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0137] The communication interface is used for communication between the above electronic device and other devices.

[0138] The memory may include a random access memory (RAM) or a non-volatile memory (NVM), such as at least one disk memory. Optionally, the memory may also be at least one storage device located away from the aforementioned processor.

[0139] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0140] Based on the same inventive concept, according to the method for determining a data classification anomaly provided in the above-mentioned embodiments of the present application, the embodiments of the present application also provide a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of any of the above-mentioned methods for determining a data classification anomaly are implemented.

[0141] Based on the same inventive concept, according to the method for determining an abnormality in data classification provided in the above-mentioned embodiment of the present application, the embodiment of the present application also provides a computer program product comprising instructions, which, when executed on a computer, enables the computer to execute any method for determining an abnormality in data classification provided in the above-mentioned embodiment.

[0142] Based on the same inventive concept, an embodiment of the present application further provides a quantum computer, comprising the above-mentioned judgment device, or using the above-mentioned judgment method to judge whether the measurement data classification of quantum bits is abnormal.

[0143] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive Solid State Disk (SSD)), etc.

[0144] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0145] Each embodiment in this specification is described in a related manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for embodiments such as devices, electronic devices, computer-readable storage media, and computer program products, since they are basically similar to method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0146] The above description is only a preferred embodiment of the present application and is not intended to limit the protection scope of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application are included in the protection scope of the present application.

Claims

1. A method for determining abnormal data classification, characterized in that: include: Perform multiple quantum state reading measurement experiments on a single quantum bit to obtain multiple measurement data, wherein the measurement data is scattered point data in an IQ coordinate system, and the scattered point data in multiple IQ coordinate systems constitute a characterization data set; Determine two Gaussian circular spot distributions on the plane of the IQ coordinate system according to the characterization data set, wherein the two Gaussian circular spots are Gaussian circular spots whose quantum state measurement results are in state 1 and state 0 respectively; According to the distribution of the two Gaussian circular spots, the distribution ratio difference of the number of measurement data located on both sides of the center straight line is determined, wherein the center straight line is a straight line connecting the two centers of the two Gaussian circular spots; If the distribution ratio difference is greater than the first threshold limit, the data classification is judged to be abnormal; if the distribution ratio difference is less than or equal to the first threshold limit, the data classification abnormality is judged based on the classification fidelity and the second threshold limit.

2. The determination method according to claim 1, characterized in that: The step of judging the classification anomaly of the data based on the classification fidelity and the second threshold limit comprises: If the classification fidelity is less than a second threshold limit, determining that the data classification is abnormal; If the classification fidelity is greater than or equal to the second threshold, the classification anomaly of the data is judged based on the outlier distribution ratio and the third threshold.

3. The determination method according to claim 2, characterized in that: The step of judging the classification abnormality of the read data based on the abnormal value distribution ratio and the third threshold limit includes: Determine the outlier distribution ratio based on two Gaussian spot distributions; If the outlier distribution ratio is greater than the third threshold limit, the data classification is judged to be abnormal.

4. The determination method according to claim 3, characterized in that: If the abnormal value distribution ratio is greater than the third threshold, after the step of determining that the data classification is abnormal, the method further includes: The power parameter of the measurement signal applied in the measurement experiment is abnormal according to the judgment result feedback.

5. The determination method according to claim 3, characterized in that: The step of determining the outlier distribution ratio based on two Gaussian spot distributions includes: According to the distribution of two Gaussian circular spots, the coordinate data of the centers of the two Gaussian circular spots are determined; According to the coordinate data of the two circle centers, determine the equation of the center line and the distance from the 3σ outer data point to the center line, which is recorded as the first distance; According to the Gaussian spot radius in state 1 of the measurement result of the first distance and the quantum state, the distribution ratio of the number of data points outside 3σ whose first distance is greater than the radius is determined, that is, the outlier distribution ratio.

6. The determination method according to claim 5, characterized in that: The outlier distribution ratio is determined as: in, r1 represents the radius of the Gaussian spot when the measurement result of the quantum state is in state 1; N represents the number of data points outside 3σ of the Gaussian model; Distance represents the distance from the calculated data point to the equation of the center line of the circle; Bool represents the Boolean value; Outlier represents the outlier distribution ratio; a and b represent the constants in the equation of the center line of the two Gaussian spots y=ax+b.

7. The determination method according to claim 1, characterized in that: The step of determining the distribution ratio difference of the number of measurement data located on both sides of the center straight line according to the two Gaussian circular spot distributions includes: According to the two Gaussian circular spot distributions, the number of measurement data on both sides of the center straight line is counted respectively; Determine, based on the number of measurement data on both sides of the straight line, a first distribution ratio of the number of measurement data on one side of the straight line and a second distribution ratio of the number of measurement data on the other side of the straight line; A distribution ratio difference is determined according to a difference between the first distribution ratio and the second distribution ratio.

8. A device for determining abnormal data classification, characterized in that: include: Acquisition module: used to perform multiple quantum state reading measurement experiments on a single quantum bit to obtain multiple measurement data, wherein the measurement data is scattered point data of an IQ coordinate system, and the scattered point data of multiple IQ coordinate systems constitute a characterization data set; A first determination module is used to determine two Gaussian circular spot distributions on the plane of the IQ coordinate system according to the characterization data set, wherein the two Gaussian circular spots are Gaussian circular spots whose quantum state measurement results are in the 1 state and the 0 state respectively; The second determination module is used to determine the distribution ratio difference of the number of measurement data located on both sides of the center straight line according to the distribution of two Gaussian circular spots, wherein the center straight line is a straight line connecting the two centers of the two Gaussian circular spots; Judgment module: used to judge that the data classification is abnormal if the distribution ratio difference is greater than the first threshold limit; if the distribution ratio difference is less than or equal to the first threshold limit, judge the data classification abnormality based on the classification fidelity and the second threshold limit.

9. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, wherein the computer program is configured to execute the determination method according to any one of claims 1 to 7 when running.

10. A quantum computer, characterized in that: The method comprises the judgment device as described in claim 8, or uses the judgment method as described in any one of claims 1 to 7 to judge whether the measurement data classification of the quantum bit is abnormal.

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