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

Abstract

The application discloses a data classification anomaly judgment method and device, a storage medium and a quantum computer. A characteristic data set is obtained by performing multiple quantum state reading measurement experiments on a single quantum bit. Two Gaussian circular spot distributions are determined according to the characteristic data set, so that the distribution ratio difference of the number of measurement data located on both sides of the straight line of the circle center is determined. The distribution ratio difference is compared with a first threshold limit. When the distribution ratio difference is greater than the first threshold limit, the data classification anomaly can be quickly judged, so that it is inferred that there is abnormal data in the multiple reading data. Further, it is indicated 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 for the experiment, thereby avoiding the problem of slow measurement efficiency caused by continuing operation under the wrong experimental parameters. Meanwhile, in this case, the classification fidelity does not need to be judged, and the judgment speed is improved.

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 data classification anomalies. Background Art

[0002] Quantum computing relies on a quantum processing unit (QPU), which consists of a coupled qubit (qubit) and a readout cavity. The qubit's state is read out through the readout cavity. As a physical measurement method for superconducting qubits, readout technology has unique physical properties. It primarily uses the readout cavity to read the qubit's state. A signal carrying the qubit's state information is transmitted via a readout circuit to a measurement device. The measurement device then processes the collected signal data to determine the qubit's state, or quantum state.

[0003] At present, the classification and identification of quantum states is mainly based on the binary classification of state 0 and state 1. 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 state 0 and state 1, state 2 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 and continue the experiment, the subsequent measurement results will be inaccurate and meaningless, and repeated measurements are required, which reduces the measurement efficiency. Therefore, there is an urgent need 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 any form of implication 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 data classification anomalies, comprising:

[0008] 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;

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

[0010] Based on the distribution of the two Gaussian spots, determine the distribution ratio difference of the number of measurement data on both sides of the center line, where the center line is the line connecting the two centers of the two Gaussian 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, determining that the data classification is abnormal;

[0014] If the classification fidelity is greater than or equal to the second threshold, the classification abnormality 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 a third threshold value, the data classification is determined to be abnormal.

[0018] The above judgment method further includes, if the outlier distribution ratio is greater than the third threshold, after the step of judging that the data classification is abnormal, the following steps are further included:

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

[0020] In the above 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 the two Gaussian spots, the coordinate data of the centers of the two Gaussian spots are determined;

[0022] Based on the two center coordinate data, 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 first distance and the Gaussian spot radius of the measurement result of the quantum state in the 1 state, a distribution ratio of the number of data points in which the first distance is greater than the radius in the 3sigma outside data points is determined, that is, an outlier distribution ratio.

[0024] The determination method as described above further determines the outlier distribution ratio as:

[0025]

[0026] Wherein,

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

[0028] The determination method as described above further includes the following steps of determining the distribution ratio difference of the number of measurement data on both sides of the center line of the circle according to the two Gaussian spot distributions:

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

[0030] According to the number of measurement data on both sides of the center line of the circle, a first distribution ratio of the number of measurement data on one side of the center line of the circle and a second distribution ratio of the number of measurement data on the other side of the center line of the circle are determined.

[0031] According to the difference between the first distribution ratio and the second distribution ratio, the distribution ratio difference is determined.

[0032] The second aspect of the present application provides a data classification anomaly determination device, which comprises:

[0033] The acquisition module is configured to perform multiple quantum state reading measurement experiments on a single quantum bit to obtain multiple measurement data, wherein the measurement data is IQ coordinate system scatter point data, and the multiple IQ coordinate system scatter point data constitute a representation data set.

[0034] The first determination module is configured to determine two Gaussian spot distributions on the plane of the IQ coordinate system according to the representation data set, wherein the two Gaussian spots are Gaussian spots of the measurement result of the quantum state in the 1 state and the 0 state.

[0035] The second determination module is configured to determine a distribution ratio difference of the number of measurement data on both sides of the center line of the circle according to the two Gaussian spot distributions, wherein the center line of the circle is a straight line connecting the centers of the two Gaussian spots.

[0036] The judging module is used for judging data classification anomaly if the distribution ratio difference is greater than the first threshold limit, and judging data classification anomaly according to the classification fidelity and the second threshold limit if the distribution ratio difference is less than or equal to the first threshold limit.

[0037] The third aspect of the present application provides a computer readable storage medium, wherein the storage medium stores a computer program, and the computer program is arranged to execute the judging method.

[0038] The fourth aspect of the present application provides a quantum computer, which comprises the judging device or uses the judging method to judge the measurement data of the quantum bit.

[0039] The present application has the following beneficial effects:

[0040] The present application obtains a characterization data set by performing multiple quantum state reading measurement experiments on a single quantum bit, determines two Gaussian circular spot distributions according to the characterization data set, determines the distribution ratio difference of the number of measurement data located on the two sides of the straight line of the circle center, compares the distribution ratio difference with the first threshold limit, and quickly judges the data classification anomaly when the distribution ratio difference is greater than the first threshold limit, thereby inferring that there is abnormal data in the multiple reading 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 for the experiment, avoids the problem of slow measurement efficiency caused by continuing operation under the wrong experimental parameters, and simultaneously, in this case, there is no need to judge the classification fidelity, thereby improving the judging speed.

[0041] The judging device, the storage medium and the quantum computer provided by the present application comprise the steps of the judging method, and therefore have the same beneficial effects, which will not be described herein. BRIEF DESCRIPTION OF DRAWINGS

[0042] Figure 1 The first flowchart of the data classification anomaly judging method provided by the present application embodiment;

[0043] Figure 2 The second flowchart of the data classification anomaly judging method provided by the present application embodiment;

[0044] Figure 3 The third flowchart of the data classification anomaly judging method provided by the present application embodiment;

[0045] Figure 4 The fourth flowchart of the data classification anomaly judging method provided by the present application embodiment;

[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 by 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 this application, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making 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 this application, and cannot be interpreted as limiting this 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 data classification anomalies, 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 spot distributions on the plane of the IQ coordinate system based on the characterization data set, wherein the two Gaussian spots are Gaussian 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 line based on the two Gaussian spot distributions, wherein the center line is a line connecting the two centers of the two Gaussian spots;

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

[0055] pass Figure 1The method shown, by performing multiple quantum state reading measurement experiments on a single quantum bit, obtains a characterization data set, determines two Gaussian circular spot distributions according to the characterization data set, thereby determines the distribution ratio difference of the number of measurement data located on both sides of the straight line of the circle center, 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 is abnormal data in the multiple reading data, 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 for the experiment, avoiding the problem of slow measurement efficiency caused by continuing operation under wrong experimental parameters, at the same time, in this case, there is no need to judge the classification fidelity, improving 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 according to the classification fidelity and the second threshold limit.

[0056] Specifically, multiple quantum state reading measurement experiments are performed on a single quantum bit, usually a driving signal is applied on the driving line of the quantum bit, the driving signal is used to drive the quantum state of the quantum bit to the target state, such as 0 state, 1 state or other superposition state; then a measurement signal is applied on 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 will be further described below through specific examples.

[0058] For the above step S1, that is, performing multiple quantum state reading measurement experiments on a single quantum bit, obtaining multiple measurement data, wherein the measurement data is IQ coordinate system scatter point data, and the multiple IQ coordinate system scatter point data constitute a characterization data set. Specifically, the signal collected by the measurement device each time is an analog signal carrying quantum state information, and by demodulating the analog signal, one scatter point data of the IQ coordinate system is obtained; the number of scatter point data corresponds to the number of measurements.

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

[0060] Figure 2 The second flowchart of the data classification anomaly judgment method provided by the embodiments of the present application is shown as follows. Figure 2 As shown in an optional embodiment of the present embodiment, the above step S1 is refined into the following steps, that is, steps S11-S13, which are shown as follows:

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

[0062] For example, a quantum state reading measurement experiment is performed on a single qubit 2000 times to obtain 2000 qubit raw read signals when the qubit is in state 0 and state 1. Specifically, under first experimental parameters, the experiment is performed on the single qubit 1000 times to obtain 1000 qubit raw read signals when the qubit is in state 0; and under second experimental parameters, the experiment is performed on the single qubit 1000 times to obtain 1000 qubit raw read signals when the qubit is in state 1. The first and second experimental parameters include parameters such as the power, amplitude, pulse width, and frequency of the drive 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 will be 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 decomposition on the complex signal to obtain scattered point data in an IQ coordinate system. 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 data is obtained.

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

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

[0069] Regarding 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 flowchart of the method for judging data classification anomaly provided by the embodiments of the present application is shown in FIG. 3, in which, in an optional implementation of the present embodiment, the step S3 is refined into the following steps, i.e. steps S31-S33, as shown below. Figure 3

[0071] In step S31, the number of measurement data on both sides of the center straight line of the two Gaussian circular spots is counted according to the distribution of the two Gaussian circular spots, wherein the center straight line is a straight line connecting the two centers of the two Gaussian circular spots.

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

[0073] In step S32, the first distribution ratio of the number of measurement data on one side of the center straight line and the second distribution ratio of the number of measurement data on the other side of the center straight line are determined according to the number of measurement data on both sides of the center straight line.

[0074] For example, when the number of measurement data on one side of the center straight line is A and the number of measurement data on the other side of the center straight line 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 center straight line is 1000 and the number of measurement data on the other side of the center straight line is also 1000, the first distribution ratio is 50% and the second distribution ratio is also 50%.

[0075] In step S33, the distribution ratio difference is determined according to the difference between the first distribution ratio and the second distribution ratio.

[0076] The determination formula of the first distribution ratio and the second distribution ratio is as follows:

[0077]

[0078] Wherein k represents the quantum state of bits, I represents the in-phase component of IQ coordinates, Q represents the quadrature component of IQ coordinates, M represents the number of reading measurement experiments, Bool represents the Boolean value, O(k|I,Q) represents the distribution ratio, and a and b represent constants in the equation y=ax+b of the center straight line of the two Gaussian circular spots.

[0079] For example, the first distribution ratio is denoted as O1 and the second distribution ratio is denoted as O2, and 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] If the distribution ratio difference is greater than the first threshold limit, it is determined that the classification of the read data is abnormal; if the distribution ratio difference is less than or equal to the first threshold limit, the classification of the data is determined according to the classification fidelity and the second threshold limit.

[0081] Specifically, the first threshold limit is set according to the actual experience of those skilled in the art, for example, set to 2.5%, so that when the distribution ratio difference is greater than 2.5%, it is determined that the classification of the read data is abnormal, and the experimental parameters need to be adjusted to re-perform the quantum state reading measurement experiment.

[0082] Figure 4 A fourth flowchart of the data classification abnormality determination method provided by the embodiment of the present application is shown in FIG. 4. Figure 4 As shown in the figure, in an optional embodiment of the present embodiment, the above step S4 is refined into the following steps, i.e., steps S41-S43, which are specifically shown as follows:

[0083] Step S41, if the distribution ratio difference is greater than the first threshold limit, 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, 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 of the data is determined according to the abnormal value distribution ratio and the third threshold limit.

[0086] Specifically, the second threshold limit is set according to the actual experience of those skilled in the art, for example, set to 85%, so that when the classification fidelity is less than 85%, it is determined that the classification of the read data is abnormal, and the experimental parameters need to be adjusted to re-perform the quantum state reading measurement experiment, which will cause the whole experiment to be slow if the following experiment is continued.

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

[0088] Figure 5 A fifth flowchart of the data classification abnormality determination method provided by the embodiment of the present application is shown in FIG. 5. Figure 5 As shown in the figure, in an optional embodiment of the present embodiment, the above step S43 is refined into the following steps, i.e., steps S431-S432, which are specifically shown as follows:

[0089] Step S431, the abnormal value distribution ratio is determined according to two Gaussian circular 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, it is judged that the data classification is abnormal.

[0091] Further, if the outlier distribution ratio is greater than the third threshold limit, it is judged that the data classification is abnormal, and the power parameter abnormality of the measurement signal applied in the reading measurement experiment is fed back according to the judgment result.

[0092] Further, 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 less than or equal to the third threshold limit, it is judged that the data classification is normal.

[0093] Specifically, the third threshold limit is set according to the actual experience of those skilled in the art, for example, set to 1%, then when the outlier distribution ratio is greater than 1%, it is judged that the reading data classification is abnormal, and further, the power parameter abnormality of the reading measurement experiment is fed back, and the power in the experimental parameter needs to be adjusted to re-perform the quantum state reading measurement experiment. If the following experiment is performed, the whole experiment progress will be slow, and if the outlier distribution ratio is less than or equal to 1%, it is judged that the reading data classification is normal.

[0094] In an optional embodiment of the embodiment, step S431, the outlier distribution ratio is determined according to the two Gaussian circular spot distributions, which is refined as follows:

[0095] According to the two Gaussian circular spot distributions, the center coordinates of the two Gaussian circular spots are determined;

[0096] According to the two center coordinate data, the center straight line equation and the distance from the 3σ outer data point to the center straight line are determined, which is recorded as the first distance;

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

[0098] The reason for selecting 3σ outer data points in the present application is that according to the three-sigma rule (3σ rule) of statistics, if a data distribution is approximately normal distribution, about 68% of 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 there is any data point exceeding 3 times of the standard deviation, these points are basically outliers.

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

[0100]

[0101] in,

[0102] r1 represents the radius of the Gaussian spot when the quantum state measurement result 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 straight line at the center 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 straight line at the center 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, namely, the distribution ratio difference, the classification fidelity and the 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, the classification fidelity and the outlier distribution ratio are compared with the corresponding preset threshold limit 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 is 2.6% obtained by the method of the present invention, the data classification is directly judged to be abnormal, without determining the classification fidelity and the outlier distribution ratio; in a specific example, when the distribution ratio difference is 2.4% obtained by the method of the present invention, it is necessary to determine the classification fidelity for further judgment. When the classification fidelity is 80%, the data classification is directly judged to be abnormal without determining the outlier distribution ratio; in a specific example, when the distribution ratio difference is 2.4% and the classification fidelity is 88% obtained by the method of the present invention, it is necessary to determine the outlier distribution ratio for further judgment. 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 is 2.4% and the classification fidelity is 88% obtained by the method of the present invention, 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 method for determining abnormal data classification provided by the above embodiment of the present application, the embodiment of the present application also provides a device for determining abnormal data classification. Figure 6 As shown, Figure 6 This is a schematic diagram of a structure of a device for determining data classification anomalies 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 in an IQ coordinate system, and the scattered point data in multiple IQ coordinate systems constitute a characterization data set;

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

[0108] A second determining module 30 is configured to determine a distribution ratio difference of the number of measurement data located on both sides of a center line based on the two Gaussian spot distributions, wherein the center line is a line connecting the two centers of the two Gaussian spots;

[0109] Judgment module 40: for judging 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, judging the classification abnormality of the read data based on the classification fidelity and the second threshold limit.

[0110] Optionally, the judgment module 40 specifically includes:

[0111] a first judgment unit configured to judge that the read data classification is abnormal if the distribution ratio difference is greater than a first threshold; and to use a second judgment unit to perform a second judgment if the distribution ratio difference is less than or equal to the first threshold;

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

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

[0114] The judgment module 40 further includes a first determination unit for determining an outlier distribution ratio based on two Gaussian spot distributions. Furthermore, the first determination unit is specifically configured to:

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

[0116] Based on the two center coordinate data, 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 of the first distance and quantum state measurement result in state 1, 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 quantum state measurement result 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 straight line at the center 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 straight line at the center 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 line according to the two Gaussian spot distributions;

[0124] a second determining unit, configured to determine, based on the number of measurement data on both sides of the center line, 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;

[0125] The third determining unit is configured to determine a distribution ratio difference according to a 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] Where k is the quantum state of the bit, I is the in-phase component of the IQ coordinate, Q is the quadrature component of the IQ coordinate, M is the number of reading measurement experiments, Bool represents the Boolean value, O(k|I,Q) is the distribution ratio, and a and b are constants in the equation y = ax + b of the straight line between the centers of the two Gaussian spots.

[0129] Based on the same inventive concept, according to the method for determining abnormal data classification provided by the above embodiment of the present application, the embodiment of the present application further provides an electronic device, such as Figure 7 As shown, Figure 7A structural diagram 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, for storing computer programs;

[0131] The processor 101 is configured to execute the program stored in the memory 103 by performing the following steps:

[0132] 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;

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

[0134] Based on the distribution of the two Gaussian spots, determine the distribution ratio difference of the number of measurement data on both sides of the center line, where the center line is the line connecting the two centers of the two Gaussian 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 electronic device mentioned above may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. This communication bus can be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is used in the figure, but this does not mean that there is only one bus or only 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 random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage. Alternatively, the memory may be at least one storage device located away from the processor.

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

[0140] Based on the same kind of inventive concept, according to the data classification exception judgment method provided by the above embodiments of the present application, the embodiments of the present application further provide a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to realize the steps of any of the above data classification exception judgment methods.

[0141] Based on the same kind of inventive concept, according to the data classification exception judgment method provided by the above embodiments of the present application, the embodiments of the present application further provide a computer program product containing instructions, which, when running on a computer, causes the computer to execute any of the data classification exception judgment methods in the above embodiments.

[0142] Based on the same kind of inventive concept, the embodiments of the present application further provide a quantum computer, which includes the above judgment device or uses the above judgment method to judge the measurement data classification exception of a quantum bit.

[0143] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When software is used for implementation, 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 can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can 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 can be transmitted from a website, computer, server or data center to another website, computer, server or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium can 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 integrations. The available medium can 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 (SSD)).

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

[0145] Each embodiment in this specification is described in a related manner. Similar portions between the various embodiments can be referenced to each other. Each embodiment focuses on the differences between the other embodiments. In particular, for embodiments such as apparatus, electronic devices, computer-readable storage media, and computer program products, since they are generally similar to the method embodiments, their descriptions are relatively simplified. For relevant portions, reference can be made to the descriptions of the method embodiments.

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

Claims

1. A method for determining abnormal data classification, characterized in that: include: 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; Determining two Gaussian spot distributions on the plane of the IQ coordinate system based on the characterization data set, wherein the two Gaussian spots are Gaussian spots whose quantum state measurement results are in a 1 state and a 0 state, respectively; Based on the distribution of the two Gaussian spots, determine the distribution ratio difference of the number of measurement data on both sides of the center line, where the center line is the line connecting the two centers of the two Gaussian 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 judgment 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 includes: If the classification fidelity is less than a second threshold, determining that the data classification is abnormal; If the classification fidelity is greater than or equal to the second threshold, the classification abnormality of the data is judged based on the outlier distribution ratio and the third threshold.

3. The judgment 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 a third threshold value, the data classification is determined to be abnormal.

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

5. The judgment method according to claim 3, characterized in that: The step of determining the outlier distribution ratio based on the two Gaussian spot distributions includes: According to the distribution of the two Gaussian spots, the coordinate data of the centers of the two Gaussian spots are determined; Based on the two center coordinate data, 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 of the first distance and quantum state measurement result in state 1, 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 judgment 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 quantum state measurement result 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 straight line at the center 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 straight line at the center of the two Gaussian spots y=ax+b.

7. The judgment 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 based on the two Gaussian spot distributions includes: According to the two Gaussian spot distributions, the number of measurement data on both sides of the center line is counted respectively; Determining, based on the number of measurement data on both sides of the center line, 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; 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, where the measurement data is scattered point data in the IQ coordinate system, and the scattered point data of multiple IQ coordinate systems constitute a characterization data set; A first determination module is configured to determine, based on the characterization data set, two Gaussian spot distributions on the plane of the IQ coordinate system, wherein the two Gaussian spots are Gaussian spots whose quantum state measurement results are in a 1 state and a 0 state, respectively; The second determination module is configured to determine the distribution ratio difference of the number of measurement data on both sides of the center line based on the distribution of the two Gaussian spots, wherein the center line is a line connecting the two centers of the two Gaussian 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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