Error source analysis method and device for dispersion reading, equipment, storage medium and program product

Abstract

The invention relates to a dispersion reading error source analysis method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the steps that under the condition that a quantum bit is in a first quantum state, a plurality of first time sequence signals on a reading link are collected, and the collection energy of the first time sequence signals is different; under the condition that the quantum bit is in the second quantum state, multiple second time sequence signals on the reading link are collected, and the collection energy of the second time sequence signals is different; determining a first error component of each first time sequence signal and a second error component of each second time sequence signal based on each first time sequence signal and each second time sequence signal by using a double-Gaussian function algorithm, and determining an error source analysis result according to the first error component and the second error component, the error source analysis result comprises at least one error type and a contribution degree corresponding to the at least one error type. By adopting the method, the accuracy of an error source analysis result can be effectively improved.

Description

Technical Field

[0001] This application relates to the field of quantum computing technology, and in particular to a method, apparatus, computer device, computer-readable storage medium, and computer program product for analyzing the sources of error in dispersion readout. Background Technology

[0002] Quantum bits are the fundamental units of information processing. In the fields of quantum computing and quantum information processing, high-fidelity qubit readout is a key technology for realizing real-time quantum feedback schemes. In superconducting qubit systems, dispersive readout is the most widely used readout technique. By analyzing the error sources of dispersive readout in superconducting qubit systems, the readout scheme can be effectively adjusted, thereby improving readout fidelity while accelerating readout speed.

[0003] In existing technologies, most methods achieve error source analysis for superconducting quantum ratio characteristic readouts by modeling and evaluating various influencing factors separately.

[0004] However, existing methods can only perform qualitative analysis and have poor accuracy, resulting in low read fidelity of qubits. Summary of the Invention

[0005] Therefore, it is necessary to provide a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for analyzing the error sources of dispersion reading with high accuracy in response to the above-mentioned technical problems.

[0006] Firstly, this application provides a method for analyzing the sources of error in dispersion reading, including:

[0007] When the qubit is in the first quantum state, multiple first time-series signals on the link are collected and read, and the collection energy of each first time-series signal is different;

[0008] When the qubit is in the second quantum state, multiple second time-series signals on the acquisition link are collected, and the acquisition energy of each second time-series signal is different;

[0009] Using the double Gaussian function algorithm, the first error component of each first time-series signal and the second error component of each second time-series signal are determined based on each first time-series signal and each second time-series signal. The error source analysis results are determined based on the first error component and the second error component. The error source analysis results include at least one error type and the contribution of at least one error type.

[0010] In one embodiment, the double Gaussian function algorithm is used to determine the first error component of each first time-series signal and the second error component of each second time-series signal based on each first time-series signal and each second time-series signal. This includes: performing demodulation processing on each first time-series signal and each second time-series signal respectively based on a demodulation algorithm to obtain the first in-phase-quadrature component distribution of each first time-series signal under different integration durations and the second in-phase-quadrature component distribution of each second time-series signal under different integration durations; and using the double Gaussian function algorithm, determining the first error component of each first time-series signal and the second error component of each second time-series signal based on the first in-phase-quadrature component distribution and the second in-phase-quadrature component distribution.

[0011] In one embodiment, the method utilizes a double Gaussian function algorithm to determine the first error component of each first time-series signal and the second error component of each second time-series signal based on a first in-phase-quadrangle component distribution and a second in-phase-quadrangle component distribution. This includes: fitting the first in-phase-quadrangle component distribution based on the double Gaussian function algorithm to obtain a first fitting result for each first time-series signal; fitting the second in-phase-quadrangle component distribution based on the double Gaussian function algorithm to obtain a second fitting result for each second time-series signal; determining the first error component of each first time-series signal based on the first fitting result; and determining the second error component of each second time-series signal based on the second fitting result.

[0012] In one embodiment, determining a first error component for each first time-series signal based on a first fitting result and determining a second error component for each second time-series signal based on a second fitting result includes: for each first time-series signal, determining a first coincidence error based on the principal Gaussian component in the first fitting result of the first time-series signal, determining a first residual error based on the first coincidence error, and determining the first coincidence error and the first residual error as the first error component of the first time-series signal; for each second time-series signal, determining a second coincidence error based on the principal Gaussian component in the second fitting result of the first time-series signal, determining a second residual error based on the second coincidence error, and determining the second coincidence error and the second residual error as the second error component of the second time-series signal.

[0013] In one embodiment, determining the error source analysis result based on the first error component and the second error component includes: determining the first initial state error, the first gate operation error, the first flip error, and the first noise error of each first time-series signal, and the second initial state error, the second gate operation error, the second flip error, and the second noise error of each second time-series signal based on the first error component and the second error component; and determining the first initial state error, the first gate operation error, the first flip error, and the first noise error of each first time-series signal, and the second initial state error, the second gate operation error, the second flip error, and the second noise error of each second time-series signal as the error source analysis result.

[0014] In one embodiment, the method further includes: constructing an optimization model with the goal of maximizing the readout fidelity of qubits based on the error source analysis results; determining target control parameters based on the optimization model; and optimizing the readout link based on the target control parameters, wherein the target control parameters include target acquisition energy and target integration time.

[0015] Secondly, this application also provides an error source analysis device for dispersion readout, comprising:

[0016] The first acquisition module is used to acquire and read multiple first time-series signals on the link when the qubit is in the first quantum state. The acquisition energy of each first time-series signal is different.

[0017] The second acquisition module is used to acquire and read multiple second time-series signals on the link when the qubit is in the second quantum state. The acquisition energy of each second time-series signal is different.

[0018] The determination module is used to determine the first error component of each first time-series signal and the second error component of each second time-series signal based on each first time-series signal and each second time-series signal using the double Gaussian function algorithm, and to determine the error source analysis results based on the first error component and the second error component. The error source analysis results include the error source and the contribution of the error source.

[0019] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described in any of the embodiments of the first aspect above.

[0020] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in any of the embodiments of the first aspect above.

[0021] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the method described in any of the embodiments of the first aspect above.

[0022] The aforementioned method, apparatus, computer equipment, computer-readable storage medium, and computer program product for analyzing the error sources of dispersion readout acquire multiple first timing signals on the readout link when the qubit is in a first quantum state, with each first timing signal having a different acquisition energy. When the qubit is in a second quantum state, multiple second timing signals on the readout link are acquired, with each second timing signal having a different acquisition energy. Then, using a double Gaussian function algorithm, a first error component of each first timing signal and a second error component of each second timing signal are determined based on each first timing signal and each second timing signal. The error source analysis result is determined based on the first and second error components, and the error source analysis result includes at least one error type and the contribution degree corresponding to at least one error type. The method for analyzing the sources of error in dispersion readout provided in this application determines a comprehensive error source analysis result by collecting multiple first timing signals on the readout link when the qubit is in a first quantum state and multiple second timing signals on the readout link when the qubit is in a second quantum state. The error source analysis result includes not only the error type but also the contribution of the error type, thus achieving both qualitative and quantitative analysis. Therefore, it effectively improves the accuracy of the error source analysis result, thereby making the readout scheme adjusted based on the error source analysis result more accurate and effectively improving the readout fidelity of the qubit. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a flowchart illustrating an error source analysis method for dispersion reading in one embodiment;

[0025] Figure 2 This is a flowchart illustrating a method for determining a first error component of each first timing signal and a second error component of each second timing signal in one embodiment.

[0026] Figure 3 This is a flowchart illustrating a method for determining a first error component of each first timing signal and a second error component of each second timing signal in another embodiment.

[0027] Figure 4This is a flowchart illustrating a method for determining a first error component of each first time-series signal based on a first fitting result and a second error component of each second time-series signal based on a second fitting result in one embodiment.

[0028] Figure 5 This is a flowchart illustrating a method for determining the source of error analysis results based on a first error component and a second error component in one embodiment.

[0029] Figure 6 This is a flowchart illustrating the method after obtaining the error source analysis results in one embodiment;

[0030] Figure 7 This is a flowchart illustrating the error source analysis method for dispersion reading in another embodiment;

[0031] Figure 8 This is a structural block diagram of an error source analysis device for dispersion reading in one embodiment;

[0032] Figure 9 This is an internal structural diagram of a computer device in one embodiment;

[0033] Figure 10 This is a diagram of the internal structure of a computer device in another embodiment;

[0034] Figure 11 This is a schematic diagram of the first and second fitting results in one embodiment. Detailed Implementation

[0035] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0036] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0037] In the fields of quantum computing and quantum information processing, high-fidelity single-shot qubit readout is a key technology for realizing real-time quantum feedback schemes. In superconducting qubit systems, dispersion readout is the most widely used readout technique. This technique is a measurement method based on the interaction between the qubit and the resonator, specifically measuring the state-correlated dispersion shift of the qubit to the resonator frequency.

[0038] By analyzing the error sources of superconducting quantum ratio-specific readout, the readout scheme can be effectively adjusted, thereby improving readout fidelity while accelerating readout speed.

[0039] In existing technologies, most methods analyze the sources of error in the unique readout of superconducting quantum ratios by modeling and evaluating various influencing factors separately. Specifically, this analysis is achieved by modeling and evaluating factors such as qubit decay, decoherence, resonator photon number dependence, measurement equipment noise, and qubit initial state preparation.

[0040] However, since each model can usually only provide qualitative analysis and it is difficult to give precise quantitative results, it is impossible to comprehensively and accurately assess the combined impact of each error source on readout fidelity. Furthermore, since the analysis of these error sources is isolated, it is impossible to adjust the readout scheme from a global perspective based on the analysis results. Therefore, there is a problem of poor accuracy, which in turn leads to a low readout fidelity of qubits.

[0041] In view of this, this application provides an error source analysis method for dispersion readout. By acquiring multiple first timing signals on the readout link when the qubit is in a first quantum state, and multiple second timing signals on the readout link when the qubit is in a second quantum state, a comprehensive error source analysis result is determined. The error source analysis result includes not only the error source but also the contribution of the error source, realizing both qualitative and quantitative analysis. Therefore, the accuracy of the error source analysis result is effectively improved, resulting in a more accurate readout scheme adjusted based on the error source analysis result, thereby effectively improving the readout fidelity of the qubit.

[0042] The method for analyzing the sources of error in dispersion reading provided in this application can be executed by a computer device, which can be a terminal or a server.

[0043] In one exemplary embodiment, such as Figure 1 As shown, a method for analyzing the sources of error in dispersion reading is provided, which includes the following steps:

[0044] Step 101: When the qubit is in the first quantum state, collect and read multiple first timing signals on the link.

[0045] The acquisition energy of each first time-series signal is different.

[0046] Optionally, the first quantum state refers to state 0 in the basic readout state of the qubit. For example, when the qubit is in the first quantum state, the internal degrees of freedom of the qubit are in the most stable configuration.

[0047] A readout link refers to a physical channel used to measure the state of a qubit. Exemplarily, a readout link can consist of components such as a resonator, amplifier, filter, and detector. In some alternative approaches, in superconducting quantum systems, the readout link converts quantum state information into a measurable electrical signal by detecting the dispersion shift of the qubit relative to the resonator frequency.

[0048] The first timing signal refers to the electrical signal that changes over time, acquired by the data transfer link when the qubit is in its first quantum state. For example, this first timing signal may contain characteristic information of the first quantum state, such as amplitude and phase changes.

[0049] Harvesting energy refers to the signal energy used in the readout link to excite the interaction between the qubit and the resonator. For example, this harvesting energy can be determined by the amplitude, width, and frequency of the pulse.

[0050] Furthermore, the acquisition energy of each first timing signal is different, that is, by adjusting the energy parameters of the reading pulse, multiple sets of timing signals under different acquisition energies can be obtained.

[0051] In some exemplary embodiments, a computer device can acquire multiple first timing signals on a readout link while the qubit is in a first quantum state.

[0052] Specifically, the computer device can first reset the qubit to the first quantum state through quantum gate operation, and then collect and read the first time sequence signal on the link based on different acquisition energies to obtain multiple first time sequence signals.

[0053] Step 102: With the qubit in the second quantum state, acquire and read multiple second timing signals on the link.

[0054] The acquisition energy of each second time-series signal is different.

[0055] Optionally, the second quantum state refers to state 1 in the basic readout state of the qubit. For example, the qubit requires external energy to transition from the first quantum state to the second quantum state.

[0056] The second timing signal refers to the electrical signal that changes over time, acquired by the data transfer link when the qubit is in its second quantum state. For example, this second timing signal may contain characteristic information of the second quantum state, such as amplitude and phase changes.

[0057] In some exemplary embodiments, a computer device can acquire multiple first timing signals on a readout link while the qubit is in a first quantum state.

[0058] Specifically, the computer device can first set the qubit to a second quantum state through quantum gate operation, and then collect and read the second time-series signal on the link based on different acquisition energies to obtain multiple second time-series signals.

[0059] Step 103: Using the double Gaussian function algorithm, determine the first error component of each first time-series signal and the second error component of each second time-series signal based on each first time-series signal and each second time-series signal, and determine the error source analysis results based on the first error component and the second error component.

[0060] The dual Gaussian function algorithm refers to an algorithm that fits and analyzes the timing signal of a qubit based on two Gaussian distribution models. It can be used to distinguish the timing signal characteristics of the first and second quantum states of a qubit and quantize the error.

[0061] Optionally, the first error component refers to the probability information that when a qubit is actually in a first quantum state, its corresponding first timing signal is misclassified as a second quantum state. The second error component refers to the probability information that when a qubit is actually in a second quantum state, its corresponding second timing signal is misclassified as a first quantum state.

[0062] The error source analysis results include at least one error type and its corresponding contribution. The error type, also known as the error source type, indicates the specific physical or technical factors that cause the first and second error components, such as quantum state decoherence, readout link noise, pulse parameter misalignment, and dispersion shift error. The contribution of each error type refers to its proportion in the total error.

[0063] In some exemplary embodiments, after obtaining multiple first timing signals and multiple second timing signals, the computer device can use a double Gaussian function algorithm to determine a first error component of each first timing signal and a second error component of each second timing signal based on each first timing signal and each second timing signal.

[0064] Specifically, the computer equipment can input each first time-series signal and each second time-series signal into a pre-trained error component analysis model, so that the error component analysis model can use a double Gaussian function algorithm to output the first error component of each first time-series signal and the second error component of each second time-series signal based on each first time-series signal and each second time-series signal.

[0065] Furthermore, after determining the first error component of each first timing signal and the second error component of each second timing signal, the computer equipment can determine the error source analysis results based on the first error component and the second error component.

[0066] Specifically, the computer device can input the first error component and the second error component into a pre-trained error source analysis result determination model to obtain the error source analysis result output by the error source analysis result determination model.

[0067] The above-described method for analyzing the sources of error in dispersion readout involves acquiring multiple first timing signals on the readout link when the qubit is in the first quantum state, with each first timing signal having a different acquisition energy. Then, when the qubit is in the second quantum state, multiple second timing signals on the readout link are acquired, with each second timing signal having a different acquisition energy. Next, using a dual Gaussian function algorithm, a first error component for each first timing signal and a second error component for each second timing signal are determined based on the first and second timing signals. Finally, the error source analysis results are determined based on the first and second error components, and these results include at least one error type and the contribution of each error type. The method for analyzing the sources of error in dispersion readout provided in this application determines a comprehensive error source analysis result by collecting multiple first timing signals on the readout link when the qubit is in a first quantum state and multiple second timing signals on the readout link when the qubit is in a second quantum state. The error source analysis result includes not only the error type but also the contribution of the error type, thus achieving both qualitative and quantitative analysis. Therefore, it effectively improves the accuracy of the error source analysis result, thereby making the readout scheme adjusted based on the error source analysis result more accurate and effectively improving the readout fidelity of the qubit.

[0068] In one exemplary embodiment, such as Figure 2 As shown, the double Gaussian function algorithm is used to determine the first error component of each first time-series signal and the second error component of each second time-series signal based on each first time-series signal and each second time-series signal, including the following steps:

[0069] Step 201: Based on the demodulation algorithm, perform demodulation processing on each first time-series signal and each second time-series signal to obtain the distribution of the first in-phase-orthogonal components of each first time-series signal under different integration durations and the distribution of the second in-phase-orthogonal components of each second time-series signal under different integration durations.

[0070] The demodulation algorithm refers to an algorithm that uses signal processing to separate the core feature components related to the quantum state from a modulated time-series signal.

[0071] Integration duration refers to the length of the time window for integrating a time-series signal. For example, since time-series signals may contain noise, integrating over a certain duration can suppress noise and highlight the effective components of the signal. Different integration durations affect the signal-to-noise ratio and feature sharpness of the time-series signal.

[0072] The in-phase and quadrature components include in-phase components and quadrature components. In-phase components refer to signal components that are in phase with the reference carrier, while quadrature components refer to signal components that are 90° out of phase with the reference carrier.

[0073] The first in-phase-quadrature component distribution refers to the statistical distribution of the in-phase and quadrature components of the first time-series signal under different integration durations after the signal has undergone demodulation processing.

[0074] The second in-phase-quadrature component distribution refers to the statistical distribution of the in-phase and quadrature components of the second time-series signal under different integration durations after the signal has undergone demodulation processing.

[0075] In some exemplary embodiments, after obtaining multiple first timing signals and multiple second timing signals, the computer device can perform de-modeling processing on each first timing signal and each second timing signal based on a de-modeling algorithm to obtain the first in-phase-quadrature component distribution of each first timing signal under different integration durations and the second in-phase-quadrature component distribution of each second timing signal under different integration durations.

[0076] Step 202: Using the double Gaussian function algorithm, determine the first error component of each first time-series signal and the second error component of each second time-series signal based on the first in-phase-quadrature component distribution and the second in-phase-quadrature component distribution.

[0077] In some exemplary embodiments, after obtaining the distribution of the first in-phase-quadrature components of each first time-series signal under different integration durations and the distribution of the second in-phase-quadrature components of each second time-series signal under different integration durations, the computer device can use the double Gaussian function algorithm to determine the first error component of each first time-series signal and the second error component of each second time-series signal based on the distribution of the first in-phase-quadrature components and the distribution of the second in-phase-quadrature components.

[0078] Specifically, the computer device can input the first in-phase-quadrature component distribution and the second in-phase-quadrature component distribution into a pre-trained error component analysis model, so that the error component analysis model can use the double Gaussian function algorithm to output the first error component of each first time-series signal and the second error component of each second time-series signal based on each of the first in-phase-quadrature component distribution and the second in-phase-quadrature component distribution.

[0079] In one exemplary embodiment, such as Figure 3 As shown, the first error component of each first time-series signal and the second error component of each second time-series signal are determined using the double Gaussian function algorithm based on the first in-phase-quadrature component distribution and the second in-phase-quadrature component distribution, including the following steps:

[0080] Step 301: Fit the distribution of the first in-phase-orthogonal component based on the double Gaussian function algorithm to obtain the first fitting result of each first time-series signal.

[0081] In some exemplary embodiments, after obtaining the first in-phase-quadrature component distribution, the computer device can fit the first in-phase-quadrature component distribution based on the double Gaussian function algorithm to obtain the first fitting result of each first time-series signal.

[0082] Specifically, the first fitting result can be as follows: Figure 11 As shown, Figure 11 Line A in the figure represents the first fitting result. Figure 11 Line B in the diagram represents the first in-phase-orthogonal component distribution.

[0083] Step 302: Fit the distribution of the second in-phase-orthogonal components based on the double Gaussian function algorithm to obtain the second fitting results of each second time-series signal.

[0084] In some exemplary embodiments, after obtaining the second in-phase-quadrature component distribution, the computer device can fit the second in-phase-quadrature component distribution based on the double Gaussian function algorithm to obtain the second fitting result of each second time-series signal.

[0085] Specifically, the second fitting result can be as follows: Figure 11 As shown, Figure 11 Line C in the figure represents the second fitting result. Figure 11 Line D in the diagram represents the second in-phase-orthogonal component distribution.

[0086] Step 303: Determine the first error component of each first time-series signal based on the first fitting result, and determine the second error component of each second time-series signal based on the second fitting result.

[0087] In some exemplary embodiments, after obtaining the first fitting result and the second fitting result, the computer device can determine the first error component of each first time-series signal based on the first fitting result, and determine the second error component of each second time-series signal based on the second fitting result.

[0088] Specifically, the computer equipment can input the first fitting result and the second fitting result into the pre-trained error component analysis model, respectively, to obtain the first error component and the second error component output by the error component analysis model.

[0089] In one exemplary embodiment, such as Figure 4 As shown, the method for determining the first error component of each first time-series signal based on the first fitting result and the second error component of each second time-series signal based on the second fitting result includes the following steps:

[0090] Step 401: For each first time-series signal, determine the first coincidence error based on the principal Gaussian component in the first fitting result of the first time-series signal, determine the first remaining error based on the first coincidence error, and determine the first coincidence error and the first remaining error as the first error component of the first time-series signal.

[0091] The dominant Gaussian component in the first fitting result refers to the main distribution of the first time-series signal. For example, the dominant Gaussian component in the first fitting result can be as follows: Figure 11 As shown by line E in the diagram.

[0092] The first overlap error characterizes the probability that some first quantum state signals are misidentified as second quantum states due to the natural overlap between the first and second in-phase-quadrature component distributions of the first and second time-series signals. This first overlap error mainly originates from line noise and detector errors.

[0093] The first residual error is used to characterize the probability of misjudgment caused by factors other than the first coincidence error. This first residual error mainly originates from the initial state preparation, bit attenuation, and bit excitation caused by the increase in the number of photons in the measurement cavity.

[0094] In some exemplary embodiments, after obtaining a first fitting result, the computer device determines a first coincidence error for each first time-series signal based on the principal Gaussian component in the first fitting result of the first time-series signal. Specifically, the first coincidence error can be as follows: Figure 11 The shaded area F is shown in the figure.

[0095] Furthermore, after determining the first coincidence error, the computer device can determine the first remaining error based on the first coincidence error, and determine the first coincidence error and the first remaining error as the first error component of the first timing signal.

[0096] Specifically, the computer device can determine the difference between the total error and the first coincidence error as the first residual error, and after determining the first residual error, determine the first coincidence error and the first residual error as the first error component of the first timing signal.

[0097] Step 402: For each second time-series signal, determine the second coincidence error based on the principal Gaussian component in the second fitting result of the first time-series signal, determine the second remaining error based on the second coincidence error, and determine the second coincidence error and the second remaining error as the second error component of the second time-series signal.

[0098] The dominant Gaussian component in the second fitting result refers to the main distribution of the second time-series signal. For example, the dominant Gaussian component in this second fitting result can be as follows: Figure 11 Line G is shown in the diagram.

[0099] The second overlap error characterizes the probability that some second quantum state signals are misidentified as first quantum states due to the natural overlap between the first and second in-phase-quadrature component distributions of the first and second time-series signals. This second overlap error mainly originates from line noise and detector errors.

[0100] The second residual error is used to characterize the probability of misjudgment caused by factors other than the second coincidence error. This second residual error mainly originates from the initial state preparation, bit attenuation, and bit excitation caused by the increase in the number of photons in the measurement cavity.

[0101] In some exemplary embodiments, after obtaining the second fitting result, the computer device determines a second coincidence error for each second time-series signal based on the principal Gaussian component in the second fitting result of the second time-series signal. Specifically, the second coincidence error can be as follows: Figure 11 As shown in the figure, the shaded area H is [not specified]. Figure 11 In the equation, line I is the threshold for the maximum distinguishability between the first and second quantum states obtained through optimization.

[0102] Furthermore, after determining the second coincidence error, the computer device can determine the second remaining error based on the second coincidence error, and determine the second coincidence error and the second remaining error as the second error component of the second timing signal.

[0103] Specifically, the computer device can determine the difference between the total error and the second coincidence error as the second residual error, and after determining the second residual error, determine the second coincidence error and the second residual error as the second error component of the second timing signal.

[0104] In one exemplary embodiment, such as Figure 5 As shown, the analysis results for determining the source of error based on the first and second error components include the following steps:

[0105] Step 501: Determine the first initial state error, first gate operation error, first flip error, and first noise error of each first timing signal, as well as the second initial state error, second gate operation error, second flip error, and second noise error of each second timing signal, based on the first error component and the second error component.

[0106] The first initial state error refers to the error generated during the quantum state preparation stage of the first time-series signal. The first gate operation error refers to the error introduced by insufficient operational precision after the first time-series signal undergoes logic gate operations, such as qubit flipping operations and phase modulation operations. The first flip error refers to the error caused by an unexpected flip of the first time-series signal state during transmission or processing. The first noise error refers to random errors caused by environmental noise or equipment noise.

[0107] The second initial state error refers to the error generated during the quantum state preparation stage of the second time-series signal. The second gate operation error refers to the error introduced by insufficient operational precision after the second time-series signal undergoes logic gate operations, such as qubit flipping operations and phase modulation operations. The second flip error refers to the error caused by an unexpected flip of the second time-series signal state during transmission or processing. The second noise error refers to random errors caused by environmental noise or equipment noise.

[0108] In some exemplary embodiments, after obtaining the first error component and the second error component, the computer device can determine the first initial state error, the first gate operation error, the first flip error and the first noise error of each first timing signal, as well as the second initial state error, the second gate operation error, the second flip error and the second noise error of each second timing signal, based on the first error component and the second error component.

[0109] Specifically, the first / second overlap error can be expressed as: Where p refers to the energy collected, and t refers to the integration time. This refers to the first overlap error. This refers to the second overlap error. The remaining errors (first / second) can be expressed as... , This refers to the first remaining error. This refers to the second remaining error.

[0110] First / Second Noise Error: The first / second overlap error is defined as the first / second noise error, that is, the first / second noise error is... .

[0111] First / Second Flip Error: Fit the remaining first / second errors; this fit can be expressed as... Where A is the fidelity value that is independent of the pulse duration, and B is other noise components. Both A and B are independent of the reading duration.

[0112] Furthermore, since part B contains other noise, the error is decomposed based on part A. Specifically, for the first quantum state: For the second quantum state: When t=0, Then there is , First / Second Flip Error .

[0113] First / second gate operation error and first / second initial state error: for The fitting process can be represented as follows: The value of E obtained through fitting satisfies , ,and The first / second gate operating error and the first / second initial state error are determined based on the ratio of E, where, For the first / second initial state error, This refers to the operational error of the first / second gate.

[0114] Step 502: The first initial state error, first gate operation error, first flip error, and first noise error of each first time-series signal, as well as the second initial state error, second gate operation error, second flip error, and second noise error of each second time-series signal, are determined as the error source analysis results.

[0115] In some exemplary embodiments, after determining the first initial state error, first gate operation error, first flip error, and first noise error of each first timing signal, and the second initial state error, second gate operation error, second flip error, and second noise error of each second timing signal, the computer device can determine the first initial state error, first gate operation error, first flip error, and first noise error of each first timing signal, and the second initial state error, second gate operation error, second flip error, and second noise error of each second timing signal as the result of error source analysis.

[0116] Specifically, the computer equipment can first determine the error type of the error source analysis results based on the first initial state error, first gate operation error, first flip error and first noise error of each first time-series signal, and the second initial state error, second gate operation error, second flip error and second noise error of each second time-series signal.

[0117] Furthermore, the computer device can acquire the total error, and then use the total error, the first initial state error, the first gate operation error, the first flip error, and the first noise error of each first timing signal, as well as the second initial state error, the second gate operation error, the second flip error, and the second noise error of each second timing signal to determine the contribution of each error type. For example, the contribution of error type X = error type X / total error × 100%.

[0118] In one exemplary embodiment, such as Figure 6 As shown, after obtaining the error source analysis results, the method also includes the following steps:

[0119] Step 601: Based on the error source analysis results, construct an optimization model with the goal of maximizing the read fidelity of qubits.

[0120] In some exemplary embodiments, after obtaining the error source analysis results, the computer device can construct an optimization model with the goal of maximizing the read fidelity of qubits based on the error source analysis results.

[0121] Step 602: Determine the target control parameters based on the optimization model, and optimize the reading link based on the target control parameters.

[0122] The target control parameters include the target acquisition energy and the target integration time.

[0123] In some exemplary embodiments, after constructing an optimization model aimed at maximizing the read fidelity of qubits, the computer device can determine target control parameters based on the optimization model and optimize the read link based on the target control parameters.

[0124] Specifically, the computer equipment can solve the optimization model to determine the target control parameters, and optimize the reading link based on the target control parameters.

[0125] In one exemplary embodiment, such as Figure 7 As shown, another method for analyzing the sources of error in dispersion readings is provided, which includes the following steps:

[0126] Step 701: When the qubit is in the first quantum state, collect and read multiple first timing signals on the link, each with a different collection energy; when the qubit is in the second quantum state, collect and read multiple second timing signals on the link, each with a different collection energy.

[0127] Step 702: Based on the demodulation algorithm, perform demodulation processing on each first time-series signal and each second time-series signal to obtain the distribution of the first in-phase-orthogonal components of each first time-series signal under different integration durations and the distribution of the second in-phase-orthogonal components of each second time-series signal under different integration durations.

[0128] Step 703: Fit the first in-phase-quadrature component distribution based on the double Gaussian function algorithm to obtain the first fitting result of each first time-series signal; fit the second in-phase-quadrature component distribution based on the double Gaussian function algorithm to obtain the second fitting result of each second time-series signal.

[0129] Step 704: For each first time-series signal, determine the first coincidence error based on the principal Gaussian component in the first fitting result of the first time-series signal, and determine the first residual error based on the first coincidence error, and determine the first coincidence error and the first residual error as the first error component of the first time-series signal; for each second time-series signal, determine the second coincidence error based on the principal Gaussian component in the second fitting result of the first time-series signal, and determine the second residual error based on the second coincidence error, and determine the second coincidence error and the second residual error as the second error component of the second time-series signal;

[0130] Step 705: Determine the first initial state error, first gate operation error, first flip error, and first noise error of each first time-series signal, and the second initial state error, second gate operation error, second flip error, and second noise error of each second time-series signal based on the first error component and the second error component; determine the first initial state error, first gate operation error, first flip error, and first noise error of each first time-series signal, and the second initial state error, second gate operation error, second flip error, and second noise error of each second time-series signal as the error source analysis results, which include at least one error type and the contribution of at least one error type;

[0131] Step 706: Based on the error source analysis results, construct an optimization model with the goal of maximizing the readout fidelity of qubits; determine the target control parameters based on the optimization model, and optimize the readout link based on the target control parameters. The target control parameters include the target acquisition energy and the target integration time.

[0132] In summary, the error source analysis method provided in this application comprehensively considers all error sources. By establishing a unified model framework, it fully evaluates the interactions between error sources such as qubit decay, resonator photon number dependence effect, measurement equipment noise, and qubit initial state preparation, avoiding the limitations of isolated analysis in traditional methods. Traditional methods typically only provide qualitative analysis, while this application, through integrated modeling and data-driven methods, can more accurately quantify the impact of each error source, providing a reliable quantitative basis for optimizing readout fidelity.

[0133] Moreover, this application not only focuses on optimizing a single source of error, but also takes a global perspective to provide systematic guidance for the overall improvement of measurement technology, helping to find the optimal balance between various factors.

[0134] Secondly, compared to the problem that traditional theoretical modeling methods are difficult to correspond one-to-one with experimental data, this application starts directly from the experimental data (first time series signal and second time series signal) and uses statistical modeling techniques to ensure that the model is highly consistent with the complexity of the actual system.

[0135] Finally, data-driven quantitative analysis and global optimization enable a significant improvement in readout fidelity and a faster measurement speed, laying the foundation for the practical application of large-scale quantum computing.

[0136] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0137] Based on the same inventive concept, this application also provides an error source analysis device for implementing the above-described method for analyzing the error sources of dispersion reading. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the error source analysis device for dispersion reading provided below can be found in the limitations of the error source analysis method for dispersion reading described above, and will not be repeated here.

[0138] In one exemplary embodiment, such as Figure 8 As shown, an error source analysis device 800 for dispersion reading is provided, comprising: a first acquisition module 801, a second acquisition module 802, and a determination module 803, wherein:

[0139] The first acquisition module 801 is used to acquire and read multiple first time-series signals on the link when the qubit is in the first quantum state, and the acquisition energy of each first time-series signal is different.

[0140] The second acquisition module 802 is used to acquire and read multiple second time-series signals on the link when the qubit is in the second quantum state. The acquisition energy of each second time-series signal is different.

[0141] The determination module 803 is used to determine the first error component of each first time-series signal and the second error component of each second time-series signal based on each first time-series signal and each second time-series signal using a double Gaussian function algorithm, and to determine the error source analysis results based on the first error component and the second error component. The error source analysis results include at least one error type and the contribution degree corresponding to at least one error type.

[0142] In one embodiment, the determining module 803 is specifically used to perform demodulation processing on each first time-series signal and each second time-series signal respectively based on the demodulation algorithm to obtain the first in-phase-quadrature component distribution of each first time-series signal under different integration durations and the second in-phase-quadrature component distribution of each second time-series signal under different integration durations; and to determine the first error component of each first time-series signal and the second error component of each second time-series signal based on the first in-phase-quadrature component distribution and the second in-phase-quadrature component distribution using the double Gaussian function algorithm.

[0143] In one embodiment, the determining module 803 is specifically used to fit the first in-phase-quadrature component distribution based on the double Gaussian function algorithm to obtain a first fitting result for each first time-series signal; to fit the second in-phase-quadrature component distribution based on the double Gaussian function algorithm to obtain a second fitting result for each second time-series signal; to determine a first error component for each first time-series signal based on the first fitting result; and to determine a second error component for each second time-series signal based on the second fitting result.

[0144] In one embodiment, the determining module 803 is specifically configured to, for each first time-series signal, determine a first coincidence error based on the principal Gaussian component in the first fitting result of the first time-series signal, determine a first residual error based on the first coincidence error, and determine the first coincidence error and the first residual error as the first error component of the first time-series signal; and for each second time-series signal, determine a second coincidence error based on the principal Gaussian component in the second fitting result of the first time-series signal, determine a second residual error based on the second coincidence error, and determine the second coincidence error and the second residual error as the second error component of the second time-series signal.

[0145] In one embodiment, the determining module 803 is specifically configured to determine, based on the first error component and the second error component, the first initial state error, the first gate operation error, the first flip error and the first noise error of each first time-series signal, and the second initial state error, the second gate operation error, the second flip error and the second noise error of each second time-series signal; and to determine the first initial state error, the first gate operation error, the first flip error and the first noise error of each first time-series signal, and the second initial state error, the second gate operation error, the second flip error and the second noise error of each second time-series signal as the error source analysis results.

[0146] In one embodiment, the determining module 803 is further configured to construct an optimization model with the goal of maximizing the read fidelity of qubits based on the error source analysis results; determine target control parameters based on the optimization model; and optimize the read link based on the target control parameters, wherein the target control parameters include target acquisition energy and target integration time.

[0147] The modules in the aforementioned error source analysis device for dispersion reading can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0148] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 9 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores data. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network. When executed by the processor, the computer program implements a method for analyzing the sources of dispersion readout errors.

[0149] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 10As shown, the computer device includes a processor, memory, input / output interfaces, a communication interface, a display unit, and an input device. The processor, memory, and input / output interfaces are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interfaces. The processor provides computational and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage medium. The input / output interfaces are used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When executed by the processor, the computer program implements a method for analyzing the sources of dispersion readout errors.

[0150] Those skilled in the art will understand that Figure 9 or Figure 10 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0151] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0152] When the qubit is in the first quantum state, multiple first time-series signals on the link are collected and read, and the collection energy of each first time-series signal is different;

[0153] When the qubit is in the second quantum state, multiple second time-series signals on the acquisition link are collected, and the acquisition energy of each second time-series signal is different;

[0154] Using the double Gaussian function algorithm, the first error component of each first time-series signal and the second error component of each second time-series signal are determined based on each first time-series signal and each second time-series signal. The error source analysis results are determined based on the first error component and the second error component. The error source analysis results include at least one error type and the contribution of at least one error type.

[0155] In one embodiment, when the processor executes the computer program, it further performs the following steps: performing demodulation processing on each first timing signal and each second timing signal based on a demodulation algorithm to obtain the first in-phase-quadrature component distribution of each first timing signal under different integration durations and the second in-phase-quadrature component distribution of each second timing signal under different integration durations; and using a double Gaussian function algorithm, determining the first error component of each first timing signal and the second error component of each second timing signal based on the first in-phase-quadrature component distribution and the second in-phase-quadrature component distribution.

[0156] In one embodiment, when the processor executes the computer program, it further performs the following steps: fitting the first in-phase-quadrature component distribution based on the double Gaussian function algorithm to obtain a first fitting result for each first time-series signal; fitting the second in-phase-quadrature component distribution based on the double Gaussian function algorithm to obtain a second fitting result for each second time-series signal; determining a first error component for each first time-series signal based on the first fitting result, and determining a second error component for each second time-series signal based on the second fitting result.

[0157] In one embodiment, when the processor executes the computer program, it further implements the following steps: for each first timing signal, a first coincidence error is determined based on the principal Gaussian component in the first fitting result of the first timing signal, and a first residual error is determined based on the first coincidence error, and the first coincidence error and the first residual error are determined as a first error component of the first timing signal; for each second timing signal, a second coincidence error is determined based on the principal Gaussian component in the second fitting result of the first timing signal, and a second residual error is determined based on the second coincidence error, and the second coincidence error and the second residual error are determined as a second error component of the second timing signal.

[0158] In one embodiment, when the processor executes the computer program, it further performs the following steps: determining, based on the first error component and the second error component, the first initial state error, the first gate operation error, the first flip error, and the first noise error of each first timing signal, as well as the second initial state error, the second gate operation error, the second flip error, and the second noise error of each second timing signal; and determining the first initial state error, the first gate operation error, the first flip error, and the first noise error of each first timing signal, as well as the second initial state error, the second gate operation error, the second flip error, and the second noise error of each second timing signal, as the result of error source analysis.

[0159] In one embodiment, when the processor executes the computer program, it also performs the following steps: constructing an optimization model with the goal of maximizing the read fidelity of qubits based on the error source analysis results; determining target control parameters based on the optimization model; and optimizing the read link based on the target control parameters, which include target acquisition energy and target integration time.

[0160] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0161] When the qubit is in the first quantum state, multiple first time-series signals on the link are collected and read, and the collection energy of each first time-series signal is different;

[0162] When the qubit is in the second quantum state, multiple second time-series signals on the acquisition link are collected, and the acquisition energy of each second time-series signal is different;

[0163] Using the double Gaussian function algorithm, the first error component of each first time-series signal and the second error component of each second time-series signal are determined based on each first time-series signal and each second time-series signal. The error source analysis results are determined based on the first error component and the second error component. The error source analysis results include at least one error type and the contribution of at least one error type.

[0164] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: performing demodulation processing on each first time-series signal and each second time-series signal respectively based on the demodulation algorithm to obtain the first in-phase-quadrature component distribution of each first time-series signal under different integration durations and the second in-phase-quadrature component distribution of each second time-series signal under different integration durations; and using the double Gaussian function algorithm, determining the first error component of each first time-series signal and the second error component of each second time-series signal based on the first in-phase-quadrature component distribution and the second in-phase-quadrature component distribution.

[0165] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: fitting the first in-phase-quadrature component distribution based on the double Gaussian function algorithm to obtain a first fitting result for each first time-series signal; fitting the second in-phase-quadrature component distribution based on the double Gaussian function algorithm to obtain a second fitting result for each second time-series signal; determining a first error component for each first time-series signal based on the first fitting result, and determining a second error component for each second time-series signal based on the second fitting result.

[0166] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: for each first timing signal, determining a first coincidence error based on the principal Gaussian component in the first fitting result of the first timing signal, determining a first residual error based on the first coincidence error, and determining the first coincidence error and the first residual error as a first error component of the first timing signal; for each second timing signal, determining a second coincidence error based on the principal Gaussian component in the second fitting result of the first timing signal, determining a second residual error based on the second coincidence error, and determining the second coincidence error and the second residual error as a second error component of the second timing signal.

[0167] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: determining, based on the first error component and the second error component, the first initial state error, the first gate operation error, the first flip error, and the first noise error of each first timing signal, as well as the second initial state error, the second gate operation error, the second flip error, and the second noise error of each second timing signal; and determining the first initial state error, the first gate operation error, the first flip error, and the first noise error of each first timing signal, as well as the second initial state error, the second gate operation error, the second flip error, and the second noise error of each second timing signal, as the result of the error source analysis.

[0168] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: constructing an optimization model with the goal of maximizing the read fidelity of qubits based on the error source analysis results; determining target control parameters based on the optimization model; and optimizing the read link based on the target control parameters, which include target acquisition energy and target integration time.

[0169] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the method described in any of the above embodiments.

[0170] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0171] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0172] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for analyzing the sources of error in dispersion reading, characterized in that, The method includes: When the qubit is in the first quantum state, multiple first time-series signals on the acquisition link are collected and read, and the acquisition energy of each first time-series signal is different; When the qubit is in the second quantum state, multiple second timing signals on the readout link are collected, and the collection energy of each second timing signal is different; Using the double Gaussian function algorithm, a first error component of each first time-series signal and a second error component of each second time-series signal are determined based on each first time-series signal and each second time-series signal. The error source analysis results are then determined based on the first error components and the second error components. The error source analysis results include at least one error type and the contribution degree corresponding to the at least one error type.

2. The method according to claim 1, characterized in that, The step of using the double Gaussian function algorithm to determine the first error component of each first time-series signal and the second error component of each second time-series signal based on each first time-series signal and each second time-series signal includes: Based on the demodulation algorithm, the first time series signal and the second time series signal are respectively demodulated to obtain the first in-phase-orthogonal component distribution of the first time series signal under different integration durations and the second in-phase-orthogonal component distribution of the second time series signal under different integration durations; Using the double Gaussian function algorithm, the first error component of each first time-series signal and the second error component of each second time-series signal are determined based on the first in-phase-orthogonal component distribution and the second in-phase-orthogonal component distribution.

3. The method according to claim 2, characterized in that, The step of using the double Gaussian function algorithm to determine the first error component of each first time-series signal and the second error component of each second time-series signal based on the first in-phase-quadrature component distribution and the second in-phase-quadrature component distribution includes: The first in-phase-orthogonal component distribution is fitted based on the double Gaussian function algorithm to obtain the first fitting result of each of the first time series signals; The second in-phase-orthogonal component distribution is fitted using the double Gaussian function algorithm to obtain the second fitting result for each of the second time-series signals; Based on the first fitting result, a first error component of each of the first time-series signals is determined, and based on the second fitting result, a second error component of each of the second time-series signals is determined.

4. The method according to claim 3, characterized in that, The step of determining the first error component of each of the first time-series signals based on the first fitting result, and determining the second error component of each of the second time-series signals based on the second fitting result, includes: For each of the first time-series signals, a first coincidence error is determined based on the principal Gaussian component in the first fitting result of the first time-series signal, and a first remaining error is determined based on the first coincidence error. The first coincidence error and the first remaining error are then determined as the first error component of the first time-series signal. For each of the second time-series signals, a second coincidence error is determined based on the principal Gaussian component in the second fitting result of the first time-series signal, and a second residual error is determined based on the second coincidence error. The second coincidence error and the second residual error are then determined as the second error components of the second time-series signal.

5. The method according to any one of claims 1 to 4, characterized in that, The step of determining the error source analysis result based on the first error component and the second error component includes: The first initial state error, first gate operation error, first flip error, and first noise error of each of the first timing signals are determined based on the first error component and the second error component, as well as the second initial state error, second gate operation error, second flip error, and second noise error of each of the second timing signals; The first initial state error, first gate operation error, first flip error, and first noise error of each of the first time-series signals, as well as the second initial state error, second gate operation error, second flip error, and second noise error of each of the second time-series signals, are determined as the error source analysis results.

6. The method according to claim 1, characterized in that, The method further includes: Based on the analysis results of the error sources, an optimization model is constructed with the goal of maximizing the readout fidelity of qubits. The target control parameters are determined based on the optimization model, and the read link is optimized based on the target control parameters. The target control parameters include target acquisition energy and target integration time.

7. An error source analysis device for dispersion reading, characterized in that, The device includes: The first acquisition module is used to acquire and read multiple first timing signals on the link when the qubit is in the first quantum state, and the acquisition energy of each first timing signal is different; The second acquisition module is used to acquire multiple second timing signals on the readout link when the qubit is in the second quantum state, and the acquisition energy of each second timing signal is different; The determination module is used to determine the first error component of each first time-series signal and the second error component of each second time-series signal based on each first time-series signal and each second time-series signal using a double Gaussian function algorithm, and to determine the error source analysis result based on the first error component and the second error component. The error source analysis result includes at least one error type and the contribution degree corresponding to the at least one error type.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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