Audience prediction and supervision system based on quantum computing

Abstract

The invention discloses an audience prediction supervision system based on quantum computing, and relates to the technical field of quantum computing and data analysis, and the system comprises a data loading module used for converting to-be-predicted data into a quantum state and loading the quantum state into a quantum computer, and a quantum evolution module used for analyzing the quantum state of the to-be-predicted data. The quantum state evolution module is used for carrying out evolution operation on a quantum state based on a pre-trained quantum circuit, the measurement correction module is used for dynamically adjusting quantum state measurement parameters based on a quantum measurement error correction algorithm so as to improve the measurement precision, the prediction analysis module is used for generating an audience prediction report according to an optimized measurement result, and the real-time supervision module is used for monitoring the audience prediction report. The audience prediction module is used for monitoring the audience prediction process in real time; according to the audience prediction supervision system based on quantum computing, the problem of how to regulate and control the accuracy of quantum state measurement to solve the reliability of results in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of quantum computing and data analysis, and in particular to an audience prediction and supervision system based on quantum computing. Background Art

[0002] In modern information technology, quantum computing is gradually being applied to data analysis, machine learning, and predictive analysis due to its super parallel computing capabilities and efficient processing capabilities for complex problems. Audience prediction, as an important application scenario of data analysis, is widely used in marketing, content recommendation, user behavior analysis, etc. Its accuracy and credibility directly affect the effectiveness of related decisions. However, traditional audience prediction methods based on classical computing are often limited by computing resources when processing massive data, solving nonlinear problems, and optimizing complex models, making it difficult to quickly and accurately complete highly complex prediction tasks.

[0003] Audience prediction methods based on quantum computing can complete efficient prediction analysis in a relatively short time by utilizing the superposition and entanglement characteristics of quantum states. However, the measurement of quantum states in quantum computing has certain uncertainties, and the measurement results depend on the collapse process of the quantum state, which makes the accuracy of the measurement a key issue in practical applications. If the accuracy of the quantum state measurement is insufficient, the prediction results may be biased, thereby reducing the credibility of the results and having a negative impact on subsequent decisions. In the prior art, the regulation of quantum state measurements usually relies on increasing the number of experiments or adjusting the structure of the quantum algorithm, but this often leads to significant consumption of computing resources and even causes instability in the measurement results. At the same time, due to the susceptibility of quantum states to environmental noise, traditional error correction methods are less efficient when applied in complex quantum algorithms, further increasing the difficulty of measurement regulation. Summary of the invention

[0004] The purpose of the present invention is to provide an audience prediction and supervision system based on quantum computing, so as to solve the problem of how to regulate the accuracy of quantum state measurement in the prior art to solve the credibility of the results.

[0005] To achieve the above object, the present invention provides the following technical solution: an audience prediction and supervision system based on quantum computing, the system comprising:

[0006] A data loading module, used to convert the data to be predicted into quantum states and load them into the quantum computer;

[0007] The quantum evolution module connected to the data loading module is used to perform evolution operations on the quantum state based on the pre-trained quantum circuit, including simulating the interaction between different sub-states of the quantum state, adjusting the evolution path, mapping the interaction results into the operation sequence of the quantum circuit, and completing the evolution of the quantum state. The specific formula for simulating the interaction between different sub-states of the quantum state is:

[0008] x i (t+1)=x i (t)+μ(x j (t)-x i (t)),|x j (t)-x i (t)|<d;

[0009] Among them, x i (t) represents the characteristic parameter of quantum state i at time t, x j (t) represents the characteristic parameter of another quantum state j that interacts with quantum state i, μ represents the sensitivity of the quantum state to adjust its own characteristic parameters during the interaction, t represents time, d represents the condition for the interaction between quantum states, i represents the number of the quantum state currently evolving, and j represents the number of another quantum state that interacts with quantum state i;

[0010] The measurement correction module connected to the quantum evolution module is used to dynamically adjust the quantum state measurement parameters to improve the measurement accuracy based on the quantum measurement error correction algorithm, including adjusting the quantum measurement sampling frequency, analyzing the frequency characteristics of the quantum measurement error distribution, and ensuring the integrity of the measurement results. The specific formula is:

[0011] f s ≥2f max ;

[0012] Among them, f s Indicates the frequency of data sampling of quantum state measurement results during quantum measurement, f max It represents the highest frequency of change contained in the quantum state error during the quantum state measurement process;

[0013] A prediction analysis module connected to the measurement correction module, for generating an audience prediction report based on the optimized measurement results;

[0014] The real-time monitoring module connected to the prediction analysis module is used to monitor the audience prediction process in real time.

[0015] Preferably, the data loading module converts the data to be predicted into a quantum state and loads the data into the quantum computer, including:

[0016] Extract features from the data to be predicted, convert the multidimensional data vector into feature key-value pairs, map the features to the feature space, reduce the data dimension, convert the mapped data into quantum states, and load them into the quantum computer. The specific formula for mapping the features to the feature space is h(y)=y mod m;

[0017] Among them, y represents the eigenvalue to be predicted, m represents the size of the mapped target feature space, h(y) represents the result after the eigenvalue to be predicted is converted, and mod represents the modulus operation.

[0018] Preferably, the prediction analysis module generates an audience prediction report according to the optimized measurement results, including:

[0019] Calculate the probability of occurrence of each optimized quantum measurement result and analyze the distribution of audience characteristics. The specific formula is:

[0020] Among them, P(a) represents the probability of occurrence of audience characteristic value a, a represents one of the audience characteristic values, E(a) represents the energy value associated with the audience characteristic value a, k represents the proportionality constant, T represents the temperature parameter, Z represents the weighted sum of quantum measurement results, and e represents the base of the natural logarithm.

[0021] Preferably, the real-time monitoring module performs real-time monitoring on the audience prediction process, including:

[0022] Define the indicators that need to be monitored in the real-time monitoring module, randomly sample the state of the system under different conditions, and generate the distribution of each indicator. The specific formula is:

[0023]

[0024] Among them, η represents the average value of the system index estimated by random samples, h l represents the input data generated in the lth sampling, f(h l ) means that when the input variable is h l When , the calculation result of the system index is, N represents the total number of random sampling, and l represents the sampling number.

[0025] Preferably, the data loading module converts the data to be predicted into a quantum state and loads it into the quantum computer. The calculation formula for the size m of the target feature space mapped is: m=log 2 (n);

[0026] Among them, m represents the size of the mapped target feature space, n represents the total number of data to be predicted,

[0027] Indicates the rounding up operator.

[0028] Preferably, the prediction analysis module generates the energy value E(a) related to the audience characteristic state a in the audience prediction report according to the optimized measurement results as follows:

[0029] E(a)=α×var(a)+β×D(a);

[0030] Among them, E(a) represents the energy value associated with the audience characteristic value a, var(a) represents the variance of the characteristic value, D(a) represents the dispersion of the characteristic value, and α and β are weight coefficients.

[0031] Preferably, the prediction analysis module generates an audience prediction report according to the optimized measurement results, and further comprises:

[0032] Set the probability threshold P threshold , when P(a)>P threshold , the audience characteristic value of the probability is screened out, where P(a) represents the probability of occurrence of audience characteristic value a, P threshold Indicates the probability threshold set according to the prediction task requirements.

[0033] Preferably, the measurement correction module dynamically adjusts the quantum state measurement parameters to improve the measurement accuracy based on the quantum measurement error correction algorithm and further includes:

[0034] Get the standard deviation σ of the initial measurement results 0 , calculate the difference λ between the current measurement result and the ideal measurement result, set the difference threshold τ, if d>τ, then make σ=σ 0 / (1+λ), where σ represents the standard deviation of the updated measurement results, σ 0 represents the standard deviation of the initial measurement result, λ represents the difference between the current measurement result and the ideal measurement result, and τ represents the threshold of the difference between the current measurement result and the ideal measurement result.

[0035] Preferably, the audience forecast report generated by the forecast analysis module according to the optimized measurement results includes an audience feature probability distribution diagram, a feature influencing factor list and a future feature change trend forecast diagram.

[0036] Preferably, the audience prediction report supports personalized generation including user selection of features or feature groups to be focused on, adjustment of the presentation format of the report content according to user needs, and output of the report in a variety of formats.

[0037] It can be seen from the above technical solution that the present invention has the following beneficial effects:

[0038] The audience prediction and supervision system based on quantum computing converts the data to be predicted into quantum states through a data loading module and loads the data into a quantum computer. The quantum evolution module performs evolution operations on the quantum state based on a pre-trained quantum circuit. The measurement correction module dynamically adjusts the quantum state measurement parameters based on a quantum measurement error correction algorithm to improve the measurement accuracy. The prediction analysis module generates an audience prediction report according to the optimized measurement results. The real-time supervision module supervises the audience prediction process in real time, which effectively improves the accuracy of quantum measurement, reduces the error in prediction analysis, improves the credibility of prediction results, and reduces the consumption of computing resources caused by repeated experiments or algorithm structure adjustments. Therefore, while meeting the needs of high-precision measurement, the computing efficiency is improved, the sensitivity of the quantum state to environmental noise is reduced, the stability of the measurement results is ensured, the nonlinear and multi-dimensional audience feature data can be efficiently processed, the prediction analysis of complex models can be completed, the analysis ability and response speed of the system can be significantly improved, the modeling and calculation difficulty of complex problems can be reduced, and a simpler and more efficient solution is provided for practical applications. The system can maintain high prediction accuracy and reliability in complex environments, adapt to the needs of changing application scenarios, and solve the problem of how the existing technology regulates the accuracy of quantum state measurement to solve the credibility of the results. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a schematic diagram of the connection of the system modules of the present invention. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0041] like Figure 1 As shown, the present invention provides a technical solution: an audience prediction and supervision system based on quantum computing, the system comprising:

[0042] A data loading module, used to convert the data to be predicted into quantum states and load them into the quantum computer;

[0043] The quantum evolution module connected to the data loading module is used to perform evolution operations on the quantum state based on the pre-trained quantum circuit, including simulating the interaction between different sub-states of the quantum state, adjusting the evolution path, mapping the interaction results into the operation sequence of the quantum circuit, and completing the evolution of the quantum state. The specific formula for simulating the interaction between different sub-states of the quantum state is:

[0044] x i(t+1)=x i (t)+μ(x j (t)-x i (t)),|x j (t)-x i (t)|<d;

[0045] Among them, x i (t) represents the characteristic parameter of quantum state i at time t, x j (t) represents the characteristic parameter of another quantum state j that interacts with quantum state i, μ represents the sensitivity of the quantum state to adjust its own characteristic parameters during the interaction, t represents time, d represents the condition for the interaction between quantum states, i represents the number of the quantum state currently evolving, and j represents the number of another quantum state that interacts with quantum state i;

[0046] The measurement correction module connected to the quantum evolution module is used to dynamically adjust the quantum state measurement parameters to improve the measurement accuracy based on the quantum measurement error correction algorithm, including adjusting the quantum measurement sampling frequency, analyzing the frequency characteristics of the quantum measurement error distribution, and ensuring the integrity of the measurement results. The specific formula is:

[0047] f s ≥2f max ;

[0048] Among them, f s Indicates the frequency of data sampling of quantum state measurement results during quantum measurement, f max It represents the highest frequency of change contained in the quantum state error during the quantum state measurement process;

[0049] A prediction analysis module connected to the measurement correction module, for generating an audience prediction report based on the optimized measurement results;

[0050] The real-time monitoring module connected to the prediction analysis module is used to monitor the audience prediction process in real time.

[0051] In this system, the data loading module first converts the data to be predicted into quantum states, and improves data processing efficiency through the characteristics of quantum computing. Then, the quantum evolution module uses pre-trained quantum circuits to simulate the interaction between quantum states. The mathematical model of evolution is expressed by the formula:

[0052] x i (t+1)=x i (t)+μ(x j (t)-x i (t),|x j (t)-x i (t)|<d;

[0053] Dynamically adjust the characteristic parameters of the quantum state. Among them, μ represents the sensitivity of the quantum state, and d is the trigger condition of the interaction. This interaction is mapped into the operation sequence of the quantum circuit to complete the evolution of the quantum state. After the evolution is completed, the measurement correction module uses a method based on the quantum measurement error correction algorithm to adjust the measurement frequency and parameters. Through the formula:

[0054] f s ≥2f max ;

[0055] Ensure the integrity and accuracy of quantum measurement results, s is the quantum measurement sampling frequency, f max is the highest frequency of change in quantum state error. Finally, the prediction analysis module analyzes the optimized measurement results, generates an accurate audience prediction report, and continuously tracks the prediction process through the real-time monitoring module to ensure the timeliness and accuracy of the prediction data.

[0056] This implementation significantly improves data processing speed and analysis accuracy by introducing quantum computing technology compared to traditional prediction methods. The quantum evolution module can simulate the interaction process of complex quantum states and reveal deep relationships that are difficult to capture with traditional methods. The measurement and correction module further optimizes the quantum measurement results, reduces the impact of measurement errors on the final results, and improves the credibility of predictive analysis. In addition, the real-time monitoring module ensures the real-time nature of the audience prediction process and provides timely feedback and adjustment capabilities for dynamic audience changes.

[0057] The data loading module converts the data to be predicted into a quantum state and loads it into a quantum computer, including extracting features from the data to be predicted, converting multidimensional data vectors into feature key-value pairs, mapping features into feature space, reducing data dimensions, converting the mapped data into a quantum state, and loading it into a quantum computer. The specific formula for mapping features into feature space is h(y)=y mod m;

[0058] Among them, y represents the eigenvalue to be predicted, m represents the size of the mapped target feature space, h(y) represents the result after the eigenvalue to be predicted is converted, and mod represents the modulus operation.

[0059] In this embodiment, the data loading module represents the high-dimensional feature vector of the data to be predicted as a key-value pair through a feature extraction method, which is convenient for subsequent quantum state loading operations. By mapping the high-dimensional data features to the low-dimensional feature space, the quantum bit resources required in the quantum computer are reduced, and the data processing efficiency is improved. The feature mapping uses the modular operation formula h(y)=y mod m, where m is the dimension of the target feature space selected in a specific scenario, which can effectively compress the data dimension and avoid the influence of redundant information on the prediction results. After completing the feature space mapping, the data is further converted into a quantum state and loaded into the quantum computer to provide initial conditions for subsequent quantum calculations. This embodiment effectively reduces the dimension of the data through feature extraction and mapping, improves the efficiency of quantum state loading, thereby reducing the demand for quantum bit resources and improving the practical feasibility of quantum computing. The feature mapping formula h(y)=y mod m can significantly reduce redundancy while maintaining the effective information of the data, making the quantum computing process more accurate and efficient. Compared with directly loading the original high-dimensional data, this solution reduces the computational complexity and helps to further improve the overall performance of the system.

[0060] The prediction analysis module generates an audience prediction report based on the optimized measurement results, including calculating the probability of occurrence of each optimized quantum measurement result and analyzing the distribution of audience characteristics. The specific formula is:

[0061]

[0062] Among them, P(a) represents the probability of occurrence of audience characteristic value a, a represents one of the audience characteristic values, E(a) represents the energy value associated with the audience characteristic value a, k represents the proportionality constant, T represents the temperature parameter, Z represents the weighted sum of quantum measurement results, and e represents the base of the natural logarithm.

[0063] In this embodiment, the prediction analysis module uses the probability distribution model to calculate the probability of occurrence of audience characteristics based on the optimized quantum measurement results.

[0064] The audience feature values ​​are quantified, where E(a) represents the energy correlation index of a specific feature value. The smaller the energy, the higher the probability P(a) of the feature value. The proportional constant k and the temperature parameter T jointly control the shape of the distribution, reflecting the range of variation of the audience features. The normalization factor Z ensures that the sum of all probabilities is 1, and the expression is:

[0065] By analyzing the distribution of audience characteristics, a detailed audience forecast report is generated, which covers the audience's core characteristics, distribution trends and possible behavior patterns.

[0066] This implementation method can efficiently generate accurate audience forecast reports by combining quantization and probability models with the measurement results of quantum computing. Compared with traditional statistical methods, distribution analysis based on quantum measurement can better reveal the complex correlations between audience characteristics and significantly improve the accuracy and reliability of predictions. The introduction of the temperature parameter T gives the model stronger control capabilities and can flexibly adapt to different data characteristics and application scenarios. In addition, the introduction of the normalization factor Z ensures the mathematical rigor of the model calculation, and the generated forecast report has higher scientificity and applicability.

[0067] The real-time supervision module performs real-time supervision on the audience prediction process, including defining the indicators that need to be monitored in the real-time supervision module, randomly sampling the state of the system under different conditions, and generating the distribution of each indicator. The specific formula is:

[0068]

[0069] Among them, η represents the average value of the system index estimated by random samples, h l represents the input data generated in the lth sampling, f(h l ) means that when the input variable is h l When , the calculation result of the system index is, N represents the total number of random sampling, and l represents the sampling number.

[0070] In this implementation, the real-time monitoring module dynamically analyzes the system status during the audience prediction process by random sampling. First, define the key system indicators that need to be monitored, such as prediction accuracy, quantum measurement error rate, and computing resource utilization rate. Then, generate the input variable set {h1, h2, …, hN} by random sampling, and calculate the corresponding system indicator f(h l ). Finally, using the formula The average value η of the system indicators is calculated to intuitively reflect the overall performance status of the system. The real-time monitoring module distributes and visualizes these analysis results to capture the changing trend of the system under dynamic conditions and provide an alarm mechanism for abnormal conditions in the prediction process. This embodiment can efficiently capture changes in system states under dynamic conditions through the random sampling method of the real-time monitoring module, significantly improving the robustness and stability of the prediction system. The average value η of the system indicators provides a quantitative assessment of the overall performance, helping to quickly locate performance bottlenecks or potential problems. Compared with traditional static monitoring methods, random sampling methods have higher flexibility and applicability, especially in complex quantum systems, the monitoring strategy can be adjusted in real time. The distribution analysis results and visualization functions further enhance the operability and user-friendliness of the system.

[0071] The data loading module converts the data to be predicted into quantum state and loads it into the quantum computer. The calculation formula for the size m of the target feature space mapped is: m = log 2 (n);

[0072] Among them, m represents the size of the mapped target feature space, n represents the total number of data to be predicted,

[0073] Indicates the rounding up operator.

[0074] In this embodiment, the feature space size m of the data loading module is calculated based on the total number n of data to be predicted, and is calculated by the formula m=log 2 (n) is determined. The core idea of ​​this formula is to adjust the size of the target feature space according to the amount of data to match the quantum bit resources required for quantum computing. For data with a total amount of n, mapping to a target feature space of size m can effectively reduce the dimension without losing key information. By rounding up, the size m of the target feature space is ensured to be an integer at all times, meeting the basic requirements for quantum bit allocation. The dynamic adjustment of the feature space enables the data loading module to flexibly adapt to data sets of different sizes while optimizing the utilization of quantum computing resources. This embodiment significantly reduces the consumption of quantum bit resources while ensuring data processing accuracy by dynamically adjusting the size m of the target feature space. Formula m=log 2 (n) ensures the matching of the feature space size with the total amount of data n, thus avoiding redundancy or insufficiency of the feature space. Compared with the fixed-size feature space division, this method is more adaptable and can be flexibly applied under different data scales. In addition, this feature space adjustment strategy improves the efficiency of quantum state initialization and lays an optimization foundation for subsequent quantum evolution and measurement links.

[0075] The prediction analysis module generates the energy value E(a) related to the audience characteristic state a in the audience prediction report based on the optimized measurement results. The calculation formula is:

[0076] E(a)=α×var(a)+β×D(a);

[0077] Among them, E(a) represents the energy value associated with the audience characteristic value a, var(a) represents the variance of the characteristic value, D(a) represents the dispersion of the characteristic value, and α and β are weight coefficients.

[0078] In this embodiment, the prediction and analysis module calculates the energy value E(a) related to the audience characteristic state a by combining the variance and dispersion of the characteristic value. The formula E(a) = α × var(a) + β × D(a) integrates the statistical characteristics of the characteristic value a into an energy value to quantify the importance of the characteristic value in audience prediction. The variance var(a) is used to measure the fluctuation range of the characteristic value a, reflecting the variation range of the characteristic value in the entire prediction sample. A larger variance indicates that the characteristic value fluctuates more and may have a more significant impact on the audience behavior; the dispersion D(a) is used to describe the sparse distribution of the characteristic value and represents the relative difference between the characteristic values. The definition of dispersion can be adjusted according to specific needs, such as based on the average distance between the characteristic values ​​or other statistical indicators. The weight coefficients α and β are used to balance the contribution of variance and dispersion to the energy value calculation, and are dynamically adjusted according to the specific application scenario. In this way, the prediction and analysis module can more accurately identify the characteristic values ​​that have the greatest influence on the audience behavior prediction, and the generated audience prediction report is more targeted and explanatory. This embodiment significantly improves the accuracy and reliability of the audience characteristic state analysis through the calculation formula of the energy value E(a). The combination of variance var(a) and discreteness D(a) enables the system to analyze the dynamic changes and distribution characteristics of feature values ​​from a multi-dimensional perspective, which helps to capture hidden audience behavior patterns. The flexibility of weight coefficients α and β enables the system to adjust the focus of energy value calculation according to actual needs, improving the applicability and interpretability of the report. Compared with the traditional single feature analysis method, this solution is more systematic and adaptable, providing scientific quantitative support for audience prediction.

[0079] The prediction analysis module generates an audience prediction report based on the optimized measurement results and also includes setting the probability threshold P threshold , when P(a)>P threshold , the audience characteristic value of the probability is screened out, where P(a) represents the probability of occurrence of audience characteristic value a, P threshold Indicates the probability threshold set according to the prediction task requirements.

[0080] In this embodiment, the prediction analysis module performs probability calculation on the optimized measurement results to screen out the audience characteristic values ​​that meet the conditions. First, the probability threshold P is set according to the requirements of the prediction task. threshold , used to define the screening criteria. Then, by calculating the formula The probability of occurrence of each eigenvalue a is obtained as P(a). threshold When , the feature value a is marked as an important feature and screened out. This screening process effectively eliminates the interference of low-probability feature values ​​and focuses on the important features with high probability, thereby improving the accuracy and pertinence of the prediction report. Users can dynamically adjust P according to the actual needs of the prediction task. threshold, thereby controlling the range and accuracy of the screening results. threshold , which can effectively screen out important audience feature values ​​with high probability, thereby optimizing the results of predictive analysis. The screening mechanism significantly reduces the interference of redundant features, making the forecast report more targeted and explanatory. Compared with the method of directly analyzing all feature values, this solution reduces the complexity of data processing and improves the operating efficiency of the system. Dynamically adjust P threshold The function enables the system to flexibly adapt to different scenarios and meet diverse prediction needs. In addition, the important feature values ​​after screening provide a more accurate basis for subsequent audience behavior analysis and decision-making, significantly improving the practical application value of the report.

[0081] The measurement correction module is based on the quantum measurement error correction algorithm, dynamically adjusting the quantum state measurement parameters to improve the measurement accuracy and also obtaining the standard deviation σ of the initial measurement results 0 , calculate the difference λ between the current measurement result and the ideal measurement result, set the difference threshold τ, if d>τ, then make σ=σ 0 / (1+λ), where σ represents the standard deviation of the updated measurement results, σ 0 represents the standard deviation of the initial measurement result, λ represents the difference between the current measurement result and the ideal measurement result, and τ represents the threshold of the difference between the current measurement result and the ideal measurement result.

[0082] In this embodiment, the measurement correction module realizes efficient correction of quantum measurement errors by dynamically adjusting the standard deviation σ of the quantum state measurement. First, the standard deviation σ of the initial measurement result is obtained. 0 , as the reference value of the measurement system. Then, the measurement correction module calculates the difference λ between the current measurement result and the ideal measurement result to quantify the measurement error. By comparing with the set threshold τ, when the error λ exceeds the threshold, the correction standard deviation σ is calculated according to the formula σ=σ 0 / (1+λ) is used for dynamic adjustment. This adjustment mechanism enhances the accuracy of quantum state measurement results and reduces the impact of errors on measurement results by reducing the standard deviation σ. Through the dynamic adjustment process, the measurement correction module can adapt to different measurement conditions to ensure that the measurement results are always maintained within a high-precision range. This implementation significantly improves the accuracy of quantum state measurement results through a quantum measurement error correction algorithm. The method of dynamically adjusting the standard deviation σ can quickly respond to changes in measurement errors and effectively reduce the impact of error accumulation on subsequent predictive analysis. Compared with the measurement method with a fixed standard deviation, this scheme has stronger adaptability and robustness, and is suitable for the dynamic measurement requirements of complex quantum systems. By introducing the difference threshold τ, the system can flexibly set the error tolerance, thereby taking into account both measurement accuracy and computational efficiency, and providing reliable data support for subsequent quantum calculations and analysis.

[0083] The prediction analysis module generates an audience prediction report based on the optimized measurement results. The generated audience prediction report includes an audience characteristic probability distribution diagram, a characteristic influencing factor list, and a future characteristic change trend prediction diagram. In this embodiment, the prediction analysis module generates an audience prediction report with multi-level information by analyzing and visualizing the optimized quantum measurement results, which specifically includes the following three parts: Audience characteristic probability distribution diagram: Using the formula Calculate the probability of occurrence of each characteristic value a and display it in the form of a bar graph or a curve graph to form a probability distribution graph of audience characteristics, which intuitively reflects the distribution trend of audience characteristics and their probability size. Feature influence factor list: By calculating the influence factor of each audience characteristic in the prediction model, such as the weight, contribution rate or importance ranking of the characteristic value, a feature influence factor list is generated to help users understand the degree of influence of each feature on the audience behavior prediction. The list can be presented in a table, sorted from high to low according to the influence factor, and accompanied by corresponding numerical descriptions. Future feature change trend prediction chart: Based on historical measurement data and current optimization results, a prediction chart of audience characteristics change trend is generated through time series analysis or machine learning prediction model. The prediction chart shows the trend of characteristic values ​​changing over time in the form of a line graph, providing an intuitive view of possible changes in future characteristics. The generation process of the above report makes full use of the high precision and high efficiency of quantum computing measurement results, combined with statistical analysis and visualization technology, to provide comprehensive and clear decision support for audience prediction. The audience prediction report generated by this implementation method integrates multi-dimensional information, including current feature distribution, feature importance and future change trends, providing users with a comprehensive and accurate reference for audience behavior prediction. The probability distribution diagram intuitively reveals the overall distribution pattern of audience characteristics, helping to quickly locate key features. The feature influencing factor list quantifies the importance of each feature and provides data support for subsequent decision-making. The future change trend forecast diagram expands the depth of forecast analysis through the time dimension, making the system more forward-looking and practical. Compared with traditional single-dimensional forecast reports, the information integration capability of this solution is significantly enhanced, which improves the scientific nature of the report and user experience.

[0084] The audience forecast report supports personalized generation, including user selection of features or feature groups to be concerned, adjustment of the display form of the report content according to user needs, and output of the report in multiple formats. In this embodiment, the audience forecast report module combines the specific needs of the user to realize the personalized generation of the report content, specifically including the following functions: User selection of features or feature groups to be concerned: The system provides an interactive interface, allowing the user to select a single feature or multiple feature groups to be focused on from the list of predicted features. After the user selects, the system only generates corresponding probability distribution diagrams, feature influence factor lists, and change trend prediction diagrams for the selected features or feature groups to simplify information presentation and focus on key content. Adjust the display form of the report content: The system supports diversified display of the report content according to user preferences. For example: Chart display: You can choose a bar chart, line chart, heat map or other visualization methods. Arrangement order: Users can sort the content according to the importance of the feature or other indicators. Data summary: Users can choose to present the analysis results in the form of a text overview or a detailed data table. Support multiple report format output: The system supports report output in multiple file formats to meet the needs of different scenarios, including but not limited to: PDF format: Suitable for formal reports and archives. Excel table: Facilitates further analysis and processing of data. HTML format: for online browsing and sharing. Editable document format (such as Word): convenient for users to modify content according to their needs. Through the above functions, the system can generate highly customized audience forecast reports according to user needs to meet a variety of application scenarios. This implementation method significantly improves the user-friendliness and practicality of the system by generating audience forecast reports in a personalized way. Users can choose to focus on features or feature groups on their own, avoiding the redundancy of report content and making the analysis results more targeted. The function that supports adjusting the display format provides a variety of content presentation methods to meet the preferences and needs of different users. In addition, the report output function in multiple formats adapts to the requirements of different application scenarios (such as archiving, analysis, display or sharing), greatly improving the flexibility and applicability of the report. Compared with the report generation method in a fixed format, this solution can better meet the personalized needs of users and enhance the market competitiveness of the system.

[0085] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An audience prediction and supervision system based on quantum computing, characterized in that: The system comprises: A data loading module, used to convert the data to be predicted into quantum states and load them into the quantum computer; The quantum evolution module connected to the data loading module is used to perform evolution operations on the quantum state based on the pre-trained quantum circuit, including simulating the interaction between different sub-states of the quantum state, adjusting the evolution path, mapping the interaction results into the operation sequence of the quantum circuit, and completing the evolution of the quantum state. The specific formula for simulating the interaction between different sub-states of the quantum state is: x i (t+1)=x i (t)+μ(x j (t)-x i (t)),|x j (t)-x i (t)|<d; Among them, x i (t) represents the characteristic parameter of quantum state i at time t, x j (t) represents the characteristic parameter of another quantum state j that interacts with quantum state i, μ represents the sensitivity of the quantum state to adjust its own characteristic parameters during the interaction, t represents time, d represents the condition for the interaction between quantum states, i represents the number of the quantum state currently evolving, and j represents the number of another quantum state that interacts with quantum state i; The measurement correction module connected to the quantum evolution module is used to dynamically adjust the quantum state measurement parameters to improve the measurement accuracy based on the quantum measurement error correction algorithm, including adjusting the quantum measurement sampling frequency, analyzing the frequency characteristics of the quantum measurement error distribution, and ensuring the integrity of the measurement results. The specific formula is: f s ≥2f max ; Among them, f s Indicates the frequency of data sampling of quantum state measurement results during quantum measurement, f max It represents the highest frequency of change contained in the quantum state error during the quantum state measurement process; A prediction analysis module connected to the measurement correction module, for generating an audience prediction report based on the optimized measurement results; The real-time monitoring module connected to the prediction analysis module is used to monitor the audience prediction process in real time.

2. According to claim 1, a system for audience prediction and supervision based on quantum computing is characterized in that: The data loading module converts the data to be predicted into a quantum state and loads it into the quantum computer, including: Extract features from the data to be predicted, convert the multidimensional data vector into feature key-value pairs, map the features to the feature space, reduce the data dimension, convert the mapped data into quantum states, and load them into the quantum computer. The specific formula for mapping the features to the feature space is h(y)=y mod m; Among them, y represents the eigenvalue to be predicted, m represents the size of the mapped target feature space, h(y) represents the result after the eigenvalue to be predicted is converted, and mod represents the modulo operation.

3. The audience prediction and supervision system based on quantum computing according to claim 1 is characterized by: The prediction analysis module generates an audience prediction report according to the optimized measurement results, including: Calculate the probability of occurrence of each optimized quantum measurement result and analyze the distribution of audience characteristics. The specific formula is: Among them, P(a) represents the probability of occurrence of audience characteristic value a, a represents one of the audience characteristic values, E(a) represents the energy value associated with the audience characteristic value a, k represents the proportionality constant, T represents the temperature parameter, Z represents the weighted sum of quantum measurement results, and e represents the base of the natural logarithm.

4. The audience prediction and supervision system based on quantum computing according to claim 1 is characterized by: The real-time supervision module performs real-time supervision on the audience prediction process, including: Define the indicators that need to be monitored in the real-time monitoring module, randomly sample the state of the system under different conditions, and generate the distribution of each indicator. The specific formula is: Among them, η represents the average value of the system index estimated by random samples, h l represents the input data generated in the lth sampling, f(h l ) means that when the input variable is h l When , the calculation result of the system index is, N represents the total number of random sampling, and l represents the sampling number.

5. The audience prediction and supervision system based on quantum computing according to claim 2 is characterized by: The data loading module converts the data to be predicted into a quantum state and loads it into the quantum computer. The calculation formula for the size m of the target feature space mapped is: m=log2(n); Among them, m represents the size of the mapped target feature space, n represents the total number of data to be predicted, and represents the rounding up operator.

6. The audience prediction and supervision system based on quantum computing according to claim 3 is characterized by: The prediction analysis module generates the energy value E(a) related to the audience characteristic state a in the audience prediction report according to the optimized measurement results as follows: E(a)=α×var(a)+β×D(a); Among them, E(a) represents the energy value associated with the audience characteristic value a, var(a) represents the variance of the characteristic value, D(a) represents the dispersion of the characteristic value, and α and β are weight coefficients.

7. The audience prediction and supervision system based on quantum computing according to claim 3 is characterized by: The prediction analysis module generates an audience prediction report according to the optimized measurement results, and further comprises: Set the probability threshold P threshold , when P(a)>P threshold , the audience characteristic value of the probability is screened out, where P(a) represents the probability of occurrence of audience characteristic value a, P threshold Indicates the probability threshold set according to the prediction task requirements.

8. The audience prediction and supervision system based on quantum computing according to claim 1 is characterized by: The measurement correction module dynamically adjusts quantum state measurement parameters to improve measurement accuracy based on a quantum measurement error correction algorithm and further includes: Obtain the standard deviation σ0 of the initial measurement result, calculate the difference λ between the current measurement result and the ideal measurement result, set the difference threshold τ, and if d>τ, let σ=σ0 / (1+λ), where σ represents the standard deviation of the updated measurement result, σ0 represents the standard deviation of the initial measurement result, λ represents the difference between the current measurement result and the ideal measurement result, and τ represents the threshold of the difference between the current measurement result and the ideal measurement result.

9. The audience prediction and supervision system based on quantum computing according to claim 1 is characterized by: The prediction analysis module generates an audience prediction report according to the optimized measurement results. The generated audience prediction report includes an audience characteristic probability distribution diagram, a characteristic influencing factor list and a future characteristic change trend prediction diagram.

10. The audience prediction and supervision system based on quantum computing according to claim 9 is characterized in that: The audience prediction report supports personalized generation, including user selection of features or feature groups to be focused on, adjustment of the presentation format of the report content according to user needs, and output of the report in a variety of formats.

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