A quantum computing-based audience prediction and supervision system

Abstract

This invention discloses a quantum computing-based audience prediction and monitoring system, relating to the fields of quantum computing and data analysis technology. The system includes a data loading module for converting data to be predicted into quantum states and loading it into a quantum computer; a quantum evolution module for performing evolution operations on the quantum states based on pre-trained quantum circuits; a measurement correction module for dynamically adjusting quantum state measurement parameters to improve measurement accuracy based on a quantum measurement error correction algorithm; a prediction analysis module for generating an audience prediction report based on the optimized measurement results; and a real-time monitoring module for real-time monitoring of the audience prediction process. This quantum computing-based audience prediction and monitoring system solves the problem of how existing technologies can control the accuracy of quantum state measurements to address the reliability of the results.

Description

Technical Field

[0001] This invention relates to the field of quantum computing and data analysis technology, specifically to an audience prediction and monitoring system based on quantum computing. Background Technology

[0002] In modern information technology, quantum computing, with its superior parallel computing capabilities and efficient handling of complex problems, is increasingly being applied to fields such as data analysis, machine learning, and predictive analytics. Audience prediction, as an important application scenario in data analysis, is widely used in marketing, content recommendation, and user behavior analysis, and its accuracy and reliability 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 amounts of 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 perform efficient prediction analysis in a short time by utilizing the superposition and entanglement properties of quantum states. However, quantum state measurements in quantum computing have a certain degree of uncertainty, and the measurement results depend on the collapse process of the quantum state, making the accuracy of the measurement a critical issue in practical applications. If the accuracy of the quantum state measurement is insufficient, the prediction results may be biased, thereby reducing the reliability of the results and negatively impacting subsequent decision-making. In existing technologies, the control 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 computational resources and may even cause instability in the measurement results. At the same time, due to the susceptibility of quantum states to environmental noise, traditional error correction methods are inefficient when applied to complex quantum algorithms, further increasing the difficulty of measurement control. Summary of the Invention

[0004] The purpose of this invention is to provide an audience prediction and monitoring system based on quantum computing, which solves the problem of how to control the accuracy of quantum state measurements in order to address the reliability of the results in existing technologies.

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

[0006] The data loading module is used to convert the data to be predicted into a quantum state and load it into the quantum computer;

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

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

[0009] Where, x i (t) represents the characteristic parameters 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 parameter during the interaction, t represents time, d represents the condition for interaction between quantum states, i represents the number of the currently evolving quantum state, and j represents the number of the other quantum state that interacts with quantum state i.

[0010] The measurement correction module, connected to the quantum evolution module, is used to dynamically adjust quantum state measurement parameters to improve measurement accuracy based on a quantum measurement error correction algorithm. This includes 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 as follows:

[0011] f s ≥2f max ;

[0012] Among them, f s f represents the frequency at which data is sampled from the quantum state measurement results during quantum measurement. max This represents the highest frequency of change contained in the quantum state error during quantum state measurement;

[0013] The predictive analysis module, connected to the measurement correction module, is used to generate an audience prediction report based on the optimized measurement results;

[0014] The real-time monitoring module, connected to the predictive analytics 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 it into the quantum computer, including:

[0016] Feature extraction is performed on the data to be predicted, multidimensional data vectors are transformed into feature key-value pairs, features are mapped to feature space, data dimensionality is reduced, the mapped data is transformed into quantum state and loaded into quantum computer, and the specific formula for mapping features to feature space is h(y) = y mod m;

[0017] Where y represents the feature value to be predicted, m represents the size of the target feature space to be mapped, h(y) represents the result of the feature value to be predicted after transformation, and mod represents the modulo operation.

[0018] Preferably, the predictive analysis module generates an audience prediction report based on the optimized measurement results, including:

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

[0020] Where P(a) represents the probability of the occurrence of audience characteristic value a, a represents one of the audience characteristic values, E(a) represents the energy value associated with 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 monitors the audience prediction process in real time, including:

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

[0023]

[0024] Where η represents the average value of the system index estimated from the random sample, and h l f(h) represents the input data generated in the l-th sampling. l ) indicates that when the input variable is h l When the system index is calculated, N represents the total number of random samples and l represents the sample number.

[0025] Preferably, the data loading module converts the data to be predicted into a quantum state and loads it into the target feature space mapped in the quantum computer. The formula for calculating the size m is: m = log2(n);

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

[0027] This represents the floor function operator.

[0028] Preferably, the formula for calculating the energy value E(a) related to audience characteristic state a in the audience prediction report generated by the predictive analysis module based on the optimized measurement results is as follows:

[0029] E(a) = α × var(a) + β × D(a);

[0030] Where E(a) represents the energy value associated with the audience feature value a, var(a) represents the variance of the feature value, D(a) represents the dispersion of the feature value, and α and β are weighting coefficients.

[0031] Preferably, the predictive analysis module, based on the optimized measurement results, further generates an audience prediction report, including:

[0032] Set probability threshold P threshold When P(a) > P threshold When selecting audience feature values ​​with that probability, P(a) represents the probability of audience feature value a occurring. threshold This represents the probability threshold set based on the requirements of the prediction task.

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

[0034] Obtain the standard deviation σ0 of the initial measurement result, calculate the difference λ between the current measurement result and the ideal measurement result, and set the difference threshold τ. If d>τ, then make 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.

[0035] Preferably, the predictive analysis module generates an audience prediction report based on the optimized measurement results. The generated audience prediction report includes an audience characteristic probability distribution map, a list of characteristic influencing factors, and a prediction map of future characteristic change trends.

[0036] Preferably, the audience prediction report supports personalized generation, including allowing users to select features or feature groups to focus on, adjusting the display format of the report content according to user needs, and outputting the report in multiple formats.

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

[0038] This quantum computing-based audience prediction and monitoring system transforms the data to be predicted into quantum states and loads it into a quantum computer through a data loading module. A quantum evolution module performs evolution operations on the quantum states based on pre-trained quantum circuits. A measurement correction module dynamically adjusts the quantum state measurement parameters to improve measurement accuracy based on a quantum measurement error correction algorithm. A prediction analysis module generates an audience prediction report based on the optimized measurement results. A real-time monitoring module monitors the audience prediction process in real time. This effectively improves the accuracy of quantum measurements, reduces errors in prediction analysis, enhances the reliability of prediction results, and reduces computational resource consumption caused by repeated experiments or algorithm structure adjustments. Thus, while meeting the requirements for high-precision measurement, it improves computational efficiency, reduces the sensitivity of quantum states to environmental noise, and ensures the stability of measurement results. It can efficiently process nonlinear, multi-dimensional audience characteristic data, complete the prediction analysis of complex models, significantly improve the system's analytical capabilities and response speed, and reduce the modeling and computational difficulty of complex problems. It provides a simpler and more efficient solution for practical applications, maintains high prediction accuracy and reliability in complex environments, adapts to the needs of changing application scenarios, and solves the problem of how existing technologies can control the accuracy of quantum state measurements to address the reliability of results. Attached Figure Description

[0039] Figure 1 This is a schematic diagram of the system module connections of the present invention. Detailed Implementation

[0040] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort 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 monitoring system based on quantum computing, the system comprising:

[0042] The data loading module is used to convert the data to be predicted into a quantum state and load it into the quantum computer;

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

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

[0045] Where, x i (t) represents the characteristic parameters 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 parameter during the interaction, t represents time, d represents the condition for interaction between quantum states, i represents the number of the currently evolving quantum state, and j represents the number of the other quantum state that interacts with quantum state i.

[0046] The measurement correction module, connected to the quantum evolution module, is used to dynamically adjust quantum state measurement parameters to improve measurement accuracy based on a quantum measurement error correction algorithm. This includes 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 as follows:

[0047] f s ≥2f max ;

[0048] Among them, f s f represents the frequency at which data is sampled from the quantum state measurement results during quantum measurement. max This represents the highest frequency of change contained in the quantum state error during quantum state measurement;

[0049] The predictive analysis module, connected to the measurement correction module, is used to generate an audience prediction report based on the optimized measurement results;

[0050] The real-time monitoring module, connected to the predictive analytics 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, improving data processing efficiency through the characteristics of quantum computing. Next, the quantum evolution module uses pre-trained quantum circuits to simulate the interactions 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] The characteristic parameters of the quantum state are dynamically adjusted. Here, μ represents the quantum state sensitivity, and d is the triggering condition for the interaction. This interaction is mapped to an operational sequence of the quantum circuit, completing the evolution of the quantum state. After the evolution is complete, the measurement correction module adjusts the measurement frequency and parameters using a quantum measurement error correction algorithm. This is achieved through the formula:

[0054] f s ≥2f max ;

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

[0056] This implementation method, by introducing quantum computing technology, significantly improves data processing speed and analysis accuracy compared to traditional prediction methods. The quantum evolution module can simulate the interaction processes of complex quantum states, revealing deep-seated relationships that are difficult to capture using traditional methods. The measurement correction module further optimizes quantum measurement results, reducing the impact of measurement errors on the final results and improving the reliability of the prediction analysis. Furthermore, the real-time monitoring module ensures the real-time nature of the audience prediction process, providing timely feedback and adjustment capabilities for dynamic audience changes.

[0057] The data loading module transforms the data to be predicted into a quantum state and loads it into the quantum computer. This includes feature extraction of the data to be predicted, transforming multidimensional data vectors into feature key-value pairs, mapping features to feature space, reducing data dimensionality, transforming the mapped data into a quantum state, and loading it into the quantum computer. The specific formula for mapping features to feature space is h(y) = y mod m.

[0058] Where y represents the feature value to be predicted, m represents the size of the target feature space to be mapped, h(y) represents the result of the feature value to be predicted after transformation, and mod represents the modulo operation.

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

[0060] The predictive 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 audience characteristic distribution. The specific formula is as follows:

[0061]

[0062] Where P(a) represents the probability of the occurrence of audience characteristic value a, a represents one of the audience characteristic values, E(a) represents the energy value associated with 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 predictive analysis module uses the optimized quantum measurement results as a basis and employs a probability distribution model to calculate the probability of the audience characteristics appearing. This is achieved through the formula...

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

[0065] By analyzing audience characteristic distribution, a detailed audience prediction report is generated, which covers the core characteristics, distribution trends and possible behavioral patterns of the audience.

[0066] This implementation combines quantitative and probabilistic models with quantum computing measurement results to efficiently generate accurate audience prediction reports. Compared to traditional statistical methods, distribution analysis based on quantum measurements better reveals the complex relationships between audience characteristics, significantly improving the accuracy and reliability of predictions. The introduction of the temperature parameter T gives the model stronger controllability, allowing it to flexibly adapt to different data characteristics and application scenarios. Furthermore, the introduction of the normalization factor Z ensures the mathematical rigor of the model calculations, resulting in prediction reports with higher scientific validity and applicability.

[0067] The real-time monitoring module monitors the audience prediction process in real time. This includes defining the indicators that need to be monitored in the real-time monitoring module, randomly sampling the system's state under different conditions, and generating the distribution of each indicator. The specific formula is as follows:

[0068]

[0069] Where η represents the average value of the system index estimated from the random sample, and h l f(h) represents the input data generated in the l-th sampling. l ) indicates that when the input variable is h l When the system index is calculated, N represents the total number of random samples and l represents the sample number.

[0070] In this embodiment, the real-time monitoring module dynamically analyzes the system state during the audience prediction process using a random sampling method. First, key system indicators to be monitored are defined, such as prediction accuracy, quantum measurement error rate, and computational resource utilization. Then, an input variable set {h1,h2,…,hN} is generated through random sampling, and the corresponding system indicator f(h) is calculated for each sample. 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 visualizes these analysis results to capture the changing trends of the system under dynamic conditions and provides an alarm mechanism for abnormal states during the prediction process. This implementation method, through the random sampling method of the real-time monitoring module, can efficiently capture changes in the system state under dynamic conditions, significantly improving the robustness and stability of the prediction system. The average value η of the system indicators provides a quantitative assessment of overall performance, helping to quickly locate performance bottlenecks or potential problems. Compared with traditional static monitoring methods, the random sampling method has greater flexibility and applicability, especially in complex quantum systems where monitoring strategies can be adjusted in real time. The distributed analysis results and visualization function further enhance the operability and user-friendliness of the system.

[0071] The data loading module transforms the data to be predicted into a quantum state and loads it into the target feature space mapped in the quantum computer. The formula for calculating the size m is: m = log2(n);

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

[0073] This represents the floor function operator.

[0074] In this embodiment, the size m of the feature space of the data loading module is calculated based on the total number n of data to be predicted, determined by the formula m = log2(n). 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 qubit resources required for quantum computing. For a total amount of data of size n, mapping to a target feature space of size m can effectively reduce dimensionality without losing key information. By rounding up, the size m of the target feature space is ensured to always be an integer, meeting the basic requirements for qubit allocation. The dynamic adjustment of the feature space allows the data loading module to flexibly adapt to datasets of different sizes, while optimizing the utilization of quantum computing resources. This embodiment significantly reduces the consumption of qubit resources while ensuring data processing accuracy by dynamically adjusting the size m of the target feature space. The formula m = log2(n) ensures the matching between the feature space size and the total amount of data n, thereby avoiding redundancy or insufficiency of the feature space. Compared with a fixed-size feature space partitioning, 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, laying an optimized foundation for subsequent quantum evolution and measurement stages.

[0075] The predictive analysis module generates an audience prediction report based on optimized measurement results. The formula for calculating the energy value E(a) related to audience characteristic state a is as follows:

[0076] E(a) = α × var(a) + β × D(a);

[0077] Where E(a) represents the energy value associated with the audience feature value a, var(a) represents the variance of the feature value, D(a) represents the dispersion of the feature value, and α and β are weighting coefficients.

[0078] In this embodiment, the predictive analysis module calculates the energy value E(a) related to the audience characteristic state 'a' by combining the variance and dispersion of the feature values. The formula E(a) = α × var(a) + β × D(a) integrates the statistical characteristics of feature value 'a' into an energy value, quantifying its importance in audience prediction. Variance var(a) measures the fluctuation range of feature value 'a', reflecting the magnitude of its variation across the entire prediction sample. A larger variance indicates greater fluctuation in the feature value, potentially having a more significant impact on audience behavior. Dispersion D(a) describes the sparsity of the feature value distribution, representing the relative differences between feature values. The definition of dispersion can be adjusted according to specific needs, such as based on the average distance between feature values ​​or other statistical indicators. Weighting coefficients α and β are used to balance the contributions of variance and dispersion to the energy value calculation and are dynamically adjusted according to specific application scenarios. Through this method, the predictive analysis module can more accurately identify the feature values ​​most influential on audience behavior prediction, resulting in a more targeted and interpretable audience prediction report. This embodiment significantly improves the accuracy and reliability of audience characteristic state analysis through the calculation formula of the energy value E(a). The combination of variance var(a) and dispersion 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 the weighting coefficients α and β allows the system to adjust the focus of energy value calculation according to actual needs, improving the applicability and interpretability of the report. Compared with traditional single-feature analysis methods, this approach is more systematic and adaptable, providing scientific quantitative support for audience prediction.

[0079] The predictive analysis module generates an audience prediction report based on the optimized measurement results, and also includes setting a probability threshold P. threshold When P(a) > P threshold When selecting audience feature values ​​with that probability, P(a) represents the probability of audience feature value a occurring. threshold This represents the probability threshold set based on the requirements of the prediction task.

[0080] In this embodiment, the predictive analysis module performs probability calculations on the optimized measurement results to filter out audience feature values ​​that meet the criteria. First, a probability threshold P is set according to the requirements of the prediction task. threshold This is used to define the screening criteria. Then, it is calculated using the formula. The probability P(a) of each feature value a is calculated. When P(a) > P0, the probability of occurrence is calculated. threshold When this happens, the feature value 'a' is marked as an important feature and filtered out. This filtering process effectively eliminates interference from low-probability feature values, focusing attention on high-probability important features, thereby improving the accuracy and relevance of the prediction report. Users can dynamically adjust P according to the actual needs of the prediction task. thresholdThis allows for control over the range and accuracy of the screening results. This implementation method sets a probability threshold P. threshold This method effectively filters out high-probability, important audience feature values, thereby optimizing predictive analysis results. The filtering mechanism significantly reduces interference from redundant features, making the prediction report more targeted and interpretable. Compared to methods that directly analyze all feature values, this approach reduces data processing complexity and improves system efficiency. Dynamically adjusting P... threshold The system's functionality allows it to adapt flexibly to different scenarios, meeting diverse prediction needs. Furthermore, the selected key features provide a more accurate basis for subsequent audience behavior analysis and decision-making, significantly enhancing the report's practical application value.

[0081] The measurement correction module is based on a quantum measurement error correction algorithm. It dynamically adjusts the quantum state measurement parameters to improve measurement accuracy. It also includes obtaining the standard deviation σ0 of the initial measurement result, calculating the difference λ between the current measurement result and the ideal measurement result, and setting the difference threshold τ. If d>τ, then 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.

[0082] In this embodiment, the measurement correction module achieves efficient correction of quantum measurement errors by dynamically adjusting the standard deviation σ of quantum state measurements. First, the standard deviation σ0 of the initial measurement result is obtained as the reference value for the measurement system. Next, 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 it with a set threshold τ, when the error λ exceeds the threshold, the correction standard deviation σ is dynamically adjusted according to the formula σ = σ0 / (1+λ). This adjustment mechanism enhances the accuracy of quantum state measurement results and reduces the impact of errors on the measurement results by reducing the standard deviation σ. Through the dynamic adjustment process, the measurement correction module can adapt to different measurement conditions, ensuring that the measurement results are always maintained within a high accuracy range. This embodiment 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 error and effectively reduce the impact of error accumulation on subsequent predictive analysis. Compared with measurement methods with a fixed standard deviation, this scheme has stronger adaptability and robustness, and is suitable for the dynamic measurement needs of complex quantum systems. By introducing a difference threshold τ, the system can flexibly set the error tolerance, thereby balancing measurement accuracy and computational efficiency, and providing reliable data support for subsequent quantum computing and analysis.

[0083] The predictive analysis module generates an audience prediction report based on the optimized measurement results. This report includes an audience characteristic probability distribution map, a list of characteristic influencing factors, and a prediction map of future characteristic change trends. In this embodiment, the predictive analysis module analyzes and visualizes the optimized quantum measurement results to generate an audience prediction report with multi-level information, specifically including the following three parts: Audience characteristic probability distribution map: using formula... The probability of each feature value 'a' is calculated and displayed in the form of a bar chart or line graph, forming a probability distribution map of audience features, which intuitively reflects the distribution trend and probability magnitude of audience features. Feature Influence Factor List: By calculating the influence factors of each audience feature in the prediction model, such as the feature value's weight, contribution rate, or importance ranking, a feature influence factor list is generated to help users understand the degree of influence of each feature on audience behavior prediction. The list can be presented in tabular form, sorted from high to low according to the influence factors, with corresponding numerical explanations. Future Feature Change Trend Prediction Map: Based on historical measurement data and current optimization results, a prediction map of the change trend of audience features is generated through time series analysis or machine learning prediction models. The prediction map displays the trend of feature values ​​changing over time in the form of a line graph, providing an intuitive view of possible future feature changes. The above report generation process fully utilizes the high precision and efficiency of quantum computing measurement results, combined with statistical analysis and visualization techniques, to provide comprehensive and clear decision support for audience prediction. The audience prediction report generated by this implementation method integrates multi-dimensional information, including the current feature distribution, feature importance, and future change trends, providing users with a comprehensive and accurate reference for audience behavior prediction. The probability distribution plot visually reveals the overall distribution patterns of audience characteristics, helping to quickly identify key features. The feature influencing factor list quantifies the importance of each feature, providing data support for subsequent decision-making. The future trend prediction plot expands the depth of predictive analysis through the time dimension, making the system more forward-looking and practical. Compared to traditional single-dimensional prediction reports, this solution significantly enhances information integration capabilities, improving the scientific rigor of the report and the user experience.

[0084] The audience prediction report supports personalized generation, including user selection of features or feature groups to focus on, adjustment of report content display format according to user needs, and output of reports in multiple formats. In this embodiment, the audience prediction report module combines specific user needs to achieve personalized generation of report content, specifically including the following functions: User selection of features or feature groups to focus on: The system provides an interactive interface that allows users to select a single feature or multiple feature groups to focus on from the prediction feature list. After the user selects, the system generates a corresponding probability distribution map, feature influence factor list, and trend prediction map only for the selected feature or feature group, to simplify information presentation and focus on key content. Adjustment of report content display format: The system supports diversified display of report content according to user preferences. For example: Chart display: Bar charts, line charts, heat maps, or other visualization methods can be selected. Arrangement order: Users can sort the content according to the importance of features or other indicators. Data summary: Users can choose to present the analysis results in the form of a text summary or a detailed data table. Support for 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 archiving. Excel tables: facilitate further data analysis and processing. HTML format: for online viewing and sharing. Editable document formats (such as Word): allow users to modify content according to their needs. Through these functions, the system can generate highly customized audience prediction reports based on user requirements, meeting diverse application scenarios. This implementation significantly improves the system's user-friendliness and practicality by generating personalized audience prediction reports. Users can independently select features or feature groups to focus on, avoiding redundancy in report content and making the analysis results more targeted. The function of supporting adjustable display formats provides diverse content presentation methods to meet the preferences and needs of different users. Furthermore, the multiple report output formats adapt to the requirements of different application scenarios (such as archiving, analysis, display, or sharing), greatly improving the flexibility and applicability of the reports. Compared with fixed-format report generation methods, this solution better meets users' personalized needs and enhances the system's market competitiveness.

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

Claims

1. An audience prediction and monitoring system based on quantum computing, characterized in that, The system includes: The data loading module is used to convert the data to be predicted into a quantum state and load it into the quantum computer; The quantum evolution module, connected to the data loading module, is used to perform evolutionary operations on quantum states based on pre-trained quantum circuits. This includes simulating interactions between different sub-states of the quantum state, adjusting the evolution path, mapping the interaction results to an operation sequence of the quantum circuit, and completing the evolution of the quantum state. The specific formula for simulating interactions between different sub-states of the quantum state is as follows: x i (t+1)=x i (t)+μ(x j (t)-x i (t)),|x j (t)-x i (t)|<d; Where, x i (t) represents the characteristic parameters 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 parameter during the interaction, t represents time, d represents the condition for interaction between quantum states, i represents the number of the currently evolving quantum state, and j represents the number of the other quantum state that interacts with quantum state i. The measurement correction module, connected to the quantum evolution module, is used to dynamically adjust quantum state measurement parameters to improve measurement accuracy based on a quantum measurement error correction algorithm. This includes 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 as follows: f s ≥2f max ; Among them, f s f represents the frequency at which data is sampled from the quantum state measurement results during quantum measurement. max This represents the highest frequency of change contained in the quantum state error during quantum state measurement; The predictive analysis module, connected to the measurement correction module, is used to generate an audience prediction report based on the optimized measurement results; The real-time monitoring module, connected to the predictive analytics module, is used to monitor the audience prediction process in real time.

2. The audience prediction and monitoring system based on quantum computing according to claim 1, 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: Feature extraction is performed on the data to be predicted, multidimensional data vectors are transformed into feature key-value pairs, features are mapped to feature space, data dimensionality is reduced, the mapped data is transformed into quantum state and loaded into quantum computer, and the specific formula for mapping features to feature space is h(y) = y mod m; Where y represents the feature value to be predicted, m represents the size of the target feature space to be mapped, h(y) represents the result of the feature value to be predicted after transformation, and mod represents the modulo operation.

3. The audience prediction and monitoring system based on quantum computing according to claim 1, characterized in that: The predictive analysis module generates an audience prediction report based on the optimized measurement results, including: Calculate the probability of occurrence of each optimized quantum measurement result and analyze the audience characteristic distribution. The specific formula is as follows: Where P(a) represents the probability of the occurrence of audience characteristic value a, a represents one of the audience characteristic values, E(a) represents the energy value associated with 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 monitoring system based on quantum computing according to claim 1, characterized in that: The real-time monitoring module monitors the audience prediction process in real time, including: Define the indicators that need to be monitored in the real-time monitoring module, randomly sample the system's state under different conditions, and generate the distribution of each indicator. The specific formula is as follows: Where η represents the average value of the system index estimated from the random sample, and h l f(h) represents the input data generated in the l-th sampling. l ) indicates that when the input variable is h l When the system index is calculated, N represents the total number of random samples and l represents the sample number.

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

6. The audience prediction and monitoring system based on quantum computing according to claim 3, characterized in that: The predictive analysis module generates an audience prediction report based on the optimized measurement results. The formula for calculating the energy value E(a) related to audience characteristic state a is as follows: E(a) = α × var(a) + β × D(a); Where E(a) represents the energy value associated with the audience feature value a, var(a) represents the variance of the feature value, D(a) represents the dispersion of the feature value, and α and β are weighting coefficients.

7. The audience prediction and monitoring system based on quantum computing according to claim 3, characterized in that: The predictive analysis module, which generates an audience prediction report based on the optimized measurement results, also includes: Set probability threshold P threshold When P(a) > P threshold When selecting audience feature values ​​with that probability, P(a) represents the probability of audience feature value a occurring. threshold This represents the probability threshold set based on the requirements of the prediction task.

8. The audience prediction and monitoring system based on quantum computing according to claim 1, characterized in that: The measurement correction module, based on a quantum measurement error correction algorithm, dynamically adjusts quantum state measurement parameters to improve measurement accuracy and also 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, and set the difference threshold τ. If d>τ, then make 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 monitoring system based on quantum computing according to claim 1, characterized in that: The predictive analysis module generates an audience prediction report based on the optimized measurement results. The generated audience prediction report includes an audience characteristic probability distribution map, a list of characteristic influencing factors, and a prediction map of future characteristic change trends.

10. The audience prediction and monitoring system based on quantum computing according to claim 9, characterized in that: The audience prediction report supports personalized generation, including allowing users to select the features or feature groups they want to focus on, adjusting the display format of the report content according to user needs, and outputting the report in multiple formats.

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