Quantum computing auxiliary analysis and marketing prediction method for private domain flow pool data

Abstract

The invention provides a quantum computing auxiliary analysis and marketing prediction method for private domain traffic pool data, which comprises the following steps of: acquiring and processing user behavior data, coding the user behavior data into a quantum state by using a quantum algorithm, extracting user behavior characteristics by using a quantum support vector machine (QSVM), and analyzing a periodic mode through quantum Fourier transform, so as to realize the quantum computing auxiliary analysis and marketing prediction of the private domain traffic pool data. According to the method, a marketing prediction model is constructed based on a quantum algorithm, model parameters are optimized by using a quantum annealing technology, the prediction accuracy is improved, and the method further comprises the step of carrying out classical verification and optimization on a quantum calculation result so as to realize personalized marketing. According to the invention, efficient analysis and precise marketing prediction of the private domain traffic pool are realized, and the pertinence and effectiveness of enterprise marketing strategies are improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and specifically to a method for using quantum computing technology to assist in analyzing private domain traffic pool data and achieve marketing prediction. Background Art

[0002] With the rapid development of Internet technology, the marketing model of enterprises has gradually shifted from traditional public domain traffic acquisition to the construction and operation of private domain traffic pools. Private domain traffic pools refer to user groups accumulated by enterprises through social media, WeChat public accounts, mini programs, enterprise apps and other channels. These users have higher stickiness and conversion rates. However, how to effectively analyze private domain traffic pool data and achieve accurate marketing predictions has become a major challenge facing enterprises.

[0003] As a new computing model, quantum computing has unparalleled advantages over traditional computing. Quantum computers use the superposition and entanglement of quantum bits (qubits) to achieve exponential growth in computing power. Quantum annealing technology and quantum algorithms have shown great potential in solving combinatorial optimization problems and accelerating machine learning training. However, the application of quantum computing technology in the field of private traffic pool data analysis and marketing prediction is still immature, lacking targeted and practical solutions.

[0004] The existing technology of private domain traffic pool data analysis and marketing prediction is inaccurate, the search is limited, the calculation is slow, the security is insufficient, and the resource allocation is not optimal. Summary of the invention

[0005] In order to overcome the problems of inaccurate private traffic pool data analysis and marketing prediction in the prior art, limited search, slow calculation, insufficient security, and poor resource allocation, the present invention designs a quantum computing-assisted analysis and marketing prediction method for private traffic pool data that can effectively solve the Shanshu technical problems.

[0006] In order to solve the above technical problems, the technical solution of the present invention is as follows:

[0007] A quantum computing-assisted analysis and marketing prediction method for private domain traffic pool data, comprising the following steps:

[0008] Collect user behavior data from the private domain traffic pool and process it;

[0009] Use quantum algorithms to encode the collected user behavior data and convert it into quantum states;

[0010] Use quantum support vector machine to extract user behavior features;

[0011] Analyze periodic patterns in user behavior data through quantum Fourier transform;

[0012] Build a marketing prediction model based on quantum algorithms;

[0013] Utilizing quantum annealing technology to optimize the prediction model parameters and improve prediction accuracy;

[0014] The prediction results obtained by quantum computing are verified and optimized through classical computing, and the marketing strategy is adjusted according to the prediction results to achieve personalized marketing.

[0015] Preferably, the method of encoding the collected data using a quantum algorithm and converting the data into a quantum state comprises:

[0016] Map the processed user behavior data onto quantum bits, converting classical data into quantum states;

[0017] Initializing the qubits using quantum gate operations and applying quantum logic gates to manipulate the qubits;

[0018] Encode user behavior data into quantum states using quantum algorithms, including ground state encoding, angle encoding, and amplitude encoding;

[0019] Verify the correctness of the quantum state through quantum state tomography;

[0020] Creating superposition and quantum entanglement of said quantum states to exploit the parallelism of quantum computing;

[0021] The encoded quantum state is stored in a quantum register and transmitted.

[0022] Preferably, the extracting user behavior features by using a quantum support vector machine includes:

[0023] Initialize the QSVM model, including selecting the quantum kernel function and the number of quantum bits, and setting the QSVM model parameters;

[0024] Mapping the quantum state of the user behavior data to a high-dimensional feature space using a quantum kernel function;

[0025] Use the training data set to train the QSVM model, and adjust the QSVM model parameters through an iterative optimization algorithm to minimize the classification error;

[0026] During the training process, the QSVM model automatically learns and extracts the most useful features for classifying user behavior;

[0027] Use the validation data set to evaluate the performance of the QSVM model and make necessary adjustments and optimizations to the model based on the validation results;

[0028] The extracted user behavior features are output into a format that can be used for subsequent analysis and prediction.

[0029] Preferably, analyzing the periodic patterns in the data by quantum Fourier transform comprises:

[0030] Apply a Hadamard gate to each qubit to initialize it to a superposition state;

[0031] Applying control of rotating gates to achieve phase adjustment between quantum bits;

[0032] By applying the control rotating door multiple times iteratively, the quantum Fourier transform of the entire quantum state is completed;

[0033] After completing the quantum Fourier transform, the quantum state is measured to obtain frequency domain information. The measurement results will reflect the periodic patterns in the user behavior data.

[0034] Analyze the measurements to identify periodic patterns in user behavior data;

[0035] The periodic patterns obtained from quantum calculations are compared with the results of classical calculations to verify their accuracy and optimize them as needed.

[0036] Preferably, the construction of a marketing prediction model based on a quantum algorithm includes:

[0037] Choose the appropriate quantum algorithm;

[0038] Design the quantum model architecture, including input layers, hidden layers, and output layers;

[0039] Build quantum circuits to implement the computational process of quantum algorithms;

[0040] Training quantum models, including data encoding, training process, and parameter optimization;

[0041] Use classical computing to verify and optimize the predictions obtained from quantum computing;

[0042] Adjust marketing strategies based on prediction results to achieve personalized marketing.

[0043] Preferably, the method of optimizing the prediction model parameters by using quantum annealing technology to improve prediction accuracy includes:

[0044] The parameter optimization problem of the marketing forecasting model is transformed into a quadratic unconstrained binary optimization problem;

[0045] Construct the QUBO matrix to express the parameter optimization problem of the marketing forecasting model in matrix form;

[0046] Implement the quantum annealing algorithm, use quantum bits instead of classical bits, and find the global optimal solution through quantum superposition and quantum tunneling effects;

[0047] Initialize the instance pool, generate random solutions, and calculate the energy value of each solution;

[0048] Selecting the best solution from the pool of examples and performing quantum annealing optimization on each solution;

[0049] Extract sub-QUBO from the optimal solution, i.e., optimization problem focusing on some variables;

[0050] Use quantum annealing techniques to optimize sub-QUBO and find approximate ground state solutions to sub-problems;

[0051] combining the sub-solutions obtained by quantum annealing with the trial solution, and updating the trial solution;

[0052] Adding the updated trial solution to the instance pool and reordering the instance pool;

[0053] Repeat the above process until a satisfactory global optimal solution is found or the preset number of iterations is reached.

[0054] An electronic device, comprising:

[0055] A memory storing executable program code;

[0056] a processor coupled to the memory;

[0057] The processor calls the executable program code stored in the memory to execute the quantum computing-assisted analysis and marketing prediction method of private domain traffic pool data as described above.

[0058] A computer storage medium storing computer instructions, which, when called, are used to execute a quantum computing-assisted analysis and marketing prediction method for private domain traffic pool data as described above.

[0059] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention can use quantum algorithms to conduct deeper analysis of data through the analysis and marketing prediction method assisted by quantum computing, thereby improving the accuracy of marketing predictions. Quantum computing can achieve high-speed parallel computing, greatly improving search efficiency and capabilities. Especially when processing large amounts of data, quantum computers can explore multiple possibilities at the same time, which is difficult to achieve on traditional computers. Quantum computers can achieve exponential expansion and explosive growth in computing power, forming "quantum superiority". This high-speed parallel computing capability greatly improves the speed of processing complex problems. While improving computing power, quantum computing can also reduce energy consumption and achieve more efficient energy utilization. The development of quantum computing has an impact on existing encryption standards. It poses a threat, promotes the development of anti-quantum cryptographic technology, and enhances data security. Quantum annealing technology can optimize prediction model parameters and improve prediction accuracy, thereby achieving better resource allocation. Marketing strategies can be adjusted according to the prediction results obtained by quantum computing to achieve personalized marketing and improve user experience and satisfaction. The performance of the quantum annealing algorithm has improved by about 40% when solving specific NP-hard problems, showing its great potential in dealing with complex problems. The development of quantum annealing technology provides a powerful tool for achieving more efficient quantum algorithms. It is expected that this method will play an important role in future quantum computing and optimization problem solving. Quantum algorithms, such as quantum support vector machines (QSVM), can accelerate the training process of machine learning models and improve the efficiency of feature extraction. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived based on the provided drawings without paying any creative work.

[0061] Figure 1 A step diagram of a quantum computing-assisted analysis and marketing prediction method for private domain traffic pool data; DETAILED DESCRIPTION

[0062] The drawings are for illustrative purposes only and should not be construed as limiting the present patent;

[0063] In order to better illustrate the present embodiment, some parts in the drawings may be omitted, enlarged or reduced, and do not represent the size of the actual product;

[0064] It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0065] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0066] Example

[0067] A quantum computing-assisted analysis and marketing prediction method for private traffic pool data, such as Figure 1 As shown, the following steps are included:

[0068] Collect user behavior data from the private domain traffic pool and process it;

[0069] Use quantum algorithms to encode the collected user behavior data and convert it into quantum states;

[0070] Use quantum support vector machine (QSVM) to extract user behavior features;

[0071] Analyze periodic patterns in user behavior data through quantum Fourier transform (QFT);

[0072] Build a marketing prediction model based on quantum algorithms;

[0073] Utilizing quantum annealing technology to optimize the prediction model parameters and improve prediction accuracy;

[0074] The prediction results obtained by quantum computing are verified and optimized through classical computing, and the marketing strategy is adjusted according to the prediction results to achieve personalized marketing.

[0075] The method of encoding the collected data using a quantum algorithm and converting the data into a quantum state includes:

[0076] Map the processed user behavior data onto quantum bits (qubits), converting classical data such as binary numbers into quantum states;

[0077] For each data point, determine the number of qubits it corresponds to. For example, an n-bit data point may require n qubits to represent it.

[0078] This includes mapping data onto quantum bits using quantum bit flipping, quantum phase estimation, or quantum entanglement operations.

[0079] Initializing the qubits using quantum gate operations and applying quantum logic gates to manipulate the qubits;

[0080] Using quantum gate operations, such as the Hadamard gate, we initialize the qubit to a specific quantum state, usually starting from the |0> state.

[0081] Quantum logic gates, such as Pauli-X, Pauli-Y, and Pauli-Z gates, are used to manipulate quantum bits so that they reflect the characteristics of the data.

[0082] Encode user behavior data into quantum states using quantum algorithms, including ground state encoding, angle encoding, and amplitude encoding;

[0083] Ground state encoding: Encode user behavior data directly into the ground state of the quantum bit;

[0084] Angle encoding: Encode user behavior data into the phase of the quantum bit;

[0085] Amplitude coding: Encode user behavior data into the amplitude of the quantum state.

[0086] Verify the correctness of the quantum state through quantum state tomography;

[0087] Quantum algorithms are used to encode data into quantum states, including ground state encoding, angle encoding, and amplitude encoding.

[0088] The correctness of the quantum state is verified through Quantum State Tomography processing technology.

[0089] Creating superposition and quantum entanglement of said quantum states to exploit the parallelism of quantum computing;

[0090] Create a superposition of quantum states so that each qubit is in a superposition of multiple states to exploit the parallelism of quantum computing, and if necessary, create quantum entanglement to achieve correlation between qubits.

[0091] The encoded quantum state is stored in a quantum register and transmitted.

[0092] The method of extracting user behavior features by using a quantum support vector machine includes:

[0093] Initialize the QSVM model, including selecting the quantum kernel function and the number of quantum bits, and setting the QSVM model parameters;

[0094] Mapping the quantum state of the user behavior data to a high-dimensional feature space using a quantum kernel function;

[0095] Use the training data set to train the QSVM model, and adjust the QSVM model parameters through an iterative optimization algorithm to minimize the classification error;

[0096] During the training process, the QSVM model automatically learns and extracts the most useful features for classifying user behavior;

[0097] Use the validation data set to evaluate the performance of the QSVM model and make necessary adjustments and optimizations to the model based on the validation results;

[0098] The extracted user behavior features are output into a format that can be used for subsequent analysis and prediction.

[0099] The analysis of periodic patterns in data by quantum Fourier transform includes:

[0100] Apply a Hadamard gate to each qubit to initialize it to a superposition state;

[0101] After applying the Hadamard gate, each qubit will be in a superposition state of |0> and |1>;

[0102] A controlled rotating gate (CR) is applied to achieve phase adjustment between qubits, specifically:

[0103]

[0104] Among them, k is the parameter that controls the revolving door, and i represents the angle of rotation.

[0105] The function of the CR gate is to apply a rotation R to the target qubit when the control qubit is in the |1> state. k ;

[0106] By applying the controlled rotating gate multiple times in iterations, a quantum Fourier transform (QFT) of the entire quantum state is completed;

[0107] After completing the quantum Fourier transform, the quantum state is measured to obtain frequency domain information. The measurement results will reflect the periodic patterns in the user behavior data.

[0108] Analyze the measurements to identify periodic patterns in the user behavior data; this can be done by calculating the frequency distribution and identifying the main frequency components;

[0109] The periodic patterns obtained from quantum calculations are compared with the results of classical calculations to verify their accuracy and optimize them as needed.

[0110] The marketing prediction model constructed based on quantum algorithm includes:

[0111] Choose a suitable quantum algorithm; such as quantum neural network.

[0112] Design the quantum model architecture, including input layers, hidden layers, and output layers;

[0113] Build quantum circuits to implement the computational process of quantum algorithms;

[0114] Specifically: determine the complexity of the problem and the amount of data that needs to be processed;

[0115] Determine the number of qubits required based on data characteristics and algorithm requirements;

[0116] Initialize all qubits to the ground state |0…0>; if necessary, apply a Hadamard gate (H gate) to put the qubits into a superposition state for parallel computation.

[0117] Encoding classical data into quantum states involves the following steps:

[0118] Map each data point to a quantum state, for example, using quantum encoding techniques such as Amplitude Encoding or Qubit Encoding;

[0119] Apply quantum logic gates to encode data onto qubits;

[0120] Design the corresponding quantum circuit according to the selected quantum algorithm;

[0121] Determine the required quantum gates and how to connect them

[0122] Quantum gate layer, for quantum neural networks, a series of quantum gate layers are designed, each layer contains multiple quantum gates to perform specific computing tasks; for example, for quantum neural networks, the following quantum gates may be included: CNOT gate, rotation gate (Rz, Ry, Rx), Toffoli gate, etc.

[0123] Parameterized quantum gates, in order to train quantum models, parameterized quantum gates are used, such as rotating gates, whose angles can be used as adjustable parameters during training;

[0124] Entanglement structure designs the entanglement structure between qubits to enhance the expressive power of quantum algorithms. For example, CNOT gates can be used to create entanglement between qubits.

[0125] Training quantum models, including data encoding, training process, and parameter optimization;

[0126] Use classical computing to verify and optimize the predictions obtained from quantum computing;

[0127] Adjust marketing strategies based on prediction results to achieve personalized marketing.

[0128] The use of quantum annealing technology to optimize the prediction model parameters and improve the prediction accuracy includes:

[0129] The parameter optimization problem of the marketing forecasting model is transformed into a quadratic unconstrained binary optimization (QUBO) problem;

[0130] Construct the QUBO matrix to express the parameter optimization problem of the marketing forecasting model in matrix form;

[0131] Implement the quantum annealing algorithm, use quantum bits instead of classical bits, and find the global optimal solution through quantum superposition and quantum tunneling effects;

[0132] Initialize the instance pool, generate random solutions, and calculate the energy value of each solution;

[0133] Select the best solution from the pool of examples and perform quantum annealing optimization on each solution;

[0134] Extract sub-QUBO from the best solution, that is, the optimization problem focusing on some variables;

[0135] Use quantum annealing techniques to optimize sub-QUBO and find approximate ground state solutions to sub-problems;

[0136] Combine the sub-solutions obtained by quantum annealing with the trial solution and update the trial solution;

[0137] Add the updated trial solution to the instance pool and reorder the instance pool;

[0138] Repeat the above process until a satisfactory global optimal solution is found or the preset number of iterations is reached.

[0139] An electronic device, comprising:

[0140] A memory storing executable program code;

[0141] a processor coupled to the memory;

[0142] The processor calls the executable program code stored in the memory to execute the quantum computing-assisted analysis and marketing prediction method of private domain traffic pool data as described above.

[0143] A computer storage medium storing computer instructions, which, when called, are used to execute a quantum computing-assisted analysis and marketing prediction method for private traffic pool data as described above.

[0144] The same or similar reference numerals correspond to the same or similar components;

[0145] The terms used in the drawings to describe positional relationships are only used for illustrative purposes and should not be construed as limiting this patent;

[0146] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not limitations on the implementation methods of the present invention. For ordinary technicians in the relevant field, other different forms of changes or modifications can be made on the basis of the above description. It is not necessary and impossible to list all the implementation methods here. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the claims of the present invention.

Claims

1. A quantum computing-assisted analysis and marketing prediction method for private domain traffic pool data, characterized in that: The following steps are involved: Collect user behavior data from the private domain traffic pool and process it; Use quantum algorithms to encode the collected user behavior data and convert it into quantum states; Use quantum support vector machine to extract user behavior features; Analyze periodic patterns in user behavior data through quantum Fourier transform; Build a marketing prediction model based on quantum algorithms, and use quantum annealing technology to optimize the parameters of the prediction model to improve prediction accuracy; The prediction results obtained by quantum computing are verified and optimized through classical computing, and the marketing strategy is adjusted according to the prediction results to achieve personalized marketing.

2. According to claim 1, a quantum computing-assisted analysis and marketing prediction method for private domain traffic pool data is characterized in that: The method of encoding the collected data using a quantum algorithm and converting the data into a quantum state includes: Map the processed user behavior data onto quantum bits, converting classical data into quantum states; Initializing the qubits using quantum gate operations and applying quantum logic gates to manipulate the qubits; Encode user behavior data into quantum states using quantum algorithms, including ground state encoding, angle encoding, and amplitude encoding; Verify the correctness of the quantum state through quantum state tomography; Creating superposition and quantum entanglement of said quantum states to exploit the parallelism of quantum computing; The encoded quantum state is stored in a quantum register and transmitted.

3. According to claim 1, a quantum computing-assisted analysis and marketing prediction method for private domain traffic pool data is characterized in that: The method of extracting user behavior features by using a quantum support vector machine includes: Initialize the QSVM model, including selecting the quantum kernel function and the number of quantum bits, and setting the QSVM model parameters; Mapping the quantum state of the user behavior data to a high-dimensional feature space using a quantum kernel function; Use the training data set to train the QSVM model, and adjust the QSVM model parameters through an iterative optimization algorithm to minimize the classification error; During the training process, the QSVM model automatically learns and extracts the most useful features for classifying user behavior; Use the validation data set to evaluate the performance of the QSVM model and make necessary adjustments and optimizations to the model based on the validation results; The extracted user behavior features are output into a format that can be used for subsequent analysis and prediction.

4. According to claim 1, a quantum computing-assisted analysis and marketing prediction method for private domain traffic pool data is characterized in that: The analysis of periodic patterns in data by quantum Fourier transform includes: Apply a Hadamard gate to each qubit to initialize it to a superposition state; Applying control of rotating gates to achieve phase adjustment between quantum bits; By applying the control rotating door multiple times iteratively, the quantum Fourier transform of the entire quantum state is completed; After completing the quantum Fourier transform, the quantum state is measured to obtain frequency domain information. The measurement results will reflect the periodic patterns in the user behavior data. Analyze the measurements to identify periodic patterns in user behavior data; The periodic patterns obtained from quantum calculations are compared with the results of classical calculations to verify their accuracy and optimize them as needed.

5. According to claim 1, a quantum computing-assisted analysis and marketing prediction method for private domain traffic pool data is characterized in that: The marketing prediction model constructed based on quantum algorithm includes: Choose the appropriate quantum algorithm; Design the quantum model architecture, including input layers, hidden layers, and output layers; Build quantum circuits to implement the computational process of quantum algorithms; Training quantum models, including data encoding, training process, and parameter optimization; Use classical computing to verify and optimize the predictions obtained from quantum computing; Adjust marketing strategies based on prediction results to achieve personalized marketing.

6. According to claim 1, a quantum computing-assisted analysis and marketing prediction method for private domain traffic pool data is characterized in that: The use of quantum annealing technology to optimize the prediction model parameters and improve the prediction accuracy includes: The parameter optimization problem of the marketing forecasting model is transformed into a quadratic unconstrained binary optimization problem; Construct the QUBO matrix to express the parameter optimization problem of the marketing forecasting model in matrix form; Implement the quantum annealing algorithm, use quantum bits instead of classical bits, and find the global optimal solution through quantum superposition and quantum tunneling effects; Initialize the instance pool, generate random solutions, and calculate the energy value of each solution; Selecting the best solution from the pool of examples and performing quantum annealing optimization on each solution; Extract sub-QUBO from the optimal solution, i.e., optimization problem focusing on some variables; Use quantum annealing techniques to optimize sub-QUBO and find approximate ground state solutions to sub-problems; combining the sub-solutions obtained by quantum annealing with the trial solution, and updating the trial solution; Adding the updated trial solution to the instance pool and reordering the instance pool; Repeat the above process until a satisfactory global optimal solution is found or the preset number of iterations is reached.

7. An electronic device, characterized in that: The electronic device comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute a quantum computing-assisted analysis and marketing prediction method for private domain traffic pool data as described in any one of claims 1-6.

8. A computer storage medium, characterized in that: The computer storage medium stores computer instructions, which, when called, are used to execute a quantum computing-assisted analysis and marketing prediction method for private domain traffic pool data as described in any one of claims 1-6.

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