Hydrodynamics intelligent computing platform based on quantum-AI hybrid architecture

Abstract

The invention belongs to the technical field of engineering and science, and provides a quantum-AI hybrid architecture-based fluid dynamics intelligent computing platform, which comprises a platform architecture, and is characterized in that the platform architecture comprises a data acquisition module, a quantum computing module, an AI processing module, a data transmission module and a data storage module; compared with the prior art, the fluid dynamics intelligent computing platform based on the quantum-AI hybrid architecture has remarkable beneficial effects in multiple aspects, has higher computing efficiency, simulation precision, model optimization capability, data analysis capability and cross-scale computing capability, ensures the real-time performance and accuracy of data, and has wide application prospects. Therefore, an efficient and safe data storage and management mode is provided, and the method has a wide application prospect.

Description

Technical Field

[0001] The present invention belongs to the field of engineering and science and technology, and more specifically, it is a hydrodynamic intelligent computing platform based on a quantum-AI hybrid architecture. Background Art

[0002] Hydrodynamic calculation is an important issue in the fields of engineering and science, involving complex fluid flow phenomena and physical laws. With the development of quantum computing and artificial intelligence technologies, it has been widely applied in the fields of aerospace, energy, chemical engineering, environment, etc.

[0003] Although traditional computational fluid dynamics (CFD) methods can simulate fluid flow to a certain extent, they face challenges such as high computational costs, difficulty in capturing subscale features such as turbulence, and stability problems of numerical algorithms. In recent years, the combination of quantum computing and AI technologies applied to hydrodynamic calculation has become a new research trend. The hybrid architecture combining quantum computing and AI technologies can utilize the high parallelism and fast processing capabilities of quantum computing, as well as the data analysis and model optimization capabilities of AI technologies, to improve the efficiency and accuracy of hydrodynamic calculation.

[0004] Therefore, those skilled in the art have proposed a hydrodynamic intelligent computing platform based on a quantum-AI hybrid architecture, which combines quantum computing and artificial intelligence technologies to achieve efficient, accurate, and intelligent hydrodynamic calculation, having important scientific significance and application value, in order to solve the problems raised in the background art. Summary of the Invention

[0005] In order to solve the above technical problems, the present invention provides a hydrodynamic intelligent computing platform based on a quantum-AI hybrid architecture to solve the problems existing in the prior art.

[0006] A hydrodynamic intelligent computing platform based on a quantum-AI hybrid architecture includes a platform architecture, and the platform architecture includes:

[0007] A data acquisition module, which is used to acquire data related to hydrodynamics;

[0008] A quantum computing module, which is used to execute the quantum algorithm for hydrodynamic calculation to process qubit information;

[0009] An AI processing module, which is used to process and analyze hydrodynamic data;

[0010] A data transmission module, which is used to transmit data between modules;

[0011] A data storage module, which is used to store calculation data and results.

[0012] Preferably, the data acquisition module includes sensors, data transmission devices, and data storage devices. The sensors are used to collect data related to hydrodynamics; the above data may include key parameters such as flow rate, pressure, and temperature, which are the basis for subsequent analysis and simulation by the quantum computing and AI processing modules. The data transmission devices are responsible for transmitting the data collected by the sensors to the data processing center or other modules that require the above data, ensuring the timeliness and accuracy of the data. The data storage devices are used to store the collected hydrodynamics data and calculation results, which helps to long-term preserve important data and facilitate subsequent analysis and traceability; at the same time, it also provides the platform with the ability to back up and restore data.

[0013] Preferably, the quantum computing module includes a quantum processor and a quantum random access memory. The quantum processor is used to execute quantum algorithms for hydrodynamics calculations, and the quantum random access memory is used to store and process quantum state data. This quantum algorithm is used to simulate and optimize complex phenomena and physical processes in hydrodynamics. The specific operation steps are as follows:

[0014] Step 1: In the quantum computing module, develop a quantum simulation and quantum optimization algorithm library to support quantum simulation and optimization of complex phenomena in hydrodynamics;

[0015] Step 2: Use the quantum simulation algorithm to simulate the vortex phenomenon in hydrodynamics to improve the accuracy and efficiency of the simulation;

[0016] Step 3: Use the quantum optimization algorithm to optimize the hydrodynamics model, find the optimal solution or approximate optimal solution, and improve the prediction performance and calculation efficiency of the model.

[0017] Preferably, the AI processing module includes a machine learning model training unit and a data analysis unit. The machine learning model training unit is used to train and optimize the hydrodynamics model, and the data analysis unit is used to analyze the hydrodynamics data and extract features. That is, the AI processing module introduces a machine learning model to optimize the training and data analysis processes of the hydrodynamics model. The specific operations are as follows:

[0018] Step 1: In the AI processing module, integrate a quantum machine learning algorithm library to support the training and inference of models such as QSVM and QNN;

[0019] Step 2: Use the quantum machine learning algorithm to extract features and classify the hydrodynamics data to improve the accuracy and efficiency of data analysis;

[0020] Step 3: Combine the quantum computing module and the AI processing module to achieve the optimization of the hydrodynamics model under the quantum-AI hybrid architecture.

[0021] Preferably, the data transmission module includes a quantum communication interface and a classical communication interface. The quantum communication interface is used for transmitting quantum state data, and the classical communication interface is used for transmitting classical data.

[0022] Preferably, the data storage module includes a quantum memory and a classical memory. The quantum memory is used for storing quantum state data, and the classical memory is used for storing classical data.

[0023] A hydrodynamic intelligent calculation method based on a quantum-AI hybrid architecture, applicable to the above-mentioned hydrodynamic intelligent calculation platform based on a quantum-AI hybrid architecture, includes the following steps:

[0024] S1. Data acquisition: Obtain the data and configuration parameters corresponding to the grid cells of the system to be solved through the data transmission module;

[0025] S2. Quantum calculation: In the quantum calculation module, use the obtained data corresponding to the grid cells and the current values of the variables to be solved to prepare the initial state required for solving the target linear equations, and use quantum algorithms for solving;

[0026] S3. AI processing: In the AI processing module, use a machine learning model to process and analyze the data output by the quantum calculation module, and extract hydrodynamic characteristics;

[0027] S4. Result output: Output the processed and analyzed results through the data transmission module and store them in the data storage module.

[0028] A processor configured to execute the above-mentioned hydrodynamic intelligent calculation platform based on a quantum-AI hybrid architecture.

[0029] A machine-readable storage medium storing instructions that, when executed by a processor, cause the processor to be configured to execute the above-mentioned hydrodynamic intelligent calculation platform based on a quantum-AI hybrid architecture.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] 1. Efficient hydrodynamic calculation: By combining quantum computing and artificial intelligence technologies, the present invention can efficiently handle complex calculation problems in hydrodynamics; the quantum calculation module uses quantum algorithms to simulate and optimize complex phenomena (such as turbulence, vortices, etc.) in hydrodynamics, improving the accuracy and efficiency of the simulation; at the same time, the AI processing module optimizes the training and data analysis processes of the hydrodynamic model by introducing a machine learning model, further enhancing the calculation efficiency;

[0032] 2. Intelligent Data Processing and Analysis: The AI processing module can not only process and analyze fluid dynamics data, but also extract features and classify data through quantum machine learning algorithms, improving the accuracy and efficiency of data analysis. This intelligent data processing method helps researchers better understand fluid dynamics phenomena and provides strong support for scientific research and engineering design.

[0033] 3. Flexible Data Transmission and Storage: The data transmission module includes a quantum communication interface and a classical communication interface, which can select a suitable communication method according to the transmission requirements of different data types. The data storage module realizes the separate storage of quantum state data and classical data through the combination of a quantum memory and a classical memory, ensuring both data security and improving data access efficiency.

[0034] 4. Multi-scale Computing Capability: The platform architecture of the present invention also includes a multi-scale computing module that supports fluid dynamics calculations at different scales from micro to macro. This function enables researchers to explore fluid dynamics phenomena within a wider scale range and provides a powerful tool for interdisciplinary research and the analysis of complex systems.

[0035] 5. Broad Application Prospects: Due to the unique advantages of the present invention in fluid dynamics calculations, it has broad application prospects in the fields of aerospace, energy, chemical engineering, environment, etc. By improving the efficiency and accuracy of fluid dynamics calculations, the present invention is expected to provide important support for scientific research, engineering design, and optimization in these fields. Brief Description of the Drawings

[0036] Figure 1 is a framework diagram of the fluid dynamics intelligent computing platform based on the quantum-AI hybrid architecture of the present invention;

[0037] Figure 2 is a flow chart of the fluid dynamics intelligent computing method based on the quantum-AI hybrid architecture of the present invention. Detailed Embodiments

[0038] The following further describes in detail the embodiments of the present invention in conjunction with the drawings and embodiments. The following embodiments are used to illustrate the present invention, but cannot be used to limit the scope of the present invention.

[0039] Embodiment: The present invention provides a fluid dynamics intelligent computing platform based on a quantum-AI hybrid architecture, as Figure 1 shown, including a platform architecture, the platform architecture includes a data acquisition module, a quantum computing module, an AI processing module, a data transmission module, and a data storage module, and the data acquisition module, the quantum computing module, the AI processing module, the data transmission module, and the data storage module are electrically connected in sequence:

[0040] A data acquisition module, which is used to acquire data related to hydrodynamics; the data acquisition module includes sensors, data transmission devices, and data storage devices;

[0041] A quantum computing module, which is used to execute quantum algorithms for hydrodynamics calculations to process qubit information;

[0042] An AI processing module, which is used to process and analyze hydrodynamics data;

[0043] A data transmission module, which is used to transmit data between modules; the data transmission module includes a quantum communication interface and a classical communication interface, the quantum communication interface is used for the transmission of quantum state data, and the classical communication interface is used for the transmission of classical data;

[0044] A data storage module, which is used to store calculation data and results, the data storage module includes a quantum memory and a classical memory, the quantum memory is used to store quantum state data, and the classical memory is used to store classical data.

[0045] As can be seen from the above, when the platform is running, first, the data acquisition module acquires data related to hydrodynamics through sensors. The above data may include key parameters such as flow velocity, pressure, and temperature; the acquired data is transmitted in real time to the data processing center or other modules that require the above data through the data transmission device, ensuring the timeliness and accuracy of the data; at the same time, the data storage device is used to store the acquired hydrodynamics data and subsequent calculation results, which helps to long-term preserve important data and facilitates subsequent analysis and traceability;

[0046] Next, after the quantum computing module receives the data transmitted by the data acquisition module, it starts to execute the quantum algorithm for hydrodynamics calculations; the quantum processor uses the quantum algorithm to simulate and optimize complex phenomena in hydrodynamics (such as turbulence, vortices, etc.); the quantum random access memory is used to store and process quantum state data to support the efficient execution of the quantum algorithm; through the quantum simulation and quantum optimization algorithms, the quantum computing module can improve the accuracy and efficiency of the simulation and provide an accurate data basis for subsequent AI processing;

[0047] Then, after the AI processing module receives the data output by the quantum computing module, it starts to process and analyze; the AI processing module includes a machine learning model training unit and a data analysis unit; the machine learning model training unit is used to train and optimize the hydrodynamic model to improve the prediction performance of the model; the data analysis unit is used to analyze the hydrodynamic data and extract features, and by introducing quantum machine learning algorithms (such as QSVM and QNN, etc.), the accuracy and efficiency of data analysis are improved; the AI processing module combines the output data of the quantum computing module to achieve the optimization of the hydrodynamic model under the quantum-AI hybrid architecture.

[0048] In terms of data transmission, the data transmission module is responsible for data transmission between various modules; the quantum communication interface is used for the transmission of quantum state data, which has the characteristics of high speed and security; the classical communication interface is used for the transmission of classical data, ensuring the stability and compatibility of data transmission; through the combination of the quantum communication interface and the classical communication interface, the data transmission module can achieve the efficient and secure transmission of different types of data.

[0049] Finally, the data storage module is used to store the calculation data and results; the quantum memory is used to store quantum state data, which has the characteristics of high density and long life; the classical memory is used to store classical data, which has the advantages of fast read and write speed and large capacity; through the combination of the quantum memory and the classical memory, the data storage module can achieve the separate storage and management of different types of data, improving the access efficiency and security of data.

[0050] In summary, the intelligent hydrodynamic computing platform based on the quantum-AI hybrid architecture provided by the present invention realizes the efficient, accurate and intelligent processing and analysis of hydrodynamic data through the collaborative work of modules such as data acquisition, quantum computing, AI processing, data transmission and data storage. This platform has broad application prospects in fields such as aerospace, energy, chemical engineering, and environment, and is expected to provide important support for scientific research, engineering design and optimization in these fields.

[0051] Furthermore, the quantum computing module includes a quantum processor and a quantum random access memory. The quantum processor is used to execute the quantum algorithm for hydrodynamic calculations, and the quantum random access memory is used to store and process quantum state data. This quantum algorithm is used to simulate and optimize complex phenomena and physical processes in hydrodynamics. The specific operation steps are as follows:

[0052] Step 1: In the quantum computing module, develop a quantum simulation and quantum optimization algorithm library to support the quantum simulation and optimization of complex phenomena in hydrodynamics.

[0053] Step 2: Use quantum simulation algorithms to simulate the vortex phenomenon in hydrodynamics, improving the accuracy and efficiency of the simulation; the quantum simulation algorithms include:

[0054] 1) Quantum state evolution: The core of quantum simulation is the evolution of quantum states. In quantum mechanics, the evolution of quantum states is usually described by the Schrödinger equation:

[0055]

[0056] where ψ is the quantum state, H is the Hamiltonian, i is the imaginary unit, is the Planck constant;

[0057] 2) Quantum gate operations: Quantum gate operations are often used in quantum simulation, such as the Hadamard gate, Pauli-X gate, etc.; these gate operations act on quantum states through matrix multiplication. The formula for quantum gate operations is as follows:

[0058]

[0059] where, H B is the mixing operator, used to introduce entanglement between quantum states; H C is the target operator, related to the target function;

[0060] 3) Quantum circuits: Complex quantum simulations may involve quantum circuits composed of multiple quantum gates. The output state of the circuit is the result of a series of gate operations on the input state;

[0061] Step 3: Use quantum optimization algorithms to optimize the hydrodynamics model, find the optimal solution or approximate optimal solution, and improve the prediction performance and computational efficiency of the model;

[0062] Specifically, first use the wave function to describe the current state of the vortex phenomenon in hydrodynamics, and then randomly simulate the update of the vortex phenomenon process:

[0063]

[0064] where,

[0065] where,

[0066] where, u and θ are random numbers uniformly distributed between 0 and 1. In when u > 0.5, take the positive value. NP is the number of vortex phenomena; P i is the attractor of the vortex phenomenon, L i is the characteristic length of the potential well, which determines the search range of the algorithm; is the individual optimal position of the i-th vortex phenomenon; g bestis the globally optimal position; M best is the average optimal position of all individuals; β is the contraction-expansion coefficient, which is the only adjustable parameter. When β increases, the global search ability of the algorithm is enhanced. When β decreases, it is beneficial for the algorithm to perform local search.

[0067] As can be seen from the above, by using the quantum simulation algorithm to simulate complex phenomena in hydrodynamics (such as vortex phenomena), and utilizing quantum mechanics principles such as quantum state evolution and quantum gate operations, the accuracy and efficiency of the simulation can be significantly improved; compared with traditional simulation methods, quantum simulation can better capture the subtle features and dynamic changes in hydrodynamics. At the same time, by using the quantum optimization algorithm to optimize the hydrodynamics model and find the optimal solution or approximate optimal solution, the prediction performance and computational efficiency of the model can be significantly improved; it helps to solve the problems faced by traditional computational fluid dynamics (CFD) methods, such as high computational costs, difficulty in capturing sub-scale features such as turbulence, and numerical algorithm stability issues.

[0068] Furthermore, the AI processing module includes a machine learning model training unit and a data analysis unit. The machine learning model training unit is used to train and optimize the hydrodynamics model, and the data analysis unit is used to analyze hydrodynamics data and extract features. That is, the AI processing module optimizes the training and data analysis processes of the hydrodynamics model by introducing a machine learning model. The specific operations are as follows:

[0069] Step 1: Integrate a quantum machine learning algorithm library in the AI processing module to support the training and inference of models such as QSVM and QNN; Quantum Support Vector Machine (QSVM): Quantum Support Vector Machine is the quantum version of the traditional Support Vector Machine. It utilizes the high parallelism and fast processing ability of quantum computing to improve the training speed and prediction accuracy of the model; Quantum Neural Network (QNN): Quantum Neural Network combines the advantages of quantum computing and neural networks and can show higher efficiency and accuracy when processing complex data;

[0070] Step 2: Use the quantum machine learning algorithm to extract features and classify hydrodynamics data to improve the accuracy and efficiency of data analysis. The specific operations are as follows:

[0071] 1) First, use the quantum principal component analysis algorithm to extract more representative features from the original data. The operation steps of the quantum principal component analysis algorithm are as follows:

[0072] ① First, take the hydrodynamics data as the training set h = {h1, h2, ···, h n}, where the kth training set h k ∈R, k = 1, 2,..., n, and n is the total number of training sets. h k can be composed of multiple independent variables; define the non-linear mapping R → F, through the function the training set can be mapped into the high-dimensional feature space F. If the mapping mean is 0, that is then the covariance matrix C of the training set in the F space is:[[]]

[0073]

[0074] The corresponding characteristic equation is:[[]]

[0075] λV = CV (2)

[0076] where λ is the vector composed of eigenvalues; V is the eigenvector;

[0077] All eigenvectors V with non-zero eigenvalues can be obtained through the linear representation of the sample vectors in the feature space. Therefore, there exist coefficients such that the following equation holds:[[]]

[0078]

[0079] ② Substitute equations (1) and (3) into equation (2), and then perform the inner product operation on both sides of the equation (j = 1, 2,..., n), variables j and k can be equal or not equal. At the same time, define an n×n kernel function K, and then find the eigenvalues and eigenvectors of matrix K:[[]]

[0080]

[0081] where

[0082] each element K of the kernel function K kj is:[[]]

[0083]

[0084] where K(h k , h j ) is the kernel function;

[0085] ③ Then select the eigenvectors corresponding to the larger eigenvalues to construct the feature subspace. Assume that m eigenvectors β 1 , β 2 ,..., β m 1 are extracted, where where i = 1, 2,..., m; the corresponding principal component v in the F space is normalized through the following formula; the normalization formula is as follows:[[]] i The normalization processing formula is as follows:[[]]

[0086] λ i (β i , β i) = 1 (6)

[0087] where λ i is the corresponding eigenvalue of the principal component v i ;

[0088] ④ The deformed data h k can all be mapped into the F space, which is represented as (r1, r2,..., r m ), where the projection component r i (i = 1, 2,..., m) is:

[0089]

[0090] Since the actual data does not satisfy the condition of zero mean, the kernel matrix K is changed to

[0091]

[0092] where L n×n is an n×n identity matrix with a coefficient of 1 / n;

[0093] 2) Then classify the features extracted in step 1), and the formula is as follows:

[0094] f(X) = sign(w T φ(X) + b);

[0095] where w is the weight vector, φ(X) is the feature function that maps the input data to a high-dimensional space, and b is the bias term;

[0096] Step 3: Combine the quantum computing module and the AI processing module to optimize the hydrodynamic model under the quantum-AI hybrid architecture.

[0097] As can be seen from the above, by integrating quantum machine learning algorithm libraries such as QSVM and QNN, the AI processing module can utilize the high parallelism and fast processing capabilities of quantum computing to perform efficient feature extraction and classification on hydrodynamic data, thereby improving the accuracy and efficiency of data analysis; it helps to quickly extract valuable information from massive data, providing a solid foundation for subsequent model training and prediction; at the same time, the machine learning model training unit trains and optimizes the hydrodynamic model through quantum machine learning algorithms, which can significantly improve the prediction performance and computational efficiency of the model; models such as quantum support vector machines and quantum neural networks show higher efficiency and accuracy when dealing with complex data, helping to solve the problems faced by traditional models in hydrodynamic calculations; furthermore, by combining the quantum computing module and the AI processing module, the platform realizes the optimization of the hydrodynamic model under the quantum-AI hybrid architecture; the quantum computing module is responsible for processing complex quantum state data and executing quantum algorithms, while the AI processing module uses machine learning techniques to deeply analyze the data and optimize the model; this collaborative optimization method can give full play to the advantages of both, improving the intelligent level of hydrodynamic calculations.

[0098] The hydrodynamic intelligent computing platform based on the quantum-AI hybrid architecture in this embodiment is compared with the current existing technology (control group) to obtain the following table:

[0099]

[0100]

[0101] As can be seen from the above table, the hydrodynamic intelligent computing platform based on the quantum-AI hybrid architecture has significant beneficial effects in many aspects compared with the existing technology, with higher computing efficiency, simulation accuracy, model optimization ability, data analysis ability and cross-scale computing ability, while ensuring the real-time and accuracy of data, and then providing an efficient and secure data storage and management method, with broad application prospects.

[0102] As Figure 2 shown, a hydrodynamic intelligent computing method based on the quantum-AI hybrid architecture is applicable to the above-mentioned hydrodynamic intelligent computing platform based on the quantum-AI hybrid architecture, and includes the following steps:

[0103] S1. Data acquisition: Obtain the data and configuration parameters corresponding to the grid cells of the system to be solved through the data transmission module;

[0104] S2. Quantum computing: In the quantum computing module, use the data corresponding to the obtained grid cells and the current value of the variable to be solved to prepare the initial state required for solving the target linear equation system, and use quantum algorithms to solve it;

[0105] S3, AI Processing: In the AI processing module, a machine learning model is used to process and analyze the data output by the quantum computing module to extract hydrodynamic characteristics.

[0106] S4, Result Output: The processed and analyzed results are output through the data transmission module and stored in the data storage module.

[0107] In this embodiment, the hydrodynamic intelligent computing platform based on the quantum-AI hybrid architecture includes a processor and a memory. The processor contains a kernel, and the kernel retrieves the corresponding program units from the memory. One or more kernels can be set, and by adjusting the kernel parameters, the above-mentioned hydrodynamic intelligent computing platform based on the quantum-AI hybrid architecture can be realized.

[0108] The memory may include non-permanent memory in a computer-readable medium, forms such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one storage chip.

[0109] An embodiment of the present application provides a processor configured to execute the above-mentioned hydrodynamic intelligent computing platform based on the quantum-AI hybrid architecture.

[0110] An embodiment of the present application provides a machine-readable storage medium, on which instructions are stored. When the instructions are executed by a processor, the processor is configured to execute the above-mentioned hydrodynamic intelligent computing platform based on the quantum-AI hybrid architecture.

[0111] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0112] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate for implementing in the process Figure 1one or more processes and / or blocks Figure 1 means for the functions specified in one or more blocks.

[0113] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including an instruction means that implements the functions in the process Figure 1 one or more processes and / or blocks Figure 1 specified in one or more blocks.

[0114] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus, such that a series of operational steps are performed on the computer or other programmable apparatus to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions in the process Figure 1 one or more processes and / or blocks Figure 1 specified in one or more blocks.

[0115] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0116] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM) and / or non-volatile memory such as read-only memory (ROM) or flash memory (flash RAM). The memory is an example of computer-readable media.

[0117] Computer-readable media includes both permanent and non-permanent, removable and non-removable media implemented by any method or technology for storing information. The information may be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile discs (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media such as modulated data signals and carrier waves.

[0118] It should also be noted that the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, commodity or device comprising a series of elements not only includes those elements but also includes other elements not expressly listed, or elements inherent to such process, method, commodity or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, commodity or device comprising the element.

[0119] The embodiments of the present invention are given for the purposes of illustration and description. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

[0120] The embodiments of the present invention are given for the purposes of illustration and description. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A hydrodynamic intelligent computing platform based on a quantum-AI hybrid architecture, characterized in that: including a platform architecture, the platform architecture comprising: a data acquisition module for acquiring data related to hydrodynamics; a quantum computing module for executing quantum algorithms for hydrodynamics calculations to process qubit information; an AI processing module for processing and analyzing hydrodynamics data; a data transmission module for transmitting data between modules; a data storage module for storing calculation data and results.

2. The hydrodynamic intelligent computing platform based on the quantum-AI hybrid architecture according to claim 1, wherein: The data acquisition module includes sensors, data transmission devices, and data storage devices.

3. The hydrodynamic intelligent computing platform based on the quantum-AI hybrid architecture according to claim 1, wherein: The quantum computing module includes a quantum processor and a quantum random access memory. The quantum processor is used to execute quantum algorithms for hydrodynamics calculations, and the quantum random access memory is used to store and process quantum state data. The quantum algorithms are used to simulate and optimize complex phenomena and physical processes in hydrodynamics. The specific operation steps are as follows: Step 1: In the quantum computing module, develop a quantum simulation and quantum optimization algorithm library to support quantum simulation and optimization of complex phenomena in hydrodynamics; Step 2: Use the quantum simulation algorithm to simulate complex phenomena such as turbulence and vortices in hydrodynamics to improve the accuracy and efficiency of the simulation; Step 3: Use the quantum optimization algorithm to optimize the hydrodynamics model to find the optimal solution or approximate optimal solution and improve the prediction performance and calculation efficiency of the model.

4. The hydrodynamic intelligent computing platform based on the quantum-AI hybrid architecture according to claim 1, wherein: The AI processing module includes a machine learning model training unit and a data analysis unit. The machine learning model training unit is used to train and optimize the hydrodynamics model, and the data analysis unit is used to analyze hydrodynamics data and extract features. That is, the AI processing module optimizes the training and data analysis processes of the hydrodynamics model by introducing a machine learning model. The specific operations are as follows: Step 1: In the AI processing module, integrate a quantum machine learning algorithm library to support the training and inference of QSVM and QNN models; Step 2: Use the quantum machine learning algorithm to extract features and classify hydrodynamics data to improve the accuracy and efficiency of data analysis; Step 3: Combine the quantum computing module and the AI processing module to achieve optimization of the hydrodynamics model under the quantum-AI hybrid architecture.

5. The hydrodynamic intelligent computing platform based on the quantum-AI hybrid architecture according to claim 1, wherein: The data transmission module includes a quantum communication interface and a classical communication interface. The quantum communication interface is used for transmitting quantum state data, and the classical communication interface is used for transmitting classical data.

6. The hydrodynamic intelligent computing platform based on the quantum-AI hybrid architecture according to claim 1, wherein: The data storage module includes a quantum memory and a classical memory. The quantum memory is used to store quantum state data, and the classical memory is used to store classical data.

7. A hydrodynamic intelligent computing method based on a quantum-AI hybrid architecture, characterized in that: Applicable to the hydrodynamics intelligent computing platform based on the quantum-AI hybrid architecture as described in any one of claims 1-6, including the following steps: S1. Data acquisition: Obtain the data and configuration parameters corresponding to the grid cells of the system to be solved through the data transmission module; S2. Quantum computing: In the quantum computing module, use the data corresponding to the obtained grid cells and the current values of the variables to be solved to prepare the initial state required for solving the target linear equations, and use quantum algorithms for solving; S3. AI Processing: In the AI processing module, a machine learning model is used to process and analyze the data output by the quantum computing module to extract hydrodynamic characteristics; S4. Result Output: The processed and analyzed results are output through the data transmission module and stored in the data storage module.

8. A processor, characterized in that, configured to execute the hydrodynamic intelligent computing platform based on the quantum-AI hybrid architecture according to any one of claims 1 to 6.

9. A machine-readable storage medium having instructions stored thereon, characterized in that, When executed by a processor, the instruction causes the processor to be configured to execute the hydrodynamic intelligent computing platform based on the quantum-AI hybrid architecture according to any one of claims 1 to 6.

Images