Quantum state preparation method, system, and electronic device based on lim tdd

Abstract

The application discloses a quantum state preparation method and system based on LimTDD and electronic equipment, and relates to the technical field of quantum state preparation. The method comprises the following steps: acquiring target LimTDD corresponding to classical data in a preset technical field; based on the preparation circuit of the quantum state corresponding to the 0-successor of each non-terminating node in the target LimTDD and the preparation circuit of the quantum state corresponding to the 1-successor, a first target quantum circuit for converting the target LimTDD into a new target LimTDD is generated, wherein the quantum state of the new target LimTDD is, and all the preparation circuits are quantum circuits; and the first target quantum circuit is processed to obtain the quantum state corresponding to the classical data. The application can more accurately prepare the quantum state corresponding to the classical data, effectively improve the quantum state preparation efficiency, and has strong scalability.

Description

Technical Field

[0001] This invention relates to the field of quantum state preparation technology, and in particular to a quantum state preparation method, system and electronic device based on LimTDD. Background Technology

[0002] Quantum state preparation is a core component of quantum computing and quantum information processing. It provides the circuitry needed to convert classical data into quantum data for processes such as quantum machine learning and solving quantum linear equations, enabling the use of quantum algorithms to complete the corresponding tasks. It provides the necessary initial conditions for the quantum system, ensuring that subsequent quantum operations produce the expected results. In quantum computing, efficient quantum state preparation is fundamental to achieving efficient quantum algorithms. For example, when using the High-Hyper-Limited Algorithm (HHL) to solve a system of linear equations (Ax=b), it is necessary to convert the vector b on the right-hand side of the equations into a quantum state. When using quantum machine learning to process classical data, it is also necessary to prepare large amounts of classical data into corresponding quantum states. Efficient quantum state preparation is the foundation of processes such as quantum machine learning and solving quantum linear equations, and it is also a key step in using quantum computers to simulate classical systems. It plays a crucial role in promoting the development of quantum computing and quantum information science.

[0003] Existing methods for preparing quantum states mainly include optimization methods based on parametric quantum circuits (PQC), methods based on uniform control gates, methods based on unitary matrix factorization, and optimization methods for sparse quantum states. Each method has its unique advantages and limitations. For example, parametric quantum circuits approximate the target quantum state by optimizing circuit parameters, offering high flexibility and scalability, but may require numerous optimization iterations and are sensitive to noise, potentially resulting in imprecise quantum states. Uniform control gate methods efficiently prepare quantum states by gradually adjusting their phase and amplitude, but may require complex circuit structures, especially when dealing with high-dimensional quantum states. Unitary matrix factorization methods accurately realize the target state by decomposing it into a combination of basic quantum gates, but their computational complexity is high, and they may face excessive resource consumption in large-scale quantum systems. Finally, optimization methods for sparse quantum states can effectively reduce circuit complexity and improve preparation efficiency, but their applicability is relatively limited, mainly targeting specific types of quantum states.

[0004] Besides the methods mentioned above, some other methods utilize decision graph representations of quantum states for quantum state preparation. However, the decision graphs used in these methods are often biased towards classical computation, such as BDD or ADD. These decision graphs cannot capture the isomorphic relationships between the parts of a quantum state well, and therefore often have a large scale when representing real quantum states, which seriously affects their effectiveness in real-world scenarios.

[0005] In summary, although there are many methods available for addressing the problem of quantum state preparation, a method that can simultaneously satisfy accuracy, scalability, and efficiency is still lacking. Summary of the Invention

[0006] The technical problem to be solved by this invention is to address the shortcomings of existing technologies, specifically by providing a quantum state preparation method, system, and electronic device based on LimTDD, as detailed below:

[0007] 1) In a first aspect, the present invention provides a method for preparing quantum states based on LimTDD, the specific technical solution of which is as follows:

[0008] Obtain the target LimTDD corresponding to classic data in the preset technical field;

[0009] Based on the preparation circuits for the quantum states corresponding to the 0-successor and 1-successor of each non-termination node in the target LimTDD, a first target quantum circuit is generated to convert the target LimTDD into a new target LimTDD, wherein the quantum state of the new target LimTDD is... All fabricated circuits are quantum circuits;

[0010] The first target quantum circuit is processed to obtain the quantum state corresponding to the classical data.

[0011] The beneficial effects of the quantum state preparation method based on LimTDD provided by this invention are as follows:

[0012] Traditional methods, such as parameterized quantum circuits, suffer from limited fabrication accuracy due to noise sensitivity, and uniform control gates experience a surge in complexity at high-dimensional states, further impacting accuracy. This application addresses this by constructing the first target quantum circuit using the 0-successor and 1-successor quantum state fabrication circuits of LimTDD's non-terminating nodes. This precisely decomposes the target state into basic circuit combinations, leveraging the structural characteristics of LimTDD to accurately describe the hierarchical relationships of quantum states. This avoids the cumulative errors caused by gradient descent in traditional optimization methods, achieving high-fidelity quantum state fabrication with less circuit complexity. Unitary matrix factorization is resource-intensive in large-scale quantum systems. This application utilizes the tree-like hierarchical structure of LimTDD to decompose the overall quantum state fabrication task into local fabrication tasks for multiple sub-nodes. The 0-successor and 1-successor circuits of each non-terminating node can be independently designed and optimized. As the scale of the quantum system increases, this divide-and-conquer strategy effectively controls the growth rate of circuit depth and gate count, providing efficient architectural support for quantum state fabrication in large-scale quantum computing scenarios. To address the limited applicability of sparse state optimization methods, this application utilizes a general decision graph representation based on LimTDD. This approach retains the spatial efficiency advantages of classical decision graphs while enhancing the ability to capture quantum state isomorphisms through quantization. When generating the first target quantum circuit, circuit segments corresponding to isomorphic substructures are automatically merged, reducing redundant quantum operations. This significantly reduces the number of quantum gate operations and circuit depth while maintaining fabrication accuracy, achieving an order-of-magnitude improvement in fabrication efficiency. Unlike traditional optimization methods that rely on specific quantum state structures, this application is applicable to any classical data mapping scenario that can be represented as LimTDD. Through systematic processing of non-terminating nodes in LimTDD, it adaptively generates quantum circuit structures that match the data distribution characteristics without requiring manual intervention to adjust the circuit architecture. This demonstrates broad applicability to pre-quantum state loading tasks for general quantum algorithms such as quantum machine learning and solving quantum linear equations. This application, through its LimTDD-based structured circuit generation method, reduces the number of parameters in the optimization process and lowers the sensitivity of quantum gate operations. Meanwhile, when constructing the first target quantum circuit, fault-tolerant design can be introduced through the decomposition characteristics of LimTDD, such as adding redundant error correction paths at key nodes, so that the fabricated circuit can still maintain high state fidelity under certain noise environment.

[0013] Based on the above scheme, the quantum state preparation method based on LimTDD of the present invention can be further improved as follows.

[0014] Furthermore, the first target quantum circuit is processed to obtain the quantum state corresponding to the classical data, including:

[0015] The inverse operation of the first target quantum circuit yields the second target quantum circuit. On a quantum computer, starting from a preset quantum state, the second target quantum circuit is run to obtain the quantum state corresponding to the classical data. Here, the preset quantum state is... .

[0016] The beneficial effects of adopting the above-mentioned further scheme are as follows: By performing inverse operations on the first target quantum circuit to prepare the quantum state corresponding to classical data, the accuracy of quantum state preparation can be effectively improved. The inverse operation can accurately convert the preset quantum state into the target quantum state, reducing errors caused by circuit complexity or optimization iteration. The inverse operation process is usually more direct than traditional methods using complex optimization or decomposition, reducing computational complexity and avoiding a large number of iterations, thus obtaining the quantum state corresponding to classical data faster on a quantum computer. The method of performing inverse operations on the first target quantum circuit is relatively simple and universal, and can adapt to different types of quantum state preparation needs. Regardless of the change in the dimension of the target quantum state, its basic operational logic remains consistent, facilitating its application in large-scale quantum systems. Compared with some complex quantum state preparation methods, this application does not require excessive quantum gate combinations or complex circuit structures, thus being more economical in terms of resource consumption, which is conducive to its implementation and application in practical quantum computers and effectively reduces dependence on quantum resources.

[0017] Furthermore, the process by which the first target quantum circuit transforms the target LimTDD into a new target LimTDD includes:

[0018] Apply the inverse operation of the local operators on the incoming edges of the target LimTDD to the target LimTDD to eliminate the local operators on the incoming edges of the target LimTDD.

[0019] Eliminate the local operators on the high edges of any non-terminating node of the target LimTDD, and process the 0-successor and 1-successor of the non-terminating node to obtain the quantum state preparation circuits corresponding to the 0-successor and 1-successor. When the quantum state preparation circuits corresponding to the 0-successor and 1-successor are different, add control conditions to the quantum state preparation circuits corresponding to the 0-successor. If the quantum state preparation circuits corresponding to the 0-successor and 1-successor are the same, do not add control conditions, and adjust the weights on the two outgoing edges of the non-terminating node until each non-terminating node is processed to obtain a new target LimTDD.

[0020] The beneficial effects of adopting the above-mentioned further scheme are as follows: By applying the inverse operation of the local operators on the incoming edges to the target LimTDD, the local operators on the incoming edges are eliminated. This process can accurately restore the initial state of the quantum state, providing an accurate foundation for subsequent quantum state preparation and manipulation. This inverse operation design helps improve the accuracy and efficiency of quantum state preparation, reducing unnecessary complex operations and potential errors. This approach can flexibly adapt to the characteristics of different nodes, improving the flexibility and adaptability of quantum state preparation. Simultaneously, when the preparation circuits for 0-successors and 1-successors are different, adding control conditions can ensure the accuracy and consistency of quantum state preparation. When they are the same, the strategy of not adding control conditions and adjusting the weights can simplify the circuit structure, reduce resource consumption, and improve preparation efficiency. By progressively processing each non-terminating node, a new target LimTDD is finally obtained, whose quantum state is... This unified preparation path facilitates the standardization and normalization of quantum state preparation, enabling integration with other quantum operations and algorithms and improving the compatibility and scalability of the entire quantum computing system. When the 0-successor and 1-successor preparation circuits are identical, the strategy of not adding control conditions and adjusting weights effectively reduces the complexity and resource consumption of the quantum circuit. This not only improves the efficiency of quantum state preparation but also reduces the hardware resource requirements of the quantum computer, making quantum state preparation more economical and efficient. When the 0-successor and 1-successor preparation circuits are different, the design of adding control conditions separately allows quantum state preparation to adopt different strategies for different situations, enhancing the flexibility and adaptability of quantum state preparation and better addressing various complex quantum state preparation needs.

[0021] Furthermore, the preset technical fields are: electromagnetic field technology, fluid mechanics technology, physical system state recognition technology, time series prediction technology, or image recognition technology; the classical data included in the electromagnetic field technology field includes linear equations, the classical data included in the fluid mechanics technology field includes linear equations, the classical data included in the physical system state recognition technology field includes sensor data, actual measurement data, and theoretical calculation results, the classical data included in the time series prediction technology field includes time series data, and the classical data included in the image recognition technology field includes images.

[0022] The beneficial effects of adopting the above-mentioned further scheme are: it can adapt to various types of data, and the preparation process is highly efficient, providing strong support for the application of quantum computing in multiple practical technology fields, effectively expanding the application scope of quantum state preparation, and promoting the development of quantum computing technology in solving practical problems.

[0023] 2) In a second aspect, the present invention also provides a quantum state preparation system based on LimTDD, the specific technical solution of which is as follows:

[0024] It includes an acquisition module, a quantum circuit generation module, and a quantum state acquisition module;

[0025] The acquisition module is used to: acquire the target LimTDD corresponding to classic data in a preset technical field;

[0026] The quantum circuit generation module is used to: generate a first target quantum circuit for converting the target LimTDD into a new target LimTDD, based on the preparation circuits of the quantum states corresponding to the 0-successors and the 1-successors of each non-terminating node in the target LimTDD, where the quantum states of the new target LimTDD are... All fabricated circuits are quantum circuits;

[0027] The quantum state acquisition module is used to process the first target quantum circuit to obtain the quantum state corresponding to the classical data.

[0028] Based on the above scheme, the quantum state preparation system based on LimTDD of the present invention can be further improved as follows.

[0029] Furthermore, the quantum state acquisition module is specifically used to: perform an inverse operation on the first target quantum circuit to obtain a second target quantum circuit; and on a quantum computer, starting from a preset quantum state, run the second target quantum circuit to obtain the quantum state corresponding to the classical data, wherein the preset quantum state is... .

[0030] Furthermore, the process by which the first target quantum circuit transforms the target LimTDD into a new target LimTDD includes:

[0031] Apply the inverse operation of the local operators on the incoming edges of the target LimTDD to the target LimTDD to eliminate the local operators on the incoming edges of the target LimTDD.

[0032] Eliminate the local operators on the high edges of any non-terminating node of the target LimTDD, and process the 0-successor and 1-successor of the non-terminating node to obtain the quantum state preparation circuits corresponding to the 0-successor and 1-successor. When the quantum state preparation circuits corresponding to the 0-successor and 1-successor are different, add control conditions to the quantum state preparation circuits corresponding to the 0-successor. If the quantum state preparation circuits corresponding to the 0-successor and 1-successor are the same, do not add control conditions, and adjust the weights on the two outgoing edges of the non-terminating node until each non-terminating node is processed to obtain a new target LimTDD.

[0033] Furthermore, the preset technical fields are: electromagnetic field technology, fluid mechanics technology, physical system state recognition technology, time series prediction technology, or image recognition technology; the classical data included in the electromagnetic field technology field includes linear equations, the classical data included in the fluid mechanics technology field includes linear equations, the classical data included in the physical system state recognition technology field includes sensor data, actual measurement data, and theoretical calculation results, the classical data included in the time series prediction technology field includes time series data, and the classical data included in the image recognition technology field includes images.

[0034] 3) In a third aspect, the present invention also provides an electronic device, the electronic device including a processor coupled to a memory, the memory storing at least one computer program, the at least one computer program being loaded and executed by the processor to enable the electronic device to implement any of the above-mentioned quantum state preparation methods based on LimTDD.

[0035] 4) In a fourth aspect, the present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements any of the above-mentioned quantum state preparation methods based on LimTDD.

[0036] It should be noted that the beneficial effects of the technical solutions of the second to fourth aspects of the present invention and their corresponding possible implementations can be found in the above description of the technical effects of the first aspect and its corresponding possible implementations, and will not be repeated here. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments of the present invention will be briefly introduced below:

[0038] Figure 1 This is a schematic flowchart of a quantum state preparation method based on LimTDD according to an embodiment of the present invention;

[0039] Figure 2 A schematic diagram of LimTDD for the target;

[0040] Figure 3 A schematic diagram of the target quantum circuit;

[0041] Figure 4 This is a schematic diagram of a quantum state preparation system based on LimTDD according to an embodiment of the present invention;

[0042] Figure 5 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0043] The principles and features of the present invention are described below. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0044] The technical solution of the present invention and how the technical solution of the present invention solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of the present invention will now be described with reference to the accompanying drawings.

[0045] like Figure 1 As shown, an embodiment of the present invention provides a quantum state preparation method based on LimTDD, comprising the following steps:

[0046] S1. Obtain the target LimTDD corresponding to the classic data in the preset technical field;

[0047] The preset technical fields are: electromagnetic field technology, fluid mechanics technology, physical system state recognition technology, time series prediction technology, or image recognition technology; the classical data in the electromagnetic field technology field includes linear equations, the classical data in the fluid mechanics technology field includes linear equations, the classical data in the physical system state recognition technology field includes sensor data, actual measurement data, and theoretical calculation results, the classical data in the time series prediction technology field includes time series data, and the classical data in the image recognition technology field includes images. Specifically:

[0048] 1) Classical data in the field of electromagnetic field technology mainly includes various linear equations related to electromagnetic fields. These equations are usually derived from the discretization of Maxwell's equations. In the field of electromagnetic field technology, after preparing classical data into quantum states, the powerful capabilities of quantum computing can be used to accelerate and optimize the simulation and analysis of electromagnetic fields. Specifically, by preparing linear equations of electromagnetic fields into quantum states, quantum algorithms (such as quantum linear equation algorithms) can significantly shorten the time for solving large-scale linear equations, thereby accelerating the electromagnetic field simulation process. This is of great significance for the design and optimization of complex electromagnetic environments (such as large antenna arrays, electromagnetic compatibility analysis of integrated circuits, etc.). The preparation and processing of quantum states can achieve higher precision, making the simulation results of electromagnetic fields more accurate, which helps to identify potential problems in advance during the product development stage and reduce the number and cost of experimental verification. In antenna design, quantum computing can quickly optimize the shape, size, and material parameters of antennas to achieve the expected radiation modes and performance indicators, improving the efficiency and effectiveness of antenna design. In the electromagnetic compatibility analysis of electronic devices, quantum computing can more accurately predict electromagnetic interference between devices, helping engineers to take measures in advance to reduce interference and improve the reliability and stability of equipment.

[0049] 2) In the field of fluid mechanics, classical data mainly consists of numerical data of various fluid state parameters and linear equation sets obtained by discretizing fluid dynamics equations. For example, in aerodynamics research, data include the velocity and pressure distribution of air around an aircraft wing; in water flow research, data include the velocity, pressure, and temperature fields of water in a river channel; and large-scale linear equation sets used for numerical simulation are obtained by discretizing fundamental fluid dynamics equations such as the Navier-Stokes equations. After preparing the quantum states corresponding to these classical data, the powerful computing capabilities of quantum computing can be used for efficient fluid simulation. Compared to classical computers, quantum computers can process large-scale fluid dynamics problems in a shorter time, providing faster and more accurate solutions for aircraft design optimization in the aerospace field, vehicle shape optimization to reduce wind resistance in the automotive industry, and precise simulation of atmospheric flow in weather forecasting. This accelerates the development of related products, improves design efficiency and accuracy, and enhances the precision and timeliness of weather forecasts.

[0050] 3) In the field of physical system state identification technology, sensor data includes real-time monitoring information during the operation of physical systems, such as vibration signals and displacement signals collected by sensors in mechanical systems; actual measurement data covers direct measurement results of physical quantities, such as magnetic field strength measurements in electromagnetic systems; and theoretical calculation results are system state prediction data derived from physical models and equations. After converting sensor data, actual measurement data, and theoretical calculation results into quantum states, quantum machine learning algorithms can be used to quickly and accurately identify and classify the state of physical systems, promptly detect system faults or abnormal states, improve system reliability and security, and have wide applications in industries with intensive physical systems, such as energy and transportation.

[0051] 4) In the field of time series forecasting technology, time series data can include stock price trends in the financial market, and changes in meteorological elements such as temperature and air pressure over time in the meteorological field. After preparing time series data into quantum states, the advantages of quantum algorithms, such as quantum walks, can be leveraged to achieve more efficient and accurate time series forecasting, providing a more reliable basis for financial investment decisions and meteorological disaster early warning.

[0052] 5) In the field of image recognition technology, images can be natural landscape photos, biomedical images (such as X-ray films and CT images), and images of parts in industrial production. After converting the images into quantum states, quantum parallel computing and quantum machine learning algorithms, such as quantum convolutional neural networks, can be used to achieve rapid recognition, classification, and target detection of images, which can greatly improve the efficiency and accuracy of image recognition and be applied to multiple fields such as security monitoring, medical imaging diagnosis, and industrial quality inspection.

[0053] S2. Based on the preparation circuits for the quantum states corresponding to the 0-successor and the 1-successor of each non-termination node in the target LimTDD, generate a first target quantum circuit for converting the target LimTDD into a new target LimTDD, wherein the quantum state of the new target LimTDD is... All fabricated circuits are quantum circuits;

[0054] S3. Process the first target quantum circuit to obtain the quantum state corresponding to the classical data.

[0055] Optionally, in S3, the first target quantum circuit is processed to obtain the quantum state corresponding to the classical data, including:

[0056] The inverse operation of the first target quantum circuit yields the second target quantum circuit. On a quantum computer, starting from a preset quantum state, the second target quantum circuit is run to obtain the quantum state corresponding to the classical data. Here, the preset quantum state is... .

[0057] Optionally, in the above technical solution, the process by which the first target quantum circuit converts the target LimTDD into a new target LimTDD includes:

[0058] Apply the inverse operation of the local operators on the incoming edges of the target LimTDD to the target LimTDD to eliminate the local operators on the incoming edges of the target LimTDD.

[0059] Eliminate the local operators on the high edges of any non-terminating node of the target LimTDD, and process the 0-successor and 1-successor of the non-terminating node to obtain the quantum state preparation circuits corresponding to the 0-successor and 1-successor. When the quantum state preparation circuits corresponding to the 0-successor and 1-successor are different, add control conditions to the quantum state preparation circuits corresponding to the 0-successor. If the quantum state preparation circuits corresponding to the 0-successor and 1-successor are the same, do not add control conditions, and adjust the weights on the two outgoing edges of the non-terminating node until each non-terminating node is processed to obtain a new target LimTDD.

[0060] Using classic data from the field of electromagnetic field technology as an example, this invention will be described, specifically including the following steps:

[0061] S101. Obtain the target LimTDD corresponding to the linear equation system for calculating the spatiotemporal distribution of the potential function in the field of electromagnetic field technology;

[0062] Among them, the linear equations in the field of electromagnetic field technology concerning the calculation of the spatiotemporal distribution of the electric potential function are:

[0063]

[0064] Among them, among them, Representation: The 8×8 matrix corresponding to the Laplace operator, where the matrix elements are the second derivatives of the potential spatial distribution with respect to time. Represents: the spatiotemporal distribution data of the electric potential to be solved. It is the classical data obtained by encoding the generalized source term (which consists of a static source term and an induced source term), which is the classical data to be prepared into a quantum state.

[0065] It should be noted that the objective LimTDD corresponding to the above linear equation system refers to: for the preset linear equation system... After processing, the resulting LimTDD, that is, the target LimTDD corresponding to the linear equation system in the preset technical field, is: the LimTDD corresponding to the quantum states in the linear equation system. The obtained target LimTDD is as follows: Figure 2 As shown.

[0066] Figure 2 In this context, the numbers in the quantum states of the linear equation system and the objective LimTDD are retained to two decimal places, where j represents the imaginary unit. It should be noted that LimTDD (Locally Invertible Mapping Tensor Decision Graph) is a directed acyclic graph designed to represent tensors. Since quantum states and quantum gates can be considered as a special type of tensor, it can also be used to represent quantum states and quantum gates. This application only considers the case of representing quantum states. LimTDD consists of a series of edges (outgoing and incoming edges) and nodes (…). Figure 2 The natural number 1 in the table is the terminating node. Figure 2 In , , , and The system consists of non-terminating nodes (also called internal nodes). Each edge has a weight, which is composed of a complex number and a local operator. Each non-terminating node and edge can represent an unnormalized quantum state. Each non-terminating node has two outgoing edges, called low edges (represented by dashed lines) and high edges (represented by solid lines). The nodes connected by low edges are 0-successors, and the nodes connected by high edges are 1-successors. The quantum state represented by each edge is defined by its weight and the nodes it connects to. The quantum state represented by each non-terminating node is... The tensor product of the quantum state represented by its low edge plus The tensor product of the quantum state represented by its high edge.

[0067] Figure 2 middle, The quantum state represented by the node is , It should be noted that, It is an unnormalized quantum state, a quantum state. Dirac notation can be used to represent this. The quantum state represented by the node is , , The quantum state represented by the node is , , The quantum state represented by the node is , , The quantum state represented by the node is , The quantum state represented by the entire target LimTDD is: ,in, , and All are basic quantum gates. , The qubits corresponding to the nodes and The corresponding qubits are all , The qubits corresponding to the nodes and The qubits corresponding to the nodes are all , The node corresponding for It should be noted that the weight of the low edge in the outgoing edges of each non-terminating node includes... Such a unit operator does not change the quantum state on the corresponding branch, and therefore can be omitted.

[0068] S102. Based on the preparation circuits for the quantum states corresponding to the 0-successor and the 1-successor of each non-termination node in the target LimTDD, a first target quantum circuit is generated to convert the target LimTDD into a new target LimTDD, wherein the quantum state of the new target LimTDD is... ;

[0069] The advantage of using LimTDD to prepare quantum states is that LimTDD can effectively extract various local operators, i.e., isomorphism relations, from the quantum state structure. Therefore, by applying the inverse operations of these local operators, the quantum state corresponding to classical data can be reduced step by step to a higher state. Then, the inverse of the quantum circuit composed of these local operators is the first target quantum circuit required to prepare the quantum state corresponding to classical data. Therefore, once a target LimTDD is obtained, it needs to be directed towards the representation The new target is LimTDD.

[0070] S1020. Apply the inverse operation of the local operators on the incoming edges of the target LimTDD to the target LimTDD to eliminate the local operators on the incoming edges of the target LimTDD.

[0071] For example, Figure 2 In the context, the quantum state of the incoming edge of the target LimTDD is: The local operators for target LimTDD are: ,exist Apply a Corresponding inverse operator That will enable Local operator elimination in the target LimTDD transforms the quantum state on the incoming edge into .

[0072] S1021. Eliminate local operators on the high edges of any non-terminating node of the target LimTDD, and process the 0-successor and 1-successor of the non-terminating node to obtain the quantum state preparation circuits corresponding to the 0-successor and the 1-successor. When the quantum state preparation circuits corresponding to the 0-successor and the 1-successor are different, add control conditions to the quantum state preparation circuit corresponding to the 0-successor. Add control conditions to the preparation circuit of the quantum state corresponding to 0-successor. If the preparation circuit for the quantum state corresponding to 0-successor is the same as that for the quantum state corresponding to 1-successor, then no control conditions are added, and the weights on the two outgoing edges of the non-terminating node are adjusted until a new target LimTDD is obtained after processing each non-terminating node. The specific implementation process is as follows:

[0073] 1) Eliminate Local operators on the high edge of a node:

[0074] because The quantum state represented is ,by Pick To control the conditions, in The above effect one It can eliminate In Operator, and let the whole It becomes: Then, process recursively. The 0-successor and 1-successor of a node, i.e. and , specifically:

[0075] ① Processing The process of finding the 0th successor of a node is as follows:

[0076] Similarly, we first process the local operators of the high edges from 0 to their successors. Operator, with Pick To control the conditions, in Apply an inverse operator to the above. To eliminate Operator.

[0077] ② Processing The two 0-successors and 1-successors, namely Nodes and Nodes, specifically:

[0078] After eliminating the local operator of 0-successor high edges, And here Nodes and The successor of a node is the terminating node. Nodes and Since there are no operators on the high edges of the node, we can directly adjust... Nodes and The complex number on the outgoing edges of a node, for a quantum state: Just in A quantum gate acts on it. , then Become ,in In this application, = Therefore, let , and with Pick To control the conditions, the U-gate is applied to Up, and you can Become Similarly, let , and with Pick To control the conditions, the V-gate is applied to Up, and you can Become .at last, .make and will Effect on , then Become .

[0079] ③ Processing The process of finding the successor of a node is as follows:

[0080] First process The local operator X on the 1-successor high edge of the node is then processed. The node's first successor is its two successors, and then the process is repeated. The complex number of the 1-successor outgoing edge of the node, this process is the same as the above process. The process for 0-successor nodes is the same, the only difference is: Note that Both of their successors are Nodes, therefore only the V-gate needs to be applied to Simply apply the command; no control conditions are required. Then, Become The U-gate can then be applied to Up, and you can Become .

[0081] ④ After processing After the two successors of the node, with Pick To control the conditions, the processing will be... The fabrication circuit obtained from the 0-successor of the node is applied to and Above, with Pick To control the conditions, the processing will be... The fabrication circuit obtained from node 1-successor is applied to and If the two successors of a non-terminating node are the same, then the preparation circuits obtained by processing the 0-successor and the 1-successor are the same. It is only necessary to apply this preparation circuit to the qubits corresponding to the two successors, without applying any control conditions.

[0082] ⑤ Processing Complex numbers on the two outgoing edges of a node:

[0083] at this time, It has become Therefore, let and will Effect on Up, and you can become . The complex number that is exactly on the input edge of the target LimTDD The cancellation yields a new target LimTDD, whose quantum state becomes The resulting quantum circuit, obtained after completing this quantum state transformation process, is used to convert the quantum state of the target LimTDD to... The quantum circuit, namely the first target quantum circuit, is as follows: Figure 3 As shown.

[0084] S103. Perform the inverse operation on the first target quantum circuit to obtain the second target quantum circuit. On a quantum computer, starting from a preset quantum state, run the second target quantum circuit to obtain the quantum state corresponding to the classical data, where the preset quantum state is... .

[0085] S104, When obtained After determining the corresponding quantum state, a quantum computer is used, employing the HHL algorithm or other algorithms to solve the linear equations. After obtaining the spatiotemporal distribution of the electric potential by solving the linear equations, the electromagnetic field intensity distribution data can be calculated. Furthermore, the energy flow of electromagnetic waves can be calculated, and electromagnetic radiation modes can be analyzed based on the energy flow. Additionally, the parameters of radio frequency components in electronic devices can be optimized based on the energy flow of electromagnetic waves. Specifically:

[0086] 1) After obtaining the spatiotemporal distribution of electric potential, the electromagnetic field intensity distribution data can be accurately calculated based on Maxwell's equations. First, the spatiotemporal rate of change of electric potential is differentiated. According to Faraday's law of electromagnetic induction, a changing electric potential will induce an electric field in space. By calculating the rate of change of electric potential with time, the induced electromotive force can be obtained, thus determining one component of the electric field intensity. Simultaneously, combined with Coulomb's law, the gradient of electric potential is negatively correlated with the electric field intensity; that is, the direction of the electric field intensity always points in the direction of the fastest decrease in electric potential, and its magnitude is equal to the magnitude of the gradient of electric potential with respect to spatial coordinates. Then, according to Ampere's circuital law, a changing electric field will generate a magnetic field. The magnetic field intensity can be obtained using the time-varying rate of change of electric field intensity. Combining the above processes and integrating the results of each component, a complete electromagnetic field intensity distribution map is constructed, providing a crucial foundation for subsequent electromagnetic wave energy flow calculations and other analyses.

[0087] 2) Once electromagnetic field intensity distribution data is available, the energy flux of electromagnetic waves can be calculated using Poynting's theorem. The Poynting vector is defined as the cross product of the electric field intensity vector and the magnetic field intensity vector. Its direction indicates the propagation direction of electromagnetic wave energy, and its magnitude represents the energy passing through a unit vertical area per unit time, i.e., the energy flux density. In practice, the spatial distribution of electromagnetic field intensity is first calculated point-by-point to obtain the E and H vector values ​​at each location. Then, the vector cross product operation is performed to obtain the Poynting vector at the corresponding point. By summing the Poynting vectors of all points, an energy flux distribution model of the entire space is constructed, clearly presenting key information such as the flow path and intensity regions of electromagnetic wave energy. This lays a data foundation for in-depth analysis of electromagnetic radiation patterns and optimization of related electronic equipment parameters.

[0088] 3) Based on the energy flux distribution of electromagnetic waves, the analysis of electromagnetic radiation modes mainly starts from the following aspects. First, observe the directional characteristics of the energy flux, statistically analyze the energy flux density in each direction, draw an energy flux rose diagram, clarify the main and secondary radiation directions of electromagnetic wave energy, and determine whether the radiation has obvious directionality or exhibits an omnidirectional distribution. Second, analyze the spatial uniformity of the energy flux distribution, calculate the variance and other statistical quantities of energy flux density in different regions. If the variance is large, it indicates that the energy flux distribution is uneven, and there may be regions with significant differences in radiation intensity; conversely, the energy flux distribution is relatively uniform. Third, examine the variation law of energy flux with distance. Usually, as the distance from the source point increases, the energy flux density will change according to a certain attenuation law, such as spherical attenuation, cylindrical attenuation, etc. By fitting the relationship curve of energy flux density versus distance, the specific attenuation mode can be determined. At the same time, phase information can also be combined to analyze the interference and diffraction effects of energy flux in different regions, further improving the fine characterization of electromagnetic radiation modes and providing detailed evidence for targeted electromagnetic radiation optimization.

[0089] Electromagnetic radiation patterns have wide applications in various fields. In communications, such as the antenna design of mobile phone base stations, the installation position, height, and direction of the antenna can be rationally determined based on its electromagnetic radiation pattern, ensuring that electromagnetic wave energy effectively covers the target area, reducing signal blind spots, and improving communication quality and coverage. In radar systems, by analyzing the electromagnetic radiation pattern of radar antennas and optimizing their beam shape and direction, the radar's target detection accuracy and effective detection range can be improved, enhancing its anti-interference capabilities and enabling better tracking and identification of various targets. In radio frequency identification (RFID) systems, understanding the electromagnetic radiation pattern of the reader antenna helps optimize the communication link between the tag and the reader, ensuring reliable data transmission and identification within the effective range, improving the performance and stability of the entire RFID system, and is widely used in logistics management, warehouse inventory monitoring, and other scenarios.

[0090] 4) Taking an RF antenna as an example, firstly, an antenna model is constructed using electromagnetic simulation software. Initial design parameters are input, and the electromagnetic wave energy flow distribution is simulated. The energy flow pattern is analyzed. If the main lobe direction deviates from the expected direction, the antenna geometry can be adjusted, such as changing the element length or bending angle, to concentrate the energy flow towards the target direction. For cases where the energy flow attenuates too quickly in a specific frequency band, the antenna matching network is optimized, such as by modifying the inductor and capacitor values ​​in the impedance matching circuit to improve the antenna's input impedance matching and enhance the radiation efficiency of the energy flow in that frequency band. Taking an RF power amplifier as another example, under the premise of ensuring stable amplifier operation, the bias circuit parameters are adjusted based on the energy flow calculation results to optimize the operating point, making the amplified electromagnetic wave energy flow output more stable and meeting the expected power requirements. Simultaneously, the internal transmission line structure of the amplifier is optimized to reduce energy loss, improve energy flow transmission efficiency, and ensure that more energy can be effectively radiated, thereby optimizing the performance of the RF components.

[0091] In the above embodiments, although the steps are numbered S1, S2, etc., they are only specific embodiments given by the present invention. Those skilled in the art can adjust the execution order of S1, S2, etc. according to the actual situation, which is also within the protection scope of the present invention. It can be understood that in some embodiments, some or all of the above embodiments may be included.

[0092] like Figure 4 As shown, a quantum state preparation system 200 based on LimTDD according to an embodiment of the present invention includes: an acquisition module 201, a quantum circuit generation module 202 and a quantum state acquisition module 203;

[0093] The acquisition module 201 is used to: acquire the target LimTDD corresponding to classic data in a preset technical field;

[0094] The quantum circuit generation module 202 is used to: generate a first target quantum circuit for converting the target LimTDD into a new target LimTDD, based on the preparation circuits of the quantum states corresponding to the 0-successors and the quantum states corresponding to the 1-successors of each non-termination node in the target LimTDD, wherein the quantum states of the new target LimTDD are... All fabricated circuits are quantum circuits;

[0095] The quantum state acquisition module 203 is used to process the first target quantum circuit to obtain the quantum state corresponding to the classical data.

[0096] Optionally, in the above technical solution, the quantum state acquisition module 203 is specifically used for:

[0097] The inverse operation of the first target quantum circuit yields the second target quantum circuit. On a quantum computer, starting from a preset quantum state, the second target quantum circuit is run to obtain the quantum state corresponding to the classical data. Here, the preset quantum state is... .

[0098] Optionally, in the above technical solution, the process by which the first target quantum circuit converts the target LimTDD into a new target LimTDD includes:

[0099] Apply the inverse operation of the local operators on the incoming edges of the target LimTDD to the target LimTDD to eliminate the local operators on the incoming edges of the target LimTDD.

[0100] Eliminate the local operators on the high edges of any non-terminating node of the target LimTDD, and process the 0-successor and 1-successor of the non-terminating node to obtain the quantum state preparation circuits corresponding to the 0-successor and 1-successor. When the quantum state preparation circuits corresponding to the 0-successor and 1-successor are different, add control conditions to the quantum state preparation circuits corresponding to the 0-successor. If the quantum state preparation circuits corresponding to the 0-successor and 1-successor are the same, do not add control conditions, and adjust the weights on the two outgoing edges of the non-terminating node until each non-terminating node is processed to obtain a new target LimTDD.

[0101] Optionally, in the above technical solution, the preset technical field is: electromagnetic field technology, fluid mechanics field technology, physical system state recognition technology, time series prediction technology, or image recognition technology; the classical data included in the electromagnetic field technology field includes linear equations, the classical data included in the fluid mechanics field technology field includes linear equations, the classical data included in the physical system state recognition technology field includes sensor data, actual measurement data, and theoretical calculation results, the classical data included in the time series prediction technology field includes time series data, and the classical data included in the image recognition technology field includes images.

[0102] It should be noted that the beneficial effects of the LimTDD-based quantum state preparation system 200 provided in the above embodiments are the same as those of the LimTDD-based quantum state preparation method described above, and will not be repeated here. Furthermore, the system provided in the above embodiments is only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the system can be divided into different functional modules according to the actual situation to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiments belong to the same concept, and their specific implementation process is detailed in the method embodiments, and will not be repeated here.

[0103] The LimTDD-based quantum state preparation system of the present invention can be a computer program (including program code) running on a computer device. For example, the LimTDD-based quantum state preparation system of the present invention is an application software that can be used to execute the corresponding steps in the LimTDD-based quantum state preparation method of the present invention.

[0104] In some embodiments, the LimTDD-based quantum state preparation system of the present invention can be implemented in a combination of hardware and software. As an example, the LimTDD-based quantum state preparation system of the present invention can be a processor in the form of a hardware decoding processor, which is programmed to execute the LimTDD-based quantum state preparation method of the present invention. For example, the processor in the form of a hardware decoding processor can be one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), or other electronic components.

[0105] The modules described in the embodiments of this invention can be implemented in software or hardware. The names of the modules are not, in some cases, limiting the scope of the module itself.

[0106] An electronic device according to an embodiment of the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements any of the above-mentioned quantum state preparation methods based on LimTDD. That is, an electronic device according to an embodiment of the present invention may include, but is not limited to: a processor and a memory; the memory is used to store the computer program; the processor is used to execute the quantum state preparation method based on LimTDD shown in any embodiment of the present invention by calling the computer program.

[0107] In one alternative embodiment, an electronic device is provided, such as Figure 5 As shown, Figure 5 The illustrated electronic device 4000 includes a processor 4001 and a memory 4003. The processor 4001 and the memory 4003 are connected, for example, via a bus 4002. Optionally, the electronic device 4000 may further include a transceiver 4004, which can be used for data interaction between the electronic device and other electronic devices, such as sending and / or receiving data. It should be noted that in practical applications, the transceiver 4004 is not limited to one type, and the structure of the electronic device 4000 does not constitute a limitation on the embodiments of the present invention.

[0108] Processor 4001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this invention. Processor 4001 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0109] Bus 4002 may include a path for transmitting information between the aforementioned components. Bus 4002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 4002 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 5 The bus 4002 is represented by only one thick line, but this does not mean that there is only one bus or one type of bus.

[0110] The memory 4003 may be ROM (Read Only Memory) or other types of static storage devices capable of storing static information and instructions, RAM (Random Access Memory) or other types of dynamic storage devices capable of storing information and instructions, or EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0111] The memory 4003 stores application code (computer program) for executing the present invention, and its execution is controlled by the processor 4001. The processor 4001 executes the application code stored in the memory 4003 to implement the content shown in the foregoing method embodiments.

[0112] Among them, electronic devices can also be terminal devices, which can be any device that can install applications, including at least one of smartphones, tablets, laptops, desktop computers, smart speakers, smartwatches, smart TVs, and smart in-vehicle devices.

[0113] It should be noted that, Figure 5 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0114] An embodiment of the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements any of the above-mentioned quantum state preparation methods based on LimTDD.

[0115] Alternatively, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), magnetic tape, a floppy disk, and an optical data storage device, etc.

[0116] In an exemplary embodiment, a computer program product or computer program is also provided, which includes computer instructions stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the electronic device to perform any of the above-described LimTDD-based quantum state preparation methods.

[0117] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0118] It should be understood that the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of methods and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0119] The computer-readable storage medium provided in this invention can be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EEPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0120] The aforementioned computer-readable storage medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform the method shown in the above embodiments.

[0121] The above description is merely a preferred embodiment of the present invention and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this invention is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-disclosed concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this invention.

[0122] It should be noted that the terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and represent a limitation on a specific order or sequence. Where appropriate, the order of use for similar objects can be interchanged so that the embodiments of this application described herein can be implemented in an order other than that shown or described.

[0123] Those skilled in the art will recognize that this invention can be implemented as a system, method, or computer program product. Therefore, this invention can be specifically implemented in the following forms: it can be entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software, generally referred to herein as a "circuit," "module," or "system." Furthermore, in some embodiments, this invention can also be implemented as a computer program product contained in one or more computer-readable media, which includes computer-readable program code.

[0124] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for preparing quantum states based on LimTDD, characterized in that, include: Obtain the target LimTDD corresponding to classic data in the preset technical field; Based on the preparation circuits for the quantum states corresponding to the 0-successors and the 1-successors of each non-terminating node in the target LimTDD, a first target quantum circuit is generated to convert the target LimTDD into a new target LimTDD, wherein the quantum states of the new target LimTDD are... All fabricated circuits are quantum circuits; The first target quantum circuit is processed to obtain the quantum state corresponding to the classical data; Processing the first target quantum circuit to obtain the quantum state corresponding to the classical data includes: The first target quantum circuit is inversely operated to obtain a second target quantum circuit. On a quantum computer, starting from a preset quantum state, the second target quantum circuit is run to obtain the quantum state corresponding to the classical data, wherein the preset quantum state is... ; The process by which the first target quantum circuit converts the target LimTDD into a new target LimTDD includes: Apply the inverse operation of the local operators on the incoming edges of the target LimTDD to the target LimTDD to eliminate the local operators on the incoming edges of the target LimTDD. Eliminate the local operators on the high edges of any non-terminating node of the target LimTDD, and process the 0-successor and 1-successor of the non-terminating node to obtain the quantum state preparation circuits corresponding to the 0-successor and 1-successor. When the quantum state preparation circuits corresponding to the 0-successor and 1-successor are different, add control conditions to the quantum state preparation circuits corresponding to the 0-successor. If the quantum state preparation circuits corresponding to the 0-successor and 1-successor are the same, do not add control conditions, and adjust the weights on the two outgoing edges of the non-terminating node until the new target LimTDD is obtained after processing each non-terminating node.

2. The method for preparing quantum states based on LimTDD according to claim 1, characterized in that, The preset technical fields are: electromagnetic field technology, fluid mechanics technology, physical system state recognition technology, time series prediction technology, or image recognition technology; the classical data included in the electromagnetic field technology field includes linear equations, the classical data included in the fluid mechanics technology field includes linear equations, the classical data included in the physical system state recognition technology field includes sensor data, actual measurement data, and theoretical calculation results, the classical data included in the time series prediction technology field includes time series data, and the classical data included in the image recognition technology field includes images.

3. A quantum state preparation system based on LimTDD, characterized in that, It includes an acquisition module, a quantum circuit generation module, and a quantum state acquisition module; The acquisition module is used to: acquire the target LimTDD corresponding to classic data in a preset technical field; The quantum circuit generation module is used to: generate a first target quantum circuit for converting the target LimTDD into a new target LimTDD, based on the preparation circuits of the quantum states corresponding to the 0-successors and the quantum states corresponding to the 1-successors of each non-terminating node in the target LimTDD, wherein the quantum states of the new target LimTDD are... All fabricated circuits are quantum circuits; The quantum state acquisition module is used to: process the first target quantum circuit to obtain the quantum state corresponding to the classical data; The quantum state acquisition module is specifically used for: performing an inverse operation on the first target quantum circuit to obtain a second target quantum circuit; running the second target quantum circuit on a quantum computer, starting from a preset quantum state, to obtain the quantum state corresponding to the classical data, wherein the preset quantum state is... ; The process by which the first target quantum circuit converts the target LimTDD into a new target LimTDD includes: Apply the inverse operation of the local operators on the incoming edges of the target LimTDD to the target LimTDD to eliminate the local operators on the incoming edges of the target LimTDD. Eliminate the local operators on the high edges of any non-terminating node of the target LimTDD, and process the 0-successor and 1-successor of the non-terminating node to obtain the quantum state preparation circuits corresponding to the 0-successor and 1-successor. When the quantum state preparation circuits corresponding to the 0-successor and 1-successor are different, add control conditions to the quantum state preparation circuits corresponding to the 0-successor. If the quantum state preparation circuits corresponding to the 0-successor and 1-successor are the same, do not add control conditions, and adjust the weights on the two outgoing edges of the non-terminating node until the new target LimTDD is obtained after processing each non-terminating node.

4. The quantum state preparation system based on LimTDD according to claim 3, characterized in that, The preset technical fields are: electromagnetic field technology, fluid mechanics technology, physical system state recognition technology, time series prediction technology, or image recognition technology; the classical data included in the electromagnetic field technology field includes linear equations, the classical data included in the fluid mechanics technology field includes linear equations, the classical data included in the physical system state recognition technology field includes sensor data, actual measurement data, and theoretical calculation results, the classical data included in the time series prediction technology field includes time series data, and the classical data included in the image recognition technology field includes images.

5. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a LimTDD-based quantum state preparation method as described in claim 1 or 2.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, implements a LimTDD-based quantum state preparation method as described in claim 1 or 2.

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