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: obtaining target LimTDD corresponding to classical data in a preset technical field; generating 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; processing the first target quantum circuit 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 preparation efficiency of the quantum state, 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] Generate a first target quantum circuit for converting a target LimTDD into a new target LimTDD, wherein the quantum state of the new target LimTDD is: ;

[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] In existing technologies, parameterized quantum circuits are susceptible to noise and the preparation of quantum states is not precise enough. This application, however, transforms the target LimTDD corresponding to classical data in a predefined technical field into a quantum circuit, enabling more precise quantum state preparation, and the prepared quantum state more accurately reflects the classical data. In existing technologies, uniform control gate methods have complex circuit structures when processing high-dimensional quantum states, and unitary matrix factorization methods consume significant resources in large-scale quantum systems. The first target quantum circuit generated in this application is relatively simple and flexible, better adapting to the needs of quantum state preparation at different dimensions and scales. It maintains good performance and exhibits excellent scalability as the scale of the quantum system increases. This application is not limited to specific types of quantum states and can be widely applied to quantum state preparation tasks corresponding to classical data in various predefined technical fields. Furthermore, by processing the predefined quantum state on a quantum computer, the quantum state corresponding to the classical data is directly obtained, avoiding complex processes such as extensive optimization iterations and greatly improving the efficiency of quantum state preparation. Compared to existing methods that use decision graphs that favor classical computation, this application can better capture the isomorphic relationships between different parts of a quantum state, thereby effectively reducing the scale required to represent the quantum state and further improving its effectiveness and performance in real-world scenarios.

[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. 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] Inverse operations are performed on the local operators on the outgoing edges of each non-terminating node in the target LimTDD and on the local operators on the incoming edges of the target LimTDD, so that all local operators in the target LimTDD are eliminated. Then, based on the complex number of the outgoing edges of each non-terminating node in the target LimTDD, a corresponding quantum gate is applied to each non-terminating node to adjust the complex number of the outgoing edges of each non-terminating node to... Thus, a new target, LimTDD, was obtained.

[0019] The beneficial effects of adopting the above-mentioned further scheme are as follows: By precisely adjusting the quantum gates, the complex number of the weight of the outgoing edge of each non-terminating node is adjusted to a specific value, which can more accurately obtain the target quantum state, reduce errors caused by local operators, and ensure that the prepared quantum state is highly consistent with the target LimTDD, thus improving the accuracy of quantum state preparation; after eliminating local operators and adjusting the quantum gates, the quantum circuit is simpler, reducing the difficulty and complexity of quantum state preparation, reducing the number of quantum gate operations, thereby reducing the error rate and resource consumption in the quantum state preparation process and improving preparation efficiency; it is applicable to quantum state preparation tasks corresponding to classical data in various preset technical fields, and can flexibly adjust the quantum gates according to the structure and characteristics of different target LimTDDs to achieve efficient and accurate quantum state preparation, thus expanding the application scope of quantum state preparation.

[0020] 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.

[0021] 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.

[0022] 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:

[0023] It includes: an acquisition module, a quantum circuit generation module, and a quantum state acquisition module;

[0024] The acquisition module is used to: acquire the target LimTDD corresponding to classic data in a preset technical field;

[0025] The quantum circuit generation module is used to: generate a first target quantum circuit for converting a target LimTDD into a new target LimTDD, wherein the quantum state of the new target LimTDD is... ;

[0026] The quantum state acquisition module is used to process the first target quantum circuit to obtain the quantum state corresponding to the classical data.

[0027] Based on the above scheme, the quantum state preparation system based on LimTDD of the present invention can be further improved as follows.

[0028] Furthermore, the quantum state acquisition module is specifically used for:

[0029] 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. 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] Inverse operations are performed on the local operators on the outgoing edges of each non-terminating node in the target LimTDD and on the local operators on the incoming edges of the target LimTDD, so that all local operators in the target LimTDD are eliminated. Then, based on the complex number of the outgoing edges of each non-terminating node in the target LimTDD, a corresponding quantum gate is applied to each non-terminating node to adjust the complex number of the outgoing edges of each non-terminating node to... Thus, a new target, LimTDD, was obtained.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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

[0036] 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:

[0037] Figure 1 This is a schematic flowchart of a quantum state preparation method based on LimTDD according to an embodiment of the present invention;

[0038] Figure 2 A schematic diagram of LimTDD for the target;

[0039] Figure 3 A schematic diagram of the first intermediate target, LimTDD;

[0040] Figure 4 A schematic diagram of the second intermediate target, LimTDD;

[0041] Figure 5 A schematic diagram of the third intermediate target, LimTDD;

[0042] Figure 6 A schematic diagram of the new target LimTDD;

[0043] Figure 7 This is a schematic diagram of LimTDD, which includes cases of bifurcation caused by different complex numbers.

[0044] Figure 8 A schematic diagram of the first target quantum circuit;

[0045] Figure 9 This is a schematic diagram of a quantum state preparation system based on LimTDD according to an embodiment of the present invention;

[0046] Figure 10 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0047] 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.

[0048] 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.

[0049] like Figure 1 As shown, an embodiment of the present invention provides a quantum state preparation method based on LimTDD, comprising the following steps:

[0050] S1. Obtain the target LimTDD corresponding to the classic data in the preset technical field;

[0051] S2. 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: ;

[0052] S3. Process the first target quantum circuit to obtain the quantum state corresponding to the classical data.

[0053] 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 electromagnetic field technology includes linear equations; the classical data in fluid mechanics technology includes linear equations; the classical data in physical system state recognition technology includes sensor data, actual measurement data, and theoretical calculation results; the classical data in time series prediction technology includes time series data; and the classical data in image recognition technology includes images. Specifically:

[0054] 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.

[0055] 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.

[0056] 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.

[0057] 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.

[0058] 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.

[0059] Optionally, in S3, the first target quantum circuit is processed to obtain the quantum state corresponding to the classical data, including:

[0060] S30. Perform the inverse operation on the first target quantum circuit to obtain the second target quantum circuit. On the quantum computer, starting from a preset quantum state, run the second target quantum circuit to obtain the quantum state corresponding to the classical data. The preset quantum state is... .

[0061] 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:

[0062] Inverse operations are performed on the local operators on the outgoing edges of each non-terminating node in the target LimTDD and on the local operators on the incoming edges of the target LimTDD, so that all local operators in the target LimTDD are eliminated. Then, based on the complex number of the outgoing edges of each non-terminating node in the target LimTDD, a corresponding quantum gate is applied to each non-terminating node to adjust the complex number of the outgoing edges of each non-terminating node to... Thus, a new target, LimTDD, was obtained.

[0063] Using classic data from the field of electromagnetic field technology as an example, this invention will be described, specifically including the following steps:

[0064] 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;

[0065] 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:

[0066]

[0067] in, 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 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.

[0068] 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 parameters to be solved in the linear equation system. The obtained target LimTDD is as follows: Figure 2 As shown.

[0069] 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 this diagram, y0, y1, and y2 represent non-terminal nodes (also known as internal nodes). Each edge has a weight, which consists of a complex number and a local operator. Each non-terminal node and edge can represent an unnormalized quantum state. Each non-terminal node has two outgoing edges, called low edges (represented by dashed lines) and high edges (represented by solid lines). 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-terminal 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.

[0070] Figure 2 In the diagram, the quantum state represented by node y0 is... , It should be noted that, It is an unnormalized quantum state, a quantum state. The quantum state represented by the low edge of the left y1 node can be represented using Dirac notation. The quantum state represented by the high edge of the left node y1 is: ,in, , This is a basic quantum gate, and the quantum state represented by the left-hand node y1 is: This allows us to deduce the quantum state represented by each non-terminating node.

[0071] Figure 2 In the context of LimTDD, the local operators on the incoming edges are: This can be represented as {2:x6,1:"2",0:"0"}, where "2:x6" in {2:x6,1:"2",0:"0"} indicates that an X gate is applied to the qubit corresponding to node y2. In the gate {2:x6,1:"2",0:"0"}, "1:"2" means that a gate is applied to both the qubit corresponding to the left y1 node and the qubit corresponding to the right y1 node. In the gate {2:x6,1:"2",0:"0"}, "0:"0" indicates that a unit operator I is applied to the qubit corresponding to the left node y0. When representing local operators, the mathematical representation of the unit operator I can be omitted. Therefore, the local operator on the incoming edge of the target LimTDD can be represented as {2:x6,1:"2"}. Here, the X gate is the fundamental quantum gate. , .

[0072] S102. 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: .

[0073] 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.

[0074] S102 includes:

[0075] S1020. Perform the inverse operation on the local operators on the outgoing edges of each non-terminating node in the target LimTDD and on the incoming edges of the target LimTDD, so that all local operators in the target LimTDD are eliminated. Specifically:

[0076] S10200, according to Figure 2 From the local operators on the incoming edges of the target LimTDD, we can see that... Applying this to the target LimTDD yields the first intermediate target LimTDD. This step can be called top operator elimination, meaning that the local operators on the topmost incoming edge of the target LimTDD are eliminated. The first intermediate target LimTDD is as follows: Figure 3 As shown.

[0077] S10201. After eliminating the local operators on the incoming edges of the target LimTDD, eliminate the local operators involved in the leftmost path branch of the first intermediate target LimTDD. The local operators involved in the leftmost path branch of the first intermediate target LimTDD include: the local operators on the two outgoing edges of node y2, the local operators on the two outgoing edges of the left node y1, and the local operators on the two outgoing edges of node y0. Since Figure 3 In the LimTDD algorithm, the local operator on the low edge of node y2 is the unit operator I, the local operator on the low edge of the left node y1 is the unit operator I, and the local operators on the two outgoing edges of node y0 are both constants 1. Therefore, it is necessary to eliminate the local operator {0:x4} on the high edge of node y2 in the first intermediate target LimTDD algorithm, and also to eliminate the local operator {0:4} on the high edge of the left node y1 in the first intermediate target LimTDD algorithm. These two local operators correspond to two two-bit control gates. When the qubit corresponding to node y2 is... At that time, an action is performed on the qubit corresponding to node y0. X gate; when the qubit corresponding to node y1 is At that time, an action is performed on the qubit corresponding to node y0. In other words, a two-bit control gate needs to be applied between the qubit corresponding to node y2 and the qubit corresponding to node y0: controlled- The X-gate applies a controlled-mode connection between the qubit corresponding to node y1 and the qubit corresponding to node y0. After applying these two two-bit control gates and eliminating any local operators that might have been generated on the incoming edges during this process, the second intermediate target LimTDD is obtained. This step can be called leftmost control gate elimination. The second intermediate target LimTDD is as follows: Figure 4 As shown.

[0078] S10202. Eliminate the differing local operators in the leftmost and rightmost path branches of the second intermediate target LimTDD. The local operators in the leftmost path branch of the second intermediate target LimTDD include: the local operator on the low edge of node y2, the local operators on the two outgoing edges of the left node y1, and the local operators on the two outgoing edges of node y0; the local operators in the rightmost path branch of the second intermediate target LimTDD include: the local operator on the high edge of node y2, the local operators on the two outgoing edges of the right node y1, and the local operators on the two outgoing edges of node y0. The differing local operators in the leftmost and rightmost path branches of the second intermediate target LimTDD are: the local operator on the high edge of the left node y1 and the local operator on the high edge of the right node y1. The local operator on the high edge of the left node y1 and the local operator on the high edge of the right node y1 differ by one operator: {0:x}. Eliminating this operator involves multi-bit control gates. Specifically, in Figure 4 In the diagram, the qubit corresponding to node y2 is... And the bit corresponding to node y1 is At that time, an action is performed on the qubit corresponding to node y0. Therefore, a CCX gate needs to be applied when multi-bit control gates are used. This step can be called multi-bit control gate elimination, resulting in the third intermediate target LimTDD, such as... Figure 5 As shown.

[0079] It should be noted that when the second intermediate target LimTDD includes multiple branches arranged from left to right, the difference between the first and second branches on the left is eliminated by S10202 to obtain a new second intermediate target LimTDD. Then, by S10202, the difference between the first and second branches on the left in the new second intermediate target LimTDD is eliminated until a LimTDD containing only one branch is generated, which is used as the third intermediate target LimTDD.

[0080] S1021. Based on the complex number of the outgoing edges of each non-terminating node in the target LimTDD, apply the corresponding quantum gate to each non-terminating node to adjust the complex number of the outgoing edges of each non-terminating node to... This leads to the new goal LimTDD, specifically:

[0081] Figure 5 The third intermediate target LimTDD shown no longer contains local operators, and the quantum state corresponding to the third intermediate target LimTDD has become a product state. However, the quantum states on the qubits corresponding to each non-termination node in the third intermediate target LimTDD are not necessarily all... Therefore, it is also necessary to apply a single-qubit quantum gate to prevent non-quantum gates. The quantum state becomes For a non- quantum state In other words, the single-qubit quantum gate that needs to be applied is . To represent a complex number, Figure 5 In the diagram, the quantum state of the qubit corresponding to node y1 is: Therefore, a single-qubit quantum gate is applied to the third intermediate target, LimTDD. , Similarly, the single-bit quantum gate applied to the other two qubits (the qubit corresponding to node y2 and the qubit corresponding to node y0) is: , Among them, single-qubit quantum gates It is only accurate to two decimal places; in actual operation, precise numbers are required. This step can be called single-bit gate elimination. After this step, a new target LimTDD is obtained, such as... Figure 6 As shown.

[0082] It should be noted that in actual practice, situations may arise where "the two branches in the second intermediate goal LimTDD (e.g., the leftmost and rightmost path branches) are bifurcated not because of different local operators, but because of different corresponding complex numbers," as in... Figure 7 As shown, a control gate is needed to merge the two branches. However, the control gate is not composed of different operators, but is determined by different complex numbers.

[0083] It should be noted that when the different complex numbers are respectively and Apply After that, you can Become This enables the merging of branches belonging to different complex numbers, for example, Figure 7 In the text, the different complex numbers are respectively and Therefore, a controlled-mode is applied between the qubit corresponding to node y2 and the qubit corresponding to node y1. The gate can merge the branch to which the left y1 node belongs with the branch to which the right y1 node belongs. The merged LimTDD is the schematic diagram of the third intermediate target LimTDD. Then, the subsequent steps are executed until the new target LimTDD is obtained.

[0084] The quantum state corresponding to the new target LimTDD and The difference lies only in a global phase, and since the global phase does not alter the statistical properties of the quantum state, it is possible to determine a quantum state that can transform classical data into... The first target quantum circuit, this first target quantum circuit is as follows Figure 8 As shown.

[0085] S103. Perform the inverse operation on the first target quantum circuit to obtain the second target quantum circuit. On the quantum computer, starting from a preset quantum state, run the second target quantum circuit to obtain the quantum state corresponding to the classical data. The preset quantum state is... .

[0086] 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:

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] This invention is primarily based on a novel data structure. LimTDD stands out for its efficiency and compactness, accurately capturing isomorphism relations in quantum states. Leveraging these relations, LimTDD effectively compresses the memory space required to represent quantum states, making the representation of large-scale quantum states much simpler. In fields such as quantum machine learning and solving quantum equations, the accuracy and efficiency of preparing large-scale quantum states have always been a challenge. This solution aims to address this key issue: how to efficiently design corresponding quantum circuits based on the classical representation of large-scale quantum states. The advantage of this invention lies in its significant improvement in the scale of accurately prepared quantum states achieved by employing LimTDD and combining it with a specially designed algorithm, while substantially reducing the time and circuit complexity required for the preparation process. Specifically, given a LimTDD, this solution sequentially performs a series of operations, including global operator elimination, leftmost control gate elimination, multi-bit control gate elimination, single-bit gate elimination, and difference weight merging. In practical applications, these steps can be flexibly adjusted in order or repeated as needed until the final representation is obtained. The process is not only efficient but also highly flexible and adaptable, providing an innovative solution for quantum state preparation in the field of quantum computing.

[0093] 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.

[0094] like Figure 9 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;

[0095] The acquisition module 201 is used to: acquire the target LimTDD corresponding to classic data in a preset technical field;

[0096] Quantum circuit generation module 202 is used to: generate a first target quantum circuit for converting a target LimTDD into a new target LimTDD, wherein the quantum state of the new target LimTDD is... ;

[0097] 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.

[0098] Optionally, in the above technical solution, the quantum state acquisition module 203 is specifically used for:

[0099] 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. The preset quantum state is... .

[0100] 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:

[0101] Inverse operations are performed on the local operators on the outgoing edges of each non-terminating node in the target LimTDD and on the local operators on the incoming edges of the target LimTDD, so that all local operators in the target LimTDD are eliminated. Then, based on the complex number of the outgoing edges of each non-terminating node in the target LimTDD, a corresponding quantum gate is applied to each non-terminating node to adjust the complex number of the outgoing edges of each non-terminating node to... Thus, a new target, LimTDD, was obtained.

[0102] Optionally, in the above technical solutions, 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] In one alternative embodiment, an electronic device is provided, such as Figure 10 As shown, Figure 10The 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.

[0109] 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.

[0110] 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 10 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] It should be noted that, Figure 10 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.

[0115] 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-described quantum state preparation methods based on LimTDD.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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 methods shown in the above embodiments.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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 a quantum state based on LimTDD, characterized in that, include: Obtain the target LimTDD corresponding to classic data in the preset technical field; generating a first target quantum circuit for converting the target LimTDD into a new target LimTDD, wherein a quantum state of the new target LimTDD is ; The first target quantum circuit is processed to obtain the quantum state corresponding to the classical data; The process by which the first target quantum circuit converts the target LimTDD into a new target LimTDD includes: The local operators on the outgoing edges of each non-terminating node in the target LimTDD and the local operators on the incoming edges of the target LimTDD are inversely operated to eliminate all local operators in the target LimTDD. Then, based on the complex number of the outgoing edges of each non-terminating node in the target LimTDD, a corresponding quantum gate is applied to each non-terminating node to adjust the complex number of the outgoing edges of each non-terminating node to... Thus, a new target, LimTDD, was obtained.

2. The method of claim 1, wherein, Processing the first target quantum circuit to obtain the quantum state corresponding to the classical data includes: The first target quantum circuit is reversely operated to obtain a second target quantum circuit, and a quantum state corresponding to the classical data is obtained by running the second target quantum circuit on a quantum computer starting from a preset quantum state, wherein the preset quantum state is .

3. The method of claim 1 or 2, wherein, 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.

4. A system for preparing quantum states based on LimTDD, characterized in that, include: The module includes a quantum state 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 configured to generate a first target quantum circuit for converting the target LimTDD into a new target LimTDD, wherein a quantum state of the new target LimTDD is ; 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 process by which the first target quantum circuit converts the target LimTDD into a new target LimTDD includes: The local operators on the outgoing edges of each non-terminating node in the target LimTDD and the local operators on the incoming edges of the target LimTDD are inversely operated to eliminate all local operators in the target LimTDD. Then, based on the complex number of the outgoing edges of each non-terminating node in the target LimTDD, a corresponding quantum gate is applied to each non-terminating node to adjust the complex number of the outgoing edges of each non-terminating node to... Thus, a new target, LimTDD, was obtained.

5. The quantum state preparation system based on LimTDD of claim 4, wherein, The quantum state acquisition module is specifically used for: The first target quantum circuit is reversely operated to obtain a second target quantum circuit, and a quantum state corresponding to the classical data is obtained by running the second target quantum circuit on a quantum computer starting from a preset quantum state, wherein the preset quantum state is .

6. The system for preparing a quantum state based on LimTDD according to any one of claims 4-5, wherein, 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.

7. An electronic device, comprising: The invention 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 quantum state preparation method based on LimTDD as described in any one of claims 1 to 3.

8. 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 quantum state preparation method based on LimTDD as described in any one of claims 1 to 3.

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