Method and apparatus for designing dielectric composite material, and device, computing system and medium

Abstract

The present application relates to a method and apparatus for designing a dielectric composite material, and a device, a computing system and a medium. The method comprises: establishing a QUBO model on the basis of data of a dielectric composite material, a design optimization target and a decision space, wherein the QUBO model comprises: a first item, which is used for causing the dielectric constant of the dielectric composite material at each wavelength to be close to a target value, a second item, which is used for causing the property of the dielectric composite material to be close to a target property, and a third item, which carries a second penalty coefficient and is used for satisfying a constraint item, a constraint condition of the constraint item being used for selecting a unique composite scheme, which comprises a filling material, the filling rate of the filling material, and a substrate material; and solving the QUBO model on the basis of quantum computing, so as to obtain the composition of the dielectric composite material having an optimal dielectric function, and a corresponding filling ratio. By means of implementing the embodiments of the present application, the computing efficiency for design optimization of a dielectric composite material can be improved, and the accuracy of a computing result is improved.

Description

Dielectric composite material design method, device, equipment, computing system and medium Technical Field

[0001] The present application relates to a dielectric composite material design method, device, equipment, computing system and medium, belonging to the field of composite material design optimization. Background Art

[0002] Current computing technology is based on the optimization algorithms of traditional computers, including exact solution algorithms (such as branch and bound algorithms) based on integer programming and mixed integer programming mathematical models, heuristic algorithms, etc.

[0003] Currently, large-scale computations are performed on classical supercomputers, whose speed is determined by Moore's Law for logic LSIs. In the future, when the amount of big data increases dramatically, classical computers will be unable to handle these large-scale computations because the scaling of the gate length of logic LSIs will almost reach its manufacturing limit.

[0004] Therefore, it is difficult to use existing technologies to calculate the design of large-scale dielectric composite materials. As the scale of the problem increases (the number of material types increases and the filling rate accuracy increases), the computational complexity increases exponentially and the difficulty of solving the problem increases sharply. Existing solution technologies are difficult to complete the solution in a short period of time.

[0005] Summary of the Invention

[0006] In view of this, embodiments of the present application provide a dielectric composite material design method, apparatus, quantum computer device, computing system, and readable storage medium, which can solve the technical problem of the prior art in the difficult calculation of complex dielectric material design optimization.

[0007] In a first aspect, an embodiment of the present application discloses a method for designing a dielectric composite material, the method comprising:

[0008] A QUBO model is established based on the data, design optimization objectives and decision space of the dielectric composite material. The QUBO model includes:

[0009] The first item is used to make the dielectric constant of the dielectric composite material at each wavelength close to the target value,

[0010] The second item is used to make the properties of the dielectric composite material close to the target properties,

[0011] The third item carries a second penalty coefficient and is used to satisfy a constraint item, wherein the constraint condition of the constraint item is used to select a unique composite solution, wherein the composite solution includes a filling material, a filling rate of the filling material, and a base material;

[0012] The QUBO model is solved based on quantum computing, thereby obtaining the composition and corresponding filling ratio of the dielectric composite material with the optimal dielectric function.

[0013] Optionally, the second item carries a first penalty coefficient.

[0014] Optionally, the QUBO model specifically includes:

[0015] Among them, ∈tar λ represents the target dielectric constant at wavelength λ; ∈ iλ Represents the dielectric constant of the filling material at wavelength λ; ∈ jλ represents the dielectric constant of the substrate material at wavelength λ; x represents the decision variable to be optimized; K represents the set of all combinations of materials and mixing ratios; x k Indicates that the kth material combination and the corresponding proportion are selected; Λ represents the set of wavelengths; f (k) Indicates the filling ratio of the filling material; M1 indicates the first penalty coefficient; M2 indicates the second penalty coefficient; and They represent the relevant material properties of the filling material and base material that need to be considered in the optimization process; Ptar represents the optimization target of the material properties.

[0016] Optionally, solving the QUBO model based on quantum computing to obtain the composition and corresponding filling ratio of the dielectric composite material with the optimal dielectric function includes:

[0017] Converting the QUBO model into an Ising model;

[0018] The variable values ​​corresponding to the global optimal solution are solved according to the Ising model, so that the service node can restore the composition and corresponding filling ratio of the dielectric composite material with the optimal dielectric function according to the variable values.

[0019] Furthermore, solving the variable values ​​corresponding to the global optimal solution according to the Ising model includes:

[0020] The matrix coefficients of the Ising model are received in the form of optical pulses to obtain the variable values.

[0021] Optionally, solving the QUBO model based on quantum computing to obtain the composition and corresponding filling ratio of the dielectric composite material with the optimal dielectric function includes:

[0022] Running the QAOA quantum circuit to perform multiple transformations on the QUBO model to obtain a quantum state;

[0023] Calculating the expected value of the QUBO model using the quantum state in a classical computer;

[0024] An approximate solution to the target problem is calculated based on the expected value, wherein the approximate solution is the composition of the dielectric composite material with the optimal dielectric function and the corresponding filling ratio.

[0025] A second aspect of an embodiment of the present application discloses a dielectric composite material design device, the device comprising:

[0026] A module is provided for establishing a QUBO model based on data, design optimization objectives, and decision space of a dielectric composite material. The QUBO model includes:

[0027] The first item is used to make the dielectric constant of the dielectric composite material at each wavelength close to the target value,

[0028] The second item is used to make the properties of the dielectric composite material close to the target properties,

[0029] The third item carries a second penalty coefficient and is used to satisfy a constraint item, wherein the constraint condition of the constraint item is used to select a unique composite solution, wherein the composite solution includes a filling material, a filling rate of the filling material, and a base material;

[0030] The solution module is used to solve the QUBO model based on quantum computing, thereby obtaining the composition and corresponding filling ratio of the dielectric composite material with the optimal dielectric function.

[0031] A third aspect of an embodiment of the present application discloses a quantum computer device, including a quantum memory and a quantum processor. The quantum memory stores a computer program, and when the computer program is executed by the quantum processor, any one of the dielectric composite material design methods disclosed in the embodiments of the present application is implemented.

[0032] A fourth aspect of an embodiment of the present application discloses a computing system, including a processor and a memory for storing a program executable by the processor. When the processor executes the program stored in the memory, any one of the dielectric composite material design methods disclosed in the embodiment of the present application is implemented.

[0033] A fifth aspect of the embodiments of the present application discloses a readable storage medium storing a program. When the program is executed by a processor, any one of the dielectric composite material design methods disclosed in the embodiments of the present application is implemented.

[0034] Compared with the related art, the embodiments of the present application have the following beneficial effects:

[0035] Based on the characteristics of dielectric composite materials, the embodiments of the present application design a combinatorial optimization algorithm suitable for quantum computing and use a quantum computer to accelerate the solution of the problem, thereby improving the computational efficiency of the design optimization of dielectric composite materials and improving the accuracy of the calculation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without any creative work.

[0037] FIG1 is a flowchart illustrating a method for designing a dielectric composite material according to an embodiment of the present application.

[0038] FIG2 is a graph showing a comparison of wavelengths of the optimized dielectric and the target dielectric provided in an embodiment of the present application.

[0039] FIG3 is a diagram illustrating an exemplary architecture of a dielectric composite material design device provided in an embodiment of the present application.

[0040] FIG4 is a diagram showing an example of the structure of a quantum computer device provided in an embodiment of the present application.

[0041] FIG5 is a structural diagram illustrating an example of a computing system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0043] In the specification and claims of this application, the terms "first," "second," etc. are used to distinguish similar objects, and are not used to describe a specific order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, so that the embodiments of this application can be implemented in orders other than those illustrated or described herein. The terms "first" and "second" generally distinguish objects of the same type, and do not limit the number of objects. For example, the first object can be one or more.

[0044] It should be noted that in the embodiments of this application, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplary" or "for example" in the embodiments of this application should not be interpreted as being more preferred or advantageous than other embodiments or designs. Rather, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0045] Some nouns or terms that appear in the description of the embodiments of this application are subject to the following interpretations:

[0046] 1. Dielectric material is an electrical insulating material that can be used to make capacitors.

[0047] 2. Dielectric composite materials refer to materials composed of two or more dielectric materials, and the dielectric function of this material meets specific property requirements.

[0048] 3. Design optimization of dielectric composite materials refers to selecting the optimal material composition and mixing ratio so that the properties of the dielectric composite materials can meet specific property requirements as much as possible, that is, the corresponding dielectric function is optimized, thereby realizing the reverse design of the dielectric composite materials.

[0049] 4. Combinatorial optimization is a mathematical optimization technique consisting of discrete decision variables, an objective function, and constraints. The goal is to find the optimal value of the objective function and the corresponding optimal solution. If the objective function is quadratic, there are no constraints, and the decision variables can only take the values ​​of 0 or 1, this type of combinatorial optimization is called Quadratic Unconstrained Binary Optimization (QUBO). The QUBO model form can be solved by specialized quantum computers such as quantum annealers or coherent Ising machines.

[0050] There are many kinds of composite materials that can be formed by combining various dielectric materials with each other. At present, humans have mastered the preparation process of only a very small part of the materials, and there are still many unknown materials waiting for humans to discover. The cycle required for a new material to go from proposing a concept to continuously trying and exploring to finally forming a mature preparation scheme is long, and the rapid optimization of dielectric composite materials is expected to achieve efficient discovery and development of new materials. Based on this, the embodiment of the present application provides a dielectric composite material design method, device, quantum computer equipment, computing system and readable storage medium, which uses a dedicated quantum computer to accelerate the solution of dielectric composite material design problems, thereby improving computing efficiency and achieving accurate and rapid optimization of dielectric composite materials. Its specific implementation plan is as follows:

[0051] As shown in FIG1 , FIG1 is a flow chart of a dielectric composite material design method provided in an embodiment of the present application. The dielectric composite material design method may include the following steps:

[0052] S101. Establish a QUBO model based on the data, design optimization objectives, and decision space of the dielectric composite material.

[0053] In this embodiment, first, the sets and data used in the dielectric composite material design optimization problem are defined as follows:

[0054] Define a set of candidate dielectric materials I, such as I = {material 1, material 2, ...}.

[0055] Define a wavelength set Λ, for example, Λ = {1000nm, 2000nm,…}.

[0056] Define a set F of filling rates, for example, F = {0.01, 0.02, ...}.

[0057] Definition ∈ iλ is the dielectric constant of material i∈I at wavelength λ∈Λ.

[0058] Definition ∈tar λ is the target dielectric constant that the composite dielectric material should approach as close as possible at wavelength λ∈Λ.

[0059] Define Pi as some relevant property of material i, such as ductility, mass, etc.

[0060] Ptar is defined as the target value that the properties of the composite dielectric material should approach as close as possible.

[0061] Then, the decision space and objectives of the dielectric composite design optimization problem are set, including:

[0062] 1. The decision space for the dielectric composite material design optimization problem is to select a specific material combination and the corresponding filling ratio from the candidate materials.

[0063] 2. The goal of the dielectric composite material design optimization problem is to make the dielectric constant of the dielectric composite material at each wavelength as close to the target dielectric constant as possible, while at the same time ensuring that the properties of the dielectric composite material are close to the target value.

[0064] Finally, the QUBO model is established based on the above set and data and the above decision space and goals, including:

[0065] Optionally, the QUBO model includes:

[0066] The first item is used to make the dielectric constant of the dielectric composite material at each wavelength close to the target value.

[0067] The second item is used to make the properties of the dielectric composite material close to the target properties.

[0068] The third item carries the second penalty coefficient and is used to satisfy the constraint item, where the constraint condition of the constraint item is used to select a unique composite solution, which includes the filling material, the filling rate of the filling material, and the base material.

[0069] Optionally, the QUBO model includes:

[0070] The first item is used to make the dielectric constant of the dielectric composite material at each wavelength close to the target value.

[0071] The second term, carrying a first penalty coefficient, is used to make the properties of the dielectric composite material close to target properties.

[0072] The third item carries the second penalty coefficient and is used to satisfy the constraint item, where the constraint condition of the constraint item is used to select a unique composite solution, which includes the filling material, the filling rate of the filling material, and the base material.

[0073] It should be noted that the second penalty coefficient is necessary, and its value is large and has a strong constraint in order to determine an optimal material combination and corresponding filling ratio.

[0074] Specifically, to meet the design optimization requirements of dielectric composite materials, define the binary variable x k , any value of index k represents a specific material design scheme, namely the selection of filling material, filling rate of filling material and selection of base material. If the scheme is executed, x k Takes 1, otherwise takes 0. Since only one composite solution can be selected, the following constraints apply:

[0075] The objective function of the QUBO model is:

[0076] Among them, ∈tar λ Represents the target dielectric constant (including real and imaginary parts) at wavelength λ; ∈ iλ Represents the dielectric constant of the filling material at wavelength λ (including real and imaginary parts); ∈ jλ represents the dielectric constant of the substrate material at wavelength λ (including real and imaginary parts); x represents the decision variable to be optimized; K represents the set of all combinations of materials and mixing ratios; x k Indicates that the kth material combination and the corresponding proportion are selected; Λ represents the set of wavelengths; f (k) Indicates the filling ratio of the filling material; M1 represents the first penalty coefficient, and M2 represents the second penalty coefficient, which is used to introduce constraint conditions. The larger the value, the better the constraint; and They represent the relevant material properties of the filling material and base material that need to be considered in the optimization process, with Ptar as the optimization target.

[0077] The purpose of step S101 is to map the optimization objective to a specific QUBO form suitable for dedicated quantum computing solutions.

[0078] S102 , solving the QUBO model based on quantum computing, thereby obtaining the composition and corresponding filling ratio of the dielectric composite material with the optimal dielectric function.

[0079] The QUBO model of this embodiment is applicable to special quantum computers such as quantum annealing machines and coherent Ising machines (CIM). The implementation and solution of the physical machine takes the CIM based on the degenerate optical parametric oscillator (DOPO) as an example. This is a hybrid quantum computing system consisting of an optical part and an electrical part. The optical part includes a laser, an amplifier, a periodically poled lithium niobate crystal (PPLN) and an optical fiber loop. The laser uses a femtosecond pulse fiber laser and is equipped with an amplifier system. The amplified laser is first frequency-doubled using a PPLN crystal. The frequency-doubled laser is used as a pump source to synchronously pump the PPLN crystal in a fiber loop to form a degenerate optical parametric oscillation. Hundreds of oscillation pulses can exist simultaneously in the fiber loop. The electrical part includes an FPGA (Field Programmable Gate Array), AD / DA (digital analog / analog digital conversion) and a phase detection part. The laser output from the fiber loop and the fundamental frequency laser are measured using a phase detector, allowing the phase of the output light to be tested. FPGA is used with high-speed AD / DA for optical pulse measurement and feedback control.

[0080] Unlike classical computers, which run on semiconductor integrated circuits, CIM uses laser pulses in optical fibers as qubits for computation. In DOPO, pump light is incident on a nonlinear optical crystal, splitting it into two beams. These two beams have the same polarization direction and a frequency half that of the pump light, resulting in a compressed state that serves as a qubit. By gradually increasing the pump light power above the oscillation threshold, the generated light becomes coherent, with its phase splitting into two states (phase 0 and π). This phase can then be set to ±1 relative to the spin to solve optimization problems.

[0081] For example (1), follow these steps to get the material design:

[0082] S11. The user terminal transmits data such as dielectric constant to the server terminal, and the server terminal establishes a QUBO model based on the received data.

[0083] S12. The server inputs the converted Ising matrix coefficients into the coherent Ising machine in the form of optical pulses.

[0084] S13. The coherent Ising machine quickly obtains the variable values ​​corresponding to the global optimal solution of the function.

[0085] S14. The coherent Ising machine transmits the value of the variable back to the server.

[0086] S15. The server receives the data and restores the corresponding material design solution.

[0087] In some other embodiments, the model can also be solved by the Quantum Approximate Optimization Algorithm (QAOA). QAOA is a hybrid algorithm of classical and quantum that can solve combinatorial optimization problems on gate-based quantum computers. Based on the QUBO form, it can be converted into the Ising model and the corresponding maximum cut problem. The objective function can be expressed as maximizing Define two rotation unitary matrices U(C,γ)=e -iγC and U(B,β)=e -iβB After repeating the operation P times, the following new state can be obtained:

[0088] |ψ(γ,β)〉=|γ,β〉=U(B,β p )U(C,γ p )...U(B, β2)U(C, γ2)U(B, β1)U(C, γ1)|ψ>;

[0089] Example (2): Follow the steps below to get the material design solution:

[0090] S21. The user terminal transmits data such as dielectric constant to the server terminal, and the server terminal establishes a QUBO model based on the received data.

[0091] S22. Build a QAOA quantum circuit, which contains trainable parameters.

[0092] S23. Initialize the parameters in the circuit and obtain an initial quantum state in the quantum computer.

[0093] S24. Run the quantum circuit and perform P transformations on the target function, i.e., the QUBO model, to obtain the quantum state |ψ P (γ, β)>.

[0094] S25. Using quantum states |ψ in classical computers P(γ, β)>Calculate the expected value of the objective function.

[0095] S26. Repeat steps S23 to S25 several times, that is, measure the same set of parameters γ and β multiple times, so as to obtain the quantum state distribution.

[0096] S27. Use grid search to optimize the parameters in the circuit. Repeat steps S23 to S26 for a new set of parameters γ and β. After obtaining the quantum state distribution, select the one with the largest target value.

[0097] S28. Based on the result of step S25, an approximate solution to the target problem is calculated. The approximate solution is the composition of the dielectric composite material with the optimal dielectric function and the corresponding filling ratio, that is, the material design solution.

[0098] As shown in Figure 2, the real wavelength of the optimized dielectric is very close to that of the target dielectric, while the imaginary wavelength does not overlap slightly. However, the error is still within an acceptable range and can be improved by adjusting the optimization accuracy of the fill rate.

[0099] Those skilled in the art will appreciate that all or part of the steps in the method for implementing the above embodiments may be completed by instructing related hardware through a program, and the corresponding program may be stored in a computer-readable storage medium.

[0100] It should be noted that although the method operations of the above embodiments are described in a particular order in the accompanying drawings, this does not require or imply that the operations must be performed in this particular order, or that all of the illustrated operations must be performed to achieve the desired results. Rather, the depicted steps may be performed in a different order. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into a single step, and / or a single step may be broken down into multiple steps.

[0101] As shown in Figure 3, Figure 3 is an architecture diagram of a dielectric composite material design device provided in an embodiment of the present application. The dielectric composite material design device is applied to a quantum computer device or computing system and may include the following modules:

[0102] Establishing module 301 is used to establish a QUBO model based on the data of the dielectric composite material, the design optimization goal and the decision space. The QUBO model includes:

[0103] The first item is used to make the dielectric constant of the dielectric composite material at each wavelength close to the target value,

[0104] The second item is used to make the properties of the dielectric composite material close to the target properties,

[0105] The third item carries a second penalty coefficient and is used to satisfy a constraint item, wherein the constraint condition of the constraint item is used to select a unique composite solution, wherein the composite solution includes a filling material, a filling rate of the filling material, and a base material;

[0106] The solving module 302 is used to solve the QUBO model based on quantum computing, and thereby obtain the composition and corresponding filling ratio of the dielectric composite material with the optimal dielectric function.

[0107] As shown in Figure 4, Figure 4 is a block diagram of a quantum computer device provided in an embodiment of the present application. The quantum computer device may include a quantum processor 402 and a quantum memory 403 connected by a system bus 401. The quantum memory stores a computer program that, when executed by the quantum processor, implements any of the dielectric composite material design methods disclosed in the embodiments of the present application.

[0108] As shown in Figure 5, Figure 5 is a structural diagram of a computing system provided in an embodiment of the present application. The computing system may include a processor 502, a memory, an input device 503, a display device 504, and a network interface 505 connected via a system bus 501. Among them, the processor 502 is used to provide computing and control capabilities, and the memory includes a non-volatile storage medium 506 and an internal memory 507. The non-volatile storage medium 506 stores an operating system, a computer program, and a database. The internal memory 507 provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium 506. When the computer program is executed by the processor 502, any one of the dielectric composite material design methods disclosed in the embodiments of the present application is implemented.

[0109] The embodiments of the present application disclose a storage medium. The storage medium is a computer-readable storage medium that stores a computer program. When the computer program is executed by a processor, it implements any one of the dielectric composite material design methods disclosed in the embodiments of the present application.

[0110] It should be noted that the computer-readable storage medium of the present embodiment may be a computer-readable signal medium or a computer-readable storage medium or any combination thereof. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0111] In this embodiment, a computer-readable storage medium may 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. Furthermore, in this embodiment, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries a computer-readable program. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable storage medium other than a computer-readable storage medium that can transmit, propagate, or transfer a program for use by or in conjunction with an instruction execution system, apparatus, or device. The computer program contained on a computer-readable storage medium may be transmitted using any suitable medium, including but not limited to wires, optical cables, RF (radio frequency), etc., or any suitable combination thereof.

[0112] The computer readable storage medium can be written in one or more programming languages ​​or a combination thereof to execute the computer program for performing the present embodiment, including object-oriented programming languages ​​such as Java, Python, C++, and conventional procedural programming languages ​​such as C or similar programming languages. The program can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (such as through the Internet using an Internet service provider).

[0113] In summary, based on the characteristics of dielectric composite materials, the embodiments of the present application design a combinatorial optimization algorithm suitable for quantum computing, use a quantum computer to accelerate the solution of the problem, thereby improving the computational efficiency of the design optimization of dielectric composite materials and improving the accuracy of the calculation results.

[0114] The above is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes based on the technical solution and inventive concept of the present invention within the scope disclosed by the present invention, which falls within the scope of protection of the present invention.

Claims

1. A method for designing a dielectric composite material, characterized in that The method includes: Establishing a QUBO model based on the data of the dielectric composite material, the design optimization objective, and the decision space; Solving the QUBO model based on quantum computing, and further obtaining the composition and corresponding filling ratio of the dielectric composite material with the optimal dielectric function; The QUBO model includes: The first term, which is used to make the dielectric constant of the dielectric composite material approach the target value at each wavelength; The second term, carrying a first penalty coefficient, which is used to make the properties of the dielectric composite material approach the target properties; The third term, carrying a second penalty coefficient, which is used to satisfy the constraint term, and the constraint conditions of the constraint term are used to select a unique composite scheme, and the composite scheme includes the filling material, the filling rate of the filling material, and the substrate material; The QUBO model is as follows: where, ∈tar λ represents the target dielectric constant at wavelength λ; ∈ iλ represents the dielectric constant of the filling material at wavelength λ; ∈ jλ represents the dielectric constant of the substrate material at wavelength λ; x represents the decision variable to be optimized; K represents the set of all material and mixing ratio combinations; x k represents the selection of the k-th material combination and the corresponding ratio; Λ represents the set of wavelengths; f (k) represents the filling ratio of the filling material; M1 represents the first penalty coefficient; M2 represents the second penalty coefficient; And respectively represent the relevant material properties of the filling material and the substrate material that need to be considered in the optimization process; Ptar represents the optimization objective of the material properties; The solving the QUBO model based on quantum computing, and further obtaining the composition and corresponding filling ratio of the dielectric composite material with the optimal dielectric function includes: Running the QAOA quantum circuit, performing multiple transformations on the QUBO model to obtain a quantum state; Calculating the expected value of the QUBO model using the quantum state in a classical computer; Calculating an approximate solution to the target problem according to the expected value, and the approximate solution is the composition and corresponding filling ratio of the dielectric composite material with the optimal dielectric function.

2. The method according to claim 1, wherein The solving the QUBO model based on quantum computing, and further obtaining the composition and corresponding filling ratio of the dielectric composite material with the optimal dielectric function includes: Converting the QUBO model into an Ising model; Solving the variable values corresponding to the global optimal solution according to the Ising model, so that the service node can restore the composition and corresponding filling ratio of the dielectric composite material with the optimal dielectric function according to the variable values.

3. The method according to claim 2, characterized in that The solving the variable values corresponding to the global optimal solution according to the Ising model includes: Receiving the matrix coefficients of the Ising model in the form of optical pulses to obtain the variable values.

4. A dielectric composite material design device, characterized in that The device includes: A building module, which is used to establish a QUBO model based on the data of the dielectric composite material, the design optimization objective, and the decision space; A solving module, which is used to solve the QUBO model based on quantum computing, and further obtain the composition and corresponding filling ratio of the dielectric composite material with the optimal dielectric function; The QUBO model includes: The first term, which is used to make the dielectric constant of the dielectric composite material approach the target value at each wavelength; The second term, carrying a first penalty coefficient, which is used to make the properties of the dielectric composite material approach the target properties; The third term, carrying a second penalty coefficient, which is used to satisfy the constraint term, and the constraint conditions of the constraint term are used to select a unique composite scheme, and the composite scheme includes the filling material, the filling rate of the filling material, and the substrate material; The QUBO model is as follows: where ∈tar λ represents the target dielectric constant at wavelength λ; ∈ iλ represents the dielectric constant of the filling material at wavelength λ; ∈ jλ represents the dielectric constant of the substrate material at wavelength λ; x represents the decision variable to be optimized; K represents the set of all material and mixing ratio combinations; x k represents the selection of the k-th material combination and the corresponding ratio; Λ represents the set of wavelengths; f (k) represents the filling ratio of the filling material; M1 represents the first penalty coefficient; M2 represents the second penalty coefficient; and respectively represent the relevant material properties of the filling material and the substrate material that need to be considered in the optimization process; Ptar represents the optimization objective of the material properties; Solving the QUBO model based on quantum computing, and further obtaining the composition and corresponding filling ratio of the dielectric composite material with the optimal dielectric function, including: Running the QAOA quantum circuit, performing multiple transformations on the QUBO model to obtain a quantum state; Calculating the expected value of the QUBO model using the quantum state in a classical computer; Calculating an approximate solution to the target problem according to the expected value, and the approximate solution is the composition and corresponding filling ratio of the dielectric composite material with the optimal dielectric function.

5. A quantum computer device, characterized in that, Including a quantum memory and a quantum processor, the quantum memory stores a computer program, and when the computer program is executed by the quantum processor, the method according to any one of claims 1 to 3 is implemented.

6. A computing system, characterized in that, Including a processor and a memory for storing a program executable by the processor, when the processor executes the program stored in the memory, the method according to any one of claims 1 to 3 is implemented.

7. A readable storage medium storing a program, characterized in that, When the program is executed by the processor, the method according to any one of claims 1 to 3 is implemented.

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