Method and device for simulating aerodynamic performance of an aircraft

Abstract

The application discloses an aircraft aerodynamic performance simulation method and device, and the method comprises the following steps: determining an optimized quantum hardware pseudo-circuit according to a Hamiltonian, and obtaining an optimal quantum state through the optimized quantum hardware pseudo-circuit, wherein the Hamiltonian is used for describing the movement of the aircraft in a preset flow field; taking the optimal quantum state as a target quantum state, determining the optimal quantum state by using a hybrid tomography method, and determining an aerodynamic performance parameter of the aircraft in the preset flow field according to the optimal quantum state. It can be seen that the number of quantum state copies required by the hybrid tomography method is only related to the sampling accuracy, and is independent of the number of quantum bits, thereby significantly saving quantum computing resources and reducing the hardware cost of the aircraft aerodynamic performance simulation; on the other hand, the quantum state is reconstructed by using the hybrid tomography method, and the Hadamard Test circuit is not needed, so that the aircraft aerodynamic performance simulation can be applied to more complex environments.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of quantum computing, and in particular to an aircraft aerodynamic performance simulation method and device. BACKGROUND

[0002] Aircraft aerodynamic performance simulation refers to studying the motion law of the fluid around the aircraft when flying in the atmosphere and the interaction between the aircraft and the fluid through numerical calculation and simulation methods, so as to predict and evaluate the aerodynamic performance of the aircraft. Aerodynamic performance simulation plays an important role in aircraft design, optimization and performance evaluation, which can reduce the number of wind tunnel tests and flight tests, shorten the design cycle and reduce the research and development cost.

[0003] Currently, the method of computational fluid dynamics is mainly used for aircraft aerodynamic performance simulation. First, a three-dimensional model is established according to the geometric shape of the aircraft, and then the space around the model is discretized to generate a calculation grid. Next, boundary conditions and initial conditions are set according to the flight state, and appropriate fluid mechanics control equations (such as Euler equations, Navier-Stokes equations, etc.) are selected. Finally, the control equations are discretized using numerical discretization methods (such as finite difference, finite volume, finite element, etc.), linear equations are established, and the variational quantum linear solver (VQLS) algorithm is used to solve the linear equations on a quantum computer to obtain the flow field physical quantities (such as velocity, pressure, temperature, etc.) at each discrete point in space, thereby obtaining the aerodynamic performance parameters (such as lift, drag, pitch moment, etc.) of the aircraft.

[0004] However, the conversion of quantum states to classical data requires the use of quantum state tomography technology, which requires a large number of quantum state copies, and the number of copies required increases exponentially with the number of quantum bits. This leads to high consumption of quantum resources for aircraft aerodynamic performance simulation. Moreover, for high-dimensional or complex Hamiltonians, existing noisy intermediate-scale quantum (NISQ) devices do not support key quantum gate operations for obtaining loss functions, making it infeasible to use existing devices to implement VQLS algorithms for complex Hamiltonians, further making it difficult for aircraft aerodynamic performance simulation to be applicable to complex environments. SUMMARY

[0005] The present application provides an aircraft aerodynamic performance simulation method and device, which can significantly save quantum computing resources, reduce the hardware cost of aircraft aerodynamic performance simulation, and make the aircraft aerodynamic performance simulation applicable to more complex environments.

[0006] To solve the above technical problems, the first aspect of the present application discloses an aircraft aerodynamic performance simulation method, which comprises:

[0007] The optimized quantum hardware circuit is determined based on the Hamiltonian, and the optimal quantum state is obtained through the optimized quantum hardware circuit. The Hamiltonian is used to describe the motion of the aircraft in the preset flow field.

[0008] Using the optimal quantum state as the target quantum state, the optimal quantum state is determined by hybrid tomography, and the aerodynamic performance parameters of the aircraft in the preset flow field are determined based on the optimal quantum state.

[0009] The hybrid tomography method refers to determining the symbol information contained in the target quantum state by using the direct product obtained from classical calculation, and determining the amplitude information contained in the target quantum state by taking the square root of the probability obtained from quantum state tomography sampling. The target quantum state is determined based on the symbol information and the amplitude information. The direct product is the direct product of the n data corresponding to the 0 state and the n data corresponding to the 1 state of the n qubits in the quantum hardware proposed circuit.

[0010] As an optional implementation, in a first aspect of the present invention, determining the aerodynamic performance parameters of the aircraft in the preset flow field based on the optimal quantum state includes:

[0011] The optimal quantum state is converted into classical data according to the first formula, and the aerodynamic performance parameters of the aircraft in the preset flow field are determined according to the classical data. The first formula is:

[0012] ψ=S·|ψ>;

[0013] Where |ψ> represents the optimal quantum state, S represents the classical data transformation scalar, ψ represents the classical data corresponding to |ψ>, and the classical data transformation scalar S is:

[0014]

[0015] Where k represents the index of all non-zero elements in z, and the fluid control coefficient matrix A represents the interdependence between various physical quantities in the flow field.

[0016] As an optional implementation, in the first aspect of the invention, before determining the optimized quantum hardware design circuit based on the Hamiltonian, the method further includes:

[0017] The motion and interaction of the spacecraft with the surrounding fluid during flight are described by the system of linear equations Ax = b to be solved at each time step:

[0018] The linear equations are solved using quantum hardware to obtain the predicted solution x′, and the residual r = b - Ax′ is calculated.

[0019] Determine whether the norm of the residual r exceeds a preset residual threshold. If it does, update the Hamiltonian based on the residual r. Where I represents the identity matrix.

[0020] As an optional implementation, in the first aspect of the invention, the description of the motion and interaction laws of the fluid surrounding the aircraft during flight using the linear equation system Ax = b to be solved at each time step includes:

[0021] Establish a geometric model of the aircraft and a simulation computational domain surrounding the geometric model;

[0022] The information from the simulation computation domain and the aerodynamic performance analysis parameters are input into a pre-determined flow model, which refers to the equations that describe and predict the motion and interaction of the fluid around the aircraft during flight.

[0023] The flow model is spatially and implicitly discretized in time to obtain the aerodynamic performance evolution equation set Ax = b at each time step, where A is the fluid control coefficient matrix and is used to represent the interdependence between various physical quantities in the flow field, x is the flow field state vector at the target time and is used to represent the values ​​of the dynamic and thermodynamic state quantities of the flow field to be solved at the target time, and b is the dynamic source term vector and is used to represent the source terms determined by the known flow field state and boundary conditions.

[0024] As an optional implementation, in the first aspect of the present invention, determining the optimized quantum hardware design circuit based on the Hamiltonian includes:

[0025] Obtain a quantum hardware design circuit, and obtain a quantum state through the evolution of the quantum hardware design circuit;

[0026] The loss function is determined based on the Hamiltonian, and the logic gate parameters in the proposed quantum hardware circuit are optimized based on the loss function.

[0027] The proposed quantum hardware circuit is updated based on the optimized logic gate parameters to obtain the optimized proposed quantum hardware circuit.

[0028] As an optional implementation, in the first aspect of the present invention, optimizing the logic gate parameters in the proposed quantum hardware circuit according to the loss function includes:

[0029] Using the quantum state as the target quantum state, the mixed tomography method is used to determine the quantum state;

[0030] The loss function is calculated as L = <ψ|H|ψ>, where ψ represents the quantum state corresponding to the proposed circuit of the quantum hardware;

[0031] The logic gate parameters are adjusted according to the gradient descent method to make the loss function converge.

[0032] As an optional implementation, in the first aspect of the present invention, the proposed quantum hardware circuitry includes an RY gate acting on each qubit and a CNOT gate acting on adjacent qubits, wherein the qubit with the smaller index in the CNOT gate is the control qubit, and the qubit with the larger index is the controlled qubit.

[0033] A second aspect of the present invention discloses an aerodynamic performance simulation device for aircraft, the device comprising:

[0034] The circuit optimization module is used to determine the optimized quantum hardware proposed circuit based on the Hamiltonian, and to obtain the optimal quantum state through the optimized quantum hardware proposed circuit. The Hamiltonian is used to describe the motion of the aircraft in the preset flow field.

[0035] A quantum state determination module is used to determine the optimal quantum state using a hybrid tomography method with the optimal quantum state as the target quantum state, and to determine the aerodynamic performance parameters of the aircraft in the preset flow field based on the optimal quantum state;

[0036] The hybrid tomography method refers to determining the symbol information contained in the target quantum state by using the direct product obtained from classical calculation, and determining the amplitude information contained in the target quantum state by taking the square root of the probability obtained from quantum state tomography sampling. The target quantum state is determined based on the symbol information and the amplitude information. The direct product is the direct product of the n data corresponding to the 0 state and the n data corresponding to the 1 state of the n qubits in the quantum hardware proposed circuit.

[0037] As an optional implementation, in a second aspect of the invention, the quantum state determination module determines the aerodynamic performance parameters of the aircraft in the preset flow field based on the optimal quantum state, including:

[0038] The optimal quantum state is converted into classical data according to the first formula, and the aerodynamic performance parameters of the aircraft in the preset flow field are determined according to the classical data. The first formula is:

[0039] ψ=S·|ψ>;

[0040] Where |ψ> represents the optimal quantum state, S represents the classical data transformation scalar, ψ represents the classical data corresponding to |ψ>, and the classical data transformation scalar S is:

[0041]

[0042] Where k represents the index of all non-zero elements in z, and the fluid control coefficient matrix A represents the interdependence between various physical quantities in the flow field.

[0043] As an optional implementation, in a second aspect of the invention, before the circuit optimization module determines the optimized quantum hardware proposed circuit based on the Hamiltonian, the device further includes:

[0044] The description module is used to describe the motion and interaction of the spacecraft with the fluid around it during flight using the linear equation system Ax = b to be solved at each time step; the solution module is used to solve the linear equation system using quantum hardware to obtain the predicted solution x′ and calculate the residual r = b - Ax′.

[0045] The update module determines whether the norm of the residual r exceeds a preset residual threshold. If it does, the Hamiltonian is updated based on the residual r. Where I represents the identity matrix.

[0046] As an optional implementation, in a second aspect of the invention, the description module uses a system of linear equations Ax = b to be solved at each moment to describe the motion and interaction of the aircraft with the fluid surrounding it during flight, including:

[0047] Establish a geometric model of the aircraft and a simulation computational domain surrounding the geometric model;

[0048] The information from the simulation computational domain and the aerodynamic performance analysis parameters are input into a pre-determined flow model, which refers to the equations that describe and predict the motion and interaction of the fluid around the aircraft during flight.

[0049] The flow model is spatially and implicitly discretized in time to obtain the aerodynamic performance evolution equation set Ax = b at each time step, where A is the fluid control coefficient matrix and is used to represent the interdependence between various physical quantities in the flow field, x is the flow field state vector at the target time and is used to represent the values ​​of the dynamic and thermodynamic state quantities of the flow field to be solved at the target time, and b is the dynamic source term vector and is used to represent the source terms determined by the known flow field state and boundary conditions.

[0050] As an optional implementation, in a second aspect of the invention, the circuit optimization module determines the optimized quantum hardware proposed circuit based on the Hamiltonian, including:

[0051] Obtain a quantum hardware design circuit, and obtain a quantum state through the evolution of the quantum hardware design circuit;

[0052] The loss function is determined based on the Hamiltonian, and the logic gate parameters in the proposed quantum hardware circuit are optimized based on the loss function.

[0053] The proposed quantum hardware circuit is updated based on the optimized logic gate parameters to obtain the optimized proposed quantum hardware circuit.

[0054] As an optional implementation, in a second aspect of the invention, the circuit optimization module optimizes the logic gate parameters in the proposed quantum hardware circuit according to the loss function, including:

[0055] Using the quantum state as the target quantum state, the mixed tomography method is used to determine the quantum state;

[0056] The loss function is calculated as L = <ψ|H|ψ>, where ψ represents the quantum state corresponding to the proposed circuit of the quantum hardware;

[0057] The logic gate parameters are adjusted according to the gradient descent method to make the loss function converge.

[0058] As an optional implementation, in a second aspect of the invention, the proposed quantum hardware circuitry includes an RY gate acting on each qubit and a CNOT gate acting on adjacent qubits, wherein the qubit with the smaller index is the control qubit and the qubit with the larger index is the controlled qubit.

[0059] A third aspect of the present invention discloses another aerodynamic performance simulation device for aircraft, the device comprising:

[0060] Memory containing executable program code;

[0061] A processor coupled to the memory;

[0062] The processor calls the executable program code stored in the memory to execute the aircraft aerodynamic performance simulation method disclosed in the first aspect of the present invention.

[0063] The fourth aspect of the present invention discloses a computer storage medium storing computer instructions, which, when invoked, are used to execute the aircraft aerodynamic performance simulation method disclosed in the first aspect of the present invention.

[0064] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0065] The number of quantum state replicas required by the hybrid tomography method is only related to the sampling accuracy and not to the number of qubits, which significantly saves quantum computing resources and reduces the hardware cost of aircraft aerodynamic performance simulation. On the other hand, the reconstruction of quantum states by the hybrid tomography method does not rely on the Hadamard Test circuit, making aircraft aerodynamic performance simulation applicable to more complex environments. Attached Figure Description

[0066] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0067] Figure 1 This is a flowchart illustrating a method for simulating the aerodynamic performance of an aircraft, as disclosed in an embodiment of the present invention.

[0068] Figure 2 This is a schematic diagram of the quantum linear solver circuit disclosed in an embodiment of the present invention;

[0069] Figure 3 This is a schematic diagram of the structure of an aircraft aerodynamic performance simulation device disclosed in an embodiment of the present invention;

[0070] Figure 4 This is a schematic diagram of another aircraft aerodynamic performance simulation device disclosed in an embodiment of the present invention;

[0071] Figure 5 This is a schematic diagram of the structure of another aircraft aerodynamic performance simulation device disclosed in an embodiment of the present invention;

[0072] Figure 6 This is a simulation result of 2D incompressible Poisson flow using the method provided in this invention. Detailed Implementation

[0073] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0074] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or end that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or ends.

[0075] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0076] This invention discloses a method and apparatus for simulating the aerodynamic performance of aircraft, which can significantly save quantum computing resources and reduce the hardware cost of aircraft aerodynamic performance simulation; moreover, it does not rely on the Hadamard Test circuit, enabling the aircraft aerodynamic performance simulation to be applicable to more complex environments. Detailed descriptions follow.

[0077] Example 1

[0078] Please see Figure 1 , Figure 1 This is a flowchart illustrating a method for simulating the aerodynamic performance of an aircraft, as disclosed in an embodiment of the present invention. Figure 1 The described aerodynamic performance simulation method for aircraft can be applied to the aerodynamic performance simulation of fixed-wing aircraft as well as rotary-wing aircraft; the embodiments of this invention are not limited thereto. Figure 1 As shown, the aerodynamic performance simulation method for this aircraft may include the following operations:

[0079] 101. Determine the optimized quantum hardware circuit based on the Hamiltonian, and obtain the optimal quantum state through the optimized quantum hardware circuit. The Hamiltonian is used to describe the motion of the aircraft in the preset flow field.

[0080] 102. Using the optimal quantum state as the target quantum state, determine the optimal quantum state using the hybrid tomography method, and determine the aerodynamic performance parameters of the aircraft in the preset flow field based on the optimal quantum state;

[0081] Among them, the hybrid tomography method refers to determining the symbol information contained in the target quantum state by the direct product obtained by classical calculation, and determining the amplitude information contained in the target quantum state by taking the square root of the probability obtained by quantum state tomography sampling. The target quantum state is determined based on the symbol information and amplitude information. The direct product is the direct product of the n data corresponding to the 0 state and the n data corresponding to the 1 state of the n qubits in the proposed circuit of quantum hardware.

[0082] In this embodiment, the aerodynamic performance parameters include the fluid physical properties and boundary conditions used in the simulation. The boundary conditions include pre-defined physical quantities that can affect aerodynamic performance, corresponding to the boundary of the aerodynamic performance simulation calculation domain of the aircraft.

[0083] In this embodiment, boundary conditions refer to the constraints and physical quantities applied to the boundary of the simulation calculation domain of the aircraft's aerodynamic performance, used to simulate the motion of the fluid around the aircraft, such as velocity inlet, pressure outlet, symmetry plane, and wall.

[0084] In this embodiment, fluid physical properties refer to parameters that determine the flow behavior of the fluid and the aerodynamic distribution on the surface of the aircraft, such as air density, viscosity, temperature, and pressure.

[0085] Figure 2 This is a schematic diagram of a quantum linear solver circuit. RY in the diagram represents the Y-axis rotation gate, and θ... i The rotation angle parameter represents the i-th qubit.

[0086] The symbolic information contained in the target quantum state is obtained in the following way:

[0087] The quantum state after Y-axis rotating gate processing for:

[0088]

[0089] The sign of the target quantum state is determined using the following formula:

[0090]

[0091] Where sgn() represents the symbol extraction function, n represents the number of qubits, i represents the index of the qubit, and θ represents the rotation angle parameter. U represents the tensor product symbol. RY () represents a quantum circuit composed of multiple Y-axis rotation gates, RY() represents a Y-axis rotation gate acting on a single qubit, θ i The rotation angle parameter representing the i-th qubit, |0 i > represents the ground state |0>, |1> of the i-th qubit. i > represents the excited state of the i-th qubit |1>.

[0092] As can be seen, the number of quantum state replicas required by the hybrid tomography method in this embodiment is only related to the sampling accuracy and not to the number of qubits, which significantly saves quantum computing resources and reduces the hardware cost of aircraft aerodynamic performance simulation. On the other hand, the reconstruction of quantum states by the hybrid tomography method does not rely on the Hadamard Test circuit, making aircraft aerodynamic performance simulation applicable to more complex environments.

[0093] In an optional embodiment, determining the aerodynamic performance parameters of the aircraft in a preset flow field based on the optimal quantum state includes:

[0094] The optimal quantum state is converted into classical data according to the first formula, and the aerodynamic performance parameters of the aircraft in the preset flow field are determined according to the classical data. The first formula is:

[0095] ψ=S·|ψ>;

[0096] Where |ψ> represents the optimal quantum state, S represents the classical data transformation scalar, ψ represents the classical data corresponding to |ψ>, and the classical data transformation scalar S is:

[0097]

[0098] Where k represents the index of all non-zero elements in z, and the fluid control coefficient matrix A represents the interdependence between various physical quantities in the flow field.

[0099] As can be seen, this embodiment reduces the influence of quantum noise by converting the optimal quantum state into classical data and then using classical data to convert scalars, thereby reducing the impact of quantum noise on the final result and making the determination of the aerodynamic performance parameters of the aircraft more accurate and reliable.

[0100] In another alternative embodiment, before determining the optimized quantum hardware circuitry based on the Hamiltonian, the method further includes:

[0101] The linear equation system Ax = b, which is to be solved at each time step, describes the motion and interaction of the aircraft with the fluid around it during flight.

[0102] The linear equations are solved using quantum hardware to obtain the predicted solution x, and the residual r = b - Ax′ is calculated.

[0103] Determine if the norm of the residual r exceeds a preset residual threshold. If it does, update the Hamiltonian based on the residual r. Where I represents the identity matrix.

[0104] In this embodiment, the initial value of the Hamiltonian is

[0105] Where I represents the identity matrix.

[0106] As can be seen, this embodiment optimizes the accuracy of the quantum state by calculating the residual to update the Hamiltonian, and ensures that the quantum state is closer to the true solution through iterative optimization, which significantly improves the accuracy and reliability of the aerodynamic performance simulation of the aircraft.

[0107] In another alternative embodiment, the linear equations Ax = b to be solved at each time step describe the motion and interaction of the aircraft with the fluid surrounding it during flight, including:

[0108] Establish the geometric model of the aircraft and the simulation computation domain surrounding the geometric model;

[0109] The information from the simulation computational domain and the aerodynamic performance analysis parameters are input into a pre-determined flow model. The flow model refers to the equations that describe and predict the motion and interaction of the fluid around the aircraft during flight.

[0110] The flow model is spatially and implicitly discretized in time to obtain the aerodynamic performance evolution equation set Ax = b at each time step, where A is the fluid control coefficient matrix and is used to represent the interdependence between various physical quantities in the flow field, x is the flow field state vector at the target time and is used to represent the values ​​of the dynamic and thermodynamic state quantities of the flow field to be solved at the target time, and b is the dynamic source term vector and is used to represent the source terms determined by the known flow field state and boundary conditions.

[0111] In this embodiment, tools such as SolidWorks, CATIA, and AutoCAD can be used to create the geometric model of the aircraft; this embodiment of the invention does not impose any limitations.

[0112] In this embodiment, the flow model can be an incompressible flow model, a compressible flow model, a turbulence model, etc., and the present invention does not limit it.

[0113] As can be seen, this embodiment establishes a geometric model of the aircraft and a simulation domain surrounding the model. Information from the simulation domain and aerodynamic performance analysis parameters are input into a pre-defined flow model. The flow model is then spatially and temporally discretized to obtain a set of aerodynamic performance evolution equations for each time step, thereby accurately describing the motion and interaction of the fluid surrounding the aircraft during flight. This method improves simulation accuracy, provides more precise initial conditions for subsequent quantum computing, and enhances the overall accuracy and reliability of the aircraft's aerodynamic performance simulation.

[0114] In yet another alternative embodiment, determining the optimized quantum hardware design based on the Hamiltonian includes:

[0115] Obtain the proposed circuit of quantum hardware, and obtain the quantum state through the evolution of the proposed circuit of quantum hardware;

[0116] The loss function is determined based on the Hamiltonian, and the logic gate parameters in the proposed quantum hardware circuit are optimized based on the loss function.

[0117] The proposed quantum hardware circuit is updated based on the optimized logic gate parameters to obtain the optimized proposed quantum hardware circuit.

[0118] As can be seen, this embodiment significantly improves the accuracy and stability of quantum states by optimizing the logic gate parameters and quantum hardware simulation circuit, thereby enhancing the accuracy and reliability of aircraft aerodynamic performance simulation.

[0119] In yet another alternative embodiment, the logic gate parameters in the proposed quantum hardware circuitry are optimized according to a loss function, including:

[0120] Using mixed tomography as the target quantum state, the quantum state is determined.

[0121] The loss function is calculated as L = <ψ′|H|ψ′>, where ψ′ represents the quantum state corresponding to the proposed circuit in the quantum hardware.

[0122] Adjust the logic gate parameters using gradient descent to make the loss function converge.

[0123] As can be seen, this embodiment optimizes the logic gate parameters in the quantum hardware simulation circuit, ensuring that the optimization process of the quantum state is more accurate and efficient, thereby further improving the overall accuracy and reliability of the aerodynamic performance simulation of the aircraft.

[0124] In another alternative embodiment, the proposed quantum hardware circuitry includes an RY gate acting on each qubit and a CNOT gate acting on adjacent qubits, wherein the qubit with the smaller index is the control qubit and the qubit with the larger index is the controlled qubit.

[0125] As can be seen, the design of the proposed circuit for quantum hardware in this embodiment simplifies the circuit structure, improves the stability and efficiency of the quantum computing process, and thus further enhances the accuracy and reliability of the aerodynamic performance simulation of the aircraft.

[0126] Example 2

[0127] Please see Figure 3 , Figure 3 This is a schematic diagram of the structure of an aircraft aerodynamic performance simulation device disclosed in an embodiment of the present invention. Figure 3 The described aerodynamic performance simulation device for aircraft can be applied to aerodynamic performance simulation scenarios for fixed-wing aircraft as well as rotary-wing aircraft; the embodiments of this invention are not limited thereto. Figure 3 As shown, the aerodynamic performance simulation device for the aircraft includes:

[0128] The circuit optimization module 301 is used to determine the optimized quantum hardware proposed circuit based on the Hamiltonian, and to obtain the optimal quantum state through the optimized quantum hardware proposed circuit. The Hamiltonian is used to describe the motion of the aircraft in the preset flow field.

[0129] The quantum state determination module 302 is used to determine the optimal quantum state using the hybrid tomography method with the optimal quantum state as the target quantum state, and to determine the aerodynamic performance parameters of the aircraft in the preset flow field based on the optimal quantum state;

[0130] Among them, the hybrid tomography method refers to determining the symbol information contained in the target quantum state by the direct product obtained by classical calculation, and determining the amplitude information contained in the target quantum state by taking the square root of the probability obtained by quantum state tomography sampling. The target quantum state is determined based on the symbol information and amplitude information. The direct product is the direct product of the n data corresponding to the 0 state and the n data corresponding to the 1 state of the n qubits in the proposed circuit of quantum hardware.

[0131] As can be seen, the number of quantum state replicas required by the hybrid tomography method in this embodiment is only related to the sampling accuracy and not to the number of qubits, which significantly saves quantum computing resources and reduces the hardware cost of aircraft aerodynamic performance simulation. On the other hand, the reconstruction of quantum states by the hybrid tomography method does not rely on the Hadamard Test circuit, making aircraft aerodynamic performance simulation applicable to more complex environments.

[0132] In one embodiment, the quantum state determination module 302 determines the aerodynamic performance parameters of the aircraft in a preset flow field based on the optimal quantum state, including:

[0133] The optimal quantum state is converted into classical data according to the first formula, and the aerodynamic performance parameters of the aircraft in the preset flow field are determined according to the classical data. The first formula is:

[0134] ψ=S·|ψ>;

[0135] Where |ψ> represents the optimal quantum state, S represents the classical data transformation scalar, ψ represents the classical data corresponding to |ψ>, and the classical data transformation scalar S is:

[0136]

[0137] Where k represents the index of all non-zero elements in z, and the fluid control coefficient matrix A represents the interdependence between various physical quantities in the flow field.

[0138] As can be seen, this embodiment reduces the influence of quantum noise by converting the optimal quantum state into classical data and then using classical data to convert scalars, thereby reducing the impact of quantum noise on the final result and making the determination of the aerodynamic performance parameters of the aircraft more accurate and reliable.

[0139] In another embodiment, such as Figure 4 As shown, before the circuit optimization module 301 determines the optimized quantum hardware proposed circuit based on the Hamiltonian, the device further includes:

[0140] Description module 303 is used to describe the motion and interaction of the aircraft with the fluid around it during flight using the linear equation system Ax = b to be solved at each time step;

[0141] The solution module 304 is used to solve the linear equation system using quantum hardware to obtain the predicted solution x and calculate the residual r = b - Ax;

[0142] The update module 305 determines whether the norm of the residual r exceeds a preset residual threshold. If it does, the Hamiltonian is updated based on the residual r. Where I represents the identity matrix.

[0143] As can be seen, this embodiment optimizes the accuracy of the quantum state by calculating the residual to update the Hamiltonian, and ensures that the quantum state is closer to the true solution through iterative optimization, which significantly improves the accuracy and reliability of the aerodynamic performance simulation of the aircraft.

[0144] In yet another embodiment, the description module 303 uses the linear equation system Ax = b to be solved at each time step to describe the motion and interaction of the aircraft with the fluid surrounding it during flight, including:

[0145] Establish the geometric model of the aircraft and the simulation computation domain surrounding the geometric model;

[0146] The information from the simulation computational domain and the aerodynamic performance analysis parameters are input into a pre-determined flow model. The flow model refers to the equations that describe and predict the motion and interaction of the fluid around the aircraft during flight.

[0147] The flow model is spatially and implicitly discretized in time to obtain the aerodynamic performance evolution equation set Ax = b at each time step, where A is the fluid control coefficient matrix and is used to represent the interdependence between various physical quantities in the flow field, x is the flow field state vector at the target time and is used to represent the values ​​of the dynamic and thermodynamic state quantities of the flow field to be solved at the target time, and b is the dynamic source term vector and is used to represent the source terms determined by the known flow field state and boundary conditions.

[0148] As can be seen, this embodiment establishes a geometric model of the aircraft and a simulation domain surrounding the model. Information from the simulation domain and aerodynamic performance analysis parameters are input into a pre-defined flow model. The flow model is then spatially and temporally discretized to obtain a set of aerodynamic performance evolution equations for each time step, thereby accurately describing the motion and interaction of the fluid surrounding the aircraft during flight. This method improves simulation accuracy, provides more precise initial conditions for subsequent quantum computing, and enhances the overall accuracy and reliability of the aircraft's aerodynamic performance simulation.

[0149] In yet another embodiment, the circuit optimization module 301 determines the optimized quantum hardware proposed circuit based on the Hamiltonian, including:

[0150] Obtain the proposed circuit of quantum hardware, and obtain the quantum state through the evolution of the proposed circuit of quantum hardware;

[0151] The loss function is determined based on the Hamiltonian, and the logic gate parameters in the proposed quantum hardware circuit are optimized based on the loss function.

[0152] The proposed quantum hardware circuit is updated based on the optimized logic gate parameters to obtain the optimized proposed quantum hardware circuit.

[0153] As can be seen, this embodiment significantly improves the accuracy and stability of quantum states by optimizing the logic gate parameters and quantum hardware simulation circuit, thereby enhancing the accuracy and reliability of aircraft aerodynamic performance simulation.

[0154] In yet another embodiment, the circuit optimization module 301 optimizes the logic gate parameters in the proposed circuit of the quantum hardware according to the loss function, including:

[0155] Using mixed tomography as the target quantum state, the quantum state is determined.

[0156] The loss function is calculated as L = <ψ′|H|ψ′>, where ψ′ represents the quantum state corresponding to the proposed circuit in the quantum hardware.

[0157] Adjust the logic gate parameters using gradient descent to make the loss function converge.

[0158] As can be seen, this embodiment optimizes the logic gate parameters in the quantum hardware simulation circuit, ensuring that the optimization process of the quantum state is more accurate and efficient, thereby further improving the overall accuracy and reliability of the aerodynamic performance simulation of the aircraft.

[0159] In another embodiment, the proposed circuitry of the quantum hardware includes an RY gate acting on each qubit and a CNOT gate acting on adjacent qubits, wherein the qubit with the smaller index in the CNOT gate is the control qubit, and the qubit with the larger index is the controlled qubit.

[0160] As can be seen, the design of the proposed circuit for quantum hardware in this embodiment simplifies the circuit structure, improves the stability and efficiency of the quantum computing process, and thus further enhances the accuracy and reliability of the aerodynamic performance simulation of the aircraft.

[0161] Example 3

[0162] Please see Figure 5 , Figure 5 This is a schematic diagram of the structure of another aircraft aerodynamic performance simulation device disclosed in an embodiment of the present invention. (See diagram below.) Figure 5As shown, the aerodynamic performance simulation device for aircraft may include:

[0163] Memory 501 storing executable program code;

[0164] Processor 502 coupled to memory 501;

[0165] The processor 502 calls the executable program code stored in the memory 501 to execute the steps in the aircraft aerodynamic performance simulation method described in Embodiment 1 or Embodiment 2 of the present invention.

[0166] Example 4

[0167] This invention discloses a computer storage medium storing computer instructions, which, when invoked, are used to execute the steps in the aircraft aerodynamic performance simulation method described in Embodiment 1 or Embodiment 2 of this invention.

[0168] Example 5

[0169] This invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to perform the steps in the aircraft aerodynamic performance simulation method described in Embodiment 1 or Embodiment 2.

[0170] Figure 6 The results of the 2D incompressible Poisson's flow simulation on the Origin Wukong superconducting quantum computer, presented by the method of this invention, are shown. Figure 6 The black line represents the theoretical solution to the linear equations constructed during the aerodynamic performance simulation of the aircraft; the blue diamonds indicate the solutions obtained on the Wukong computer; and the orange squares show the absolute value of the relative error between the theoretical solution and the quantum solution. Figure 6 It can be seen that the absolute value of the error between the quantum solution obtained by the method provided by the present invention and the theoretical solution is within 0.2.

[0171] The device embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0172] Through the detailed description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-Erasable Programmable Read-Only Memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0173] Finally, it should be noted that the aerodynamic performance simulation method and apparatus for aircraft disclosed in the embodiments of the present invention are merely preferred embodiments of the present invention and are only used to illustrate the technical solutions of the present invention, not to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for simulating the aerodynamic performance of an aircraft, characterized in that, The method includes: Obtain a quantum hardware design circuit, and obtain a quantum state through the evolution of the quantum hardware design circuit; The loss function is determined based on the Hamiltonian, and the logic gate parameters in the proposed quantum hardware circuit are optimized based on the loss function. The proposed quantum hardware circuit is updated based on the optimized logic gate parameters to obtain the optimized proposed quantum hardware circuit. The optimal quantum state is obtained by designing the circuit using optimized quantum hardware, and the Hamiltonian is used to describe the motion of the aircraft in the preset flow field. Using the optimal quantum state as the target quantum state, the target quantum state is reconstructed using a hybrid tomography method, and the aerodynamic performance parameters of the aircraft in the preset flow field are determined based on the reconstructed target quantum state. The hybrid tomography method refers to determining the symbol information contained in the target quantum state by using the direct product obtained from classical calculation, and determining the amplitude information contained in the target quantum state by taking the square root of the probability obtained from quantum state tomography sampling. The target quantum state is reconstructed based on the symbol information and the amplitude information. The direct product is the direct product of the n data corresponding to the 0 state and the n data corresponding to the 1 state of the n qubits in the proposed quantum hardware circuit.

2. The aerodynamic performance simulation method for aircraft according to claim 1, characterized in that, The step of determining the aerodynamic performance parameters of the aircraft in the preset flow field based on the optimal quantum state includes: The optimal quantum state is converted into classical data according to the first formula, and the aerodynamic performance parameters of the aircraft in the preset flow field are determined according to the classical data. The first formula is: ； in, The optimal quantum state is represented by S, which represents the classical data transformation scalar. represent The corresponding classical data, the classical data transformed into a scalar S is: ； Where k represents the index of all non-zero elements in z, and the fluid control coefficient matrix A represents the interdependence between various physical quantities in the flow field.

3. The aerodynamic performance simulation method for aircraft according to claim 1, characterized in that, Before determining the optimized quantum hardware circuitry based on the Hamiltonian, the method further includes: Using the system of linear equations to be solved at each time step Describe the motion and interaction patterns of the fluid surrounding the aircraft during flight; The predicted solution is obtained by solving the linear equations using quantum hardware to simulate circuitry. And calculate the residuals ; Determine whether the norm of the residual r exceeds a preset residual threshold. If it does, update the Hamiltonian based on the residual r. , where I represents the identity matrix; Where A is the fluid control coefficient matrix and is used to represent the interdependence between various physical quantities in the flow field, x is the flow field state vector at the target time and is used to represent the values ​​of the dynamic and thermodynamic state quantities of the flow field to be solved at the target time, and b is the dynamic source term vector and is used to represent the source terms determined by the known flow field state and boundary conditions.

4. The aerodynamic performance simulation method for aircraft according to claim 3, characterized in that, The system of linear equations to be solved at each time step Describe the motion and interaction patterns of the fluid surrounding the aircraft during flight, including: Establish a geometric model of the aircraft and a simulation computational domain surrounding the geometric model; The information from the simulation computational domain and the aerodynamic performance analysis parameters are input into a pre-determined flow model, which refers to the equations that describe and predict the motion and interaction of the fluid around the aircraft during flight. The flow model is spatially and implicitly discretized in time to obtain the aerodynamic performance evolution equations at each time step. .

5. The aerodynamic performance simulation method for aircraft according to claim 1, characterized in that, The optimization of the logic gate parameters in the proposed quantum hardware circuit based on the loss function includes: Using the quantum state as the target quantum state, the hybrid tomography method is used to reconstruct the target quantum state; The loss function is calculated as follows: ,in This represents the reconstructed target quantum state corresponding to the proposed circuitry of the quantum hardware. The logic gate parameters are adjusted according to the gradient descent method to make the loss function converge.

6. The aerodynamic performance simulation method for aircraft according to claim 1, characterized in that, The proposed quantum hardware circuitry includes an RY gate acting on each qubit and a CNOT gate acting on adjacent qubits. The CNOT gate includes a control bit and a controlled bit, wherein the qubit index of the control bit is smaller than the qubit index of the controlled bit.

7. An aerodynamic performance simulation device for aircraft, characterized in that, The device includes: The circuit optimization module is used to acquire a proposed quantum hardware circuit and to obtain a quantum state through the evolution of the proposed quantum hardware circuit; to determine a loss function based on the Hamiltonian and to optimize the logic gate parameters in the proposed quantum hardware circuit based on the loss function; to update the proposed quantum hardware circuit based on the optimized logic gate parameters to obtain an optimized proposed quantum hardware circuit and to obtain an optimal quantum state through the optimized proposed quantum hardware circuit, wherein the Hamiltonian is used to describe the motion of the aircraft in a preset flow field; A quantum state determination module is used to reconstruct the target quantum state using a hybrid tomography method with the optimal quantum state as the target quantum state, and to determine the aerodynamic performance parameters of the aircraft in the preset flow field based on the reconstructed target quantum state. The hybrid tomography method refers to determining the symbol information contained in the target quantum state by using the straight product obtained from classical calculation, and determining the amplitude information contained in the target quantum state by taking the square root of the probability obtained from quantum state tomography sampling. The target quantum state is then reconstructed based on the symbol information and the amplitude information. The straight product is the symbol information in the proposed circuit of the quantum hardware. The direct product of n data points corresponding to the 0 state and n data points corresponding to the 1 state of a qubit.

8. An aerodynamic performance simulation device for aircraft, characterized in that, The device includes: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the aircraft aerodynamic performance simulation method as described in any one of claims 1-6.

9. A computer storage medium, characterized in that, The computer storage medium stores computer instructions, which, when invoked, are used to execute the aircraft aerodynamic performance simulation method as described in any one of claims 1-6.

Images