An aircraft optimal trajectory screening method and system based on adjustable phase quantum search

Abstract

The application relates to the field of low-altitude safety technology and provides an aircraft optimal trajectory screening method and system based on an adjustable phase quantum search. A candidate trajectory is generated according to flight task constraints, and a generation cost is calculated, a high-quality solution is identified based on a cost threshold value, an adjustment search phase is based on, a balanced quantum superposition state of an encoded trajectory index is iteratively searched and measured by using a Grover quantum search algorithm to obtain an output index, the index is mapped to a trajectory, and an optimized trajectory is obtained through classical local optimization, a phase parameter and a cost threshold value are adaptively adjusted based on performance evaluation and repeated execution until an optimal trajectory is determined by meeting an end condition. The adjustable phase mechanism is introduced to realize online reconfiguration of the strategy, a feedback closed loop is established to realize adaptive optimization of the parameters, and the real-time requirements of urban air traffic control, unmanned aerial vehicle clusters and other scenes are met.

Description

Technical Field

[0001] This application relates to the field of low-altitude safety technology, and more specifically, to a method and system for selecting the optimal trajectory of an aircraft based on tunable phase quantum search. Background Technology

[0002] With the rapid development of the low-altitude economy, the limitations of traditional aircraft trajectory planning methods have become increasingly apparent. The search time of classical algorithms explodes exponentially with increasing degrees of freedom, leading to increased solution complexity and making it impossible to meet the real-time replanning requirements in dynamic environments. For example, the A* algorithm has a vast number of feasible solutions in high-dimensional continuous spaces, resulting in excessively long planning times for a safe trajectory in areas with dense obstacles or complex airspace. Simultaneously, solution uncertainty is also a significant factor limiting trajectory generation. For instance, while the RRT algorithm based on random sampling can quickly find feasible solutions, it struggles to guarantee global optimality, potentially resulting in circuitous, non-smooth, and energy-intensive trajectories—unacceptable for long-endurance or fuel-constrained UAVs. The computational limitations of classical computing force computing systems to compromise between computational speed and solution optimality.

[0003] Quantum computing, as the most promising direction for breakthroughs in computing power at present, offers the theoretical possibility of achieving exponentially accelerated searches in giant combinatorial spaces due to its quantum parallelism. Grover's quantum search algorithm, proposed in 1996, is a typical representative of this potential. However, this algorithm has three inherent assumptions: the database size is a power of 2, fixed phase rotation, and the number of target solutions is known in advance. These ideal laboratory conditions are seriously out of touch with the unstructured, insufficient prior information, and stochastic flight planning scenarios in the real world, making it impossible to apply directly.

[0004] In today's rapidly developing low-altitude economy, intelligent trajectory planning systems for low-altitude unmanned aerial vehicles (UAVs) face increasingly severe computational pressure. Existing aircraft trajectory planning methods employ traditional classical computational paradigms, relying entirely on the serial and parallel processing capabilities of classical computers for core computations. They search for feasible or optimal solutions in the solution space through traversal, achieving high-quality results. However, when dealing with the dense, highly dynamic movements of low-altitude UAVs, they still struggle to quickly generate high-quality paths. Traditional aircraft trajectory prediction and planning mechanisms have the following limitations:

[0005] 1. Low solution efficiency, unable to meet real-time requirements. The computational complexity of classical algorithms is usually polynomial or even exponential with the problem size (such as the discretization precision of the state space, the number of obstacles). For example, the search time of the A* algorithm in a three-dimensional fine mesh will increase dramatically; although the RRT algorithm can find feasible solutions relatively quickly, the number of sampling points required to obtain a high-quality solution is huge, and the optimization process is slow. In scenarios requiring "second-level" or even "millisecond-level" response, such as drone obstacle avoidance or multi-drone collaborative planning in urban logistics, the computational latency of classical algorithms becomes an unacceptable bottleneck, forcing the system to compromise between "reducing planning accuracy" and "sacrificing response speed," seriously affecting the safety and efficiency of low-altitude flight.

[0006] 2. Low search flexibility. The traditional Grover algorithm uses a fixed rotation phase and a fixed search step size. When faced with problems with different proportions of target solutions, it cannot flexibly adjust the search strategy. When the proportion of target solutions is high, it is prone to oversearch; when the proportion is low, convergence is slow. Users cannot balance search speed and accuracy according to the urgency of the task. At the same time, the algorithm requires the solution space to be a power of 2, making it difficult to adapt to diverse practical task requirements.

[0007] 3. Weak feedback learning and adaptive capabilities. Existing classic planning systems are typically "one-off" systems. That is, the planning process terminates after generating a trajectory based on the current input. The system lacks a closed-loop feedback mechanism based on historical planning results and actual execution performance, and cannot build and utilize a knowledge base that links "trajectory library-performance-parameters". For example, it cannot identify which search strategy is more effective under a certain spatial structure, leading to wasted computing power and a failure to continuously improve planning efficiency.

[0008] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0009] The purpose of this application is to provide a method and system for selecting the optimal trajectory of an aircraft based on tunable phase quantum search. This method can solve the problems of low efficiency in searching for feasible solutions and inflexible coordination of computing resources in current low-altitude aircraft trajectory planning, and provide support for subsequent ground analysis.

[0010] In a first aspect, this application provides a method for selecting the optimal trajectory of an aircraft based on tunable phase quantum search, including:

[0011] Trajectory encoding steps: Based on flight mission constraints, generate several candidate trajectories and calculate their comprehensive cost value; based on a preset cost threshold, assign a Boolean tag to each trajectory to identify a high-quality solution; and generate an Oracle query table that maps the trajectory index of the candidate trajectories to the Boolean tags.

[0012] Quantum search steps: Construct a quantum Oracle operator based on the Boolean tag, and determine an adjustable phase parameter according to task requirements to construct a generalized diffusion operator; Iteratively execute quantum operations consisting of the Oracle operator and the generalized diffusion operator on the quantum superposition state encoded with the trajectory index to obtain an output index;

[0013] Optimization steps: Map the output index to the corresponding candidate trajectory, use the candidate trajectory as the initial solution to call the classical trajectory optimization algorithm for local fine-tuning and smoothing, and obtain the optimized trajectory for the current round; adjust the adjustable phase parameter and the cost threshold based on the performance evaluation of this quantum search; wherein, based on the adjusted adjustable phase parameter and the cost threshold, repeat the trajectory encoding step and the quantum search step, and when the preset termination condition is met, determine the currently obtained optimized trajectory as the optimal trajectory of the flight mission.

[0014] Furthermore, the flight mission constraints include the starting position, the ending position, airspace constraints, and the aircraft dynamics model; the airspace constraints include obstacles, no-fly zones, and terrain restrictions.

[0015] Furthermore, the comprehensive cost value includes calculating the multidimensional cost vector C(τi) of the candidate trajectory τi. i ):

[0016] ;

[0017] Where wL, wR, wF, wT∈[0,1] are weighting coefficients; R(τi) is the threat exposure cost; T(τi) is the flight time cost; L(τi) is the flight path cost; and F(τi) is the endurance consumption cost.

[0018] The step of assigning a Boolean label to each trajectory based on a preset cost threshold to identify high-quality solutions includes: defining a Boolean labeling function f(τi) to generate the determination result of the Boolean label; wherein:

[0019]

[0020] If C(τ) i )≤C th If f(τi) = 1, it is marked as a high-quality solution; otherwise, f(τi) = 0. th This is the preset cost threshold.

[0021] Preferably, generating the trajectory index that maps the candidate trajectories to the Boolean-tagged Oracle query table includes:

[0022] Based on the determination result of the Boolean flag, a one-to-one mapping relationship between the unique index of each candidate trajectory and its flag value is established to form the Oracle query table;

[0023] The Oracle lookup table and the adjustable phase parameter are encapsulated into a quantum instruction package and sent to the quantum processor to construct the quantum Oracle operator.

[0024] Furthermore, a quantum Oracle operator is constructed based on the Boolean label, and an adjustable phase parameter is determined according to task requirements to construct a generalized diffusion operator, including:

[0025] An auxiliary qubit is introduced, and the trajectory index state is associated with the Boolean tag through a controlled gate network. A phase flip operation is applied to the state marked as a high-quality solution, and the inverse operation solution is calculated to clear the auxiliary qubit, thereby realizing the negative phase tagging of the ground state corresponding to the high-quality solution, and thus obtaining the quantum Oracle operator.

[0026] The construction of the generalized diffusion operator includes: constructing a reflection operator with respect to the average probability amplitude based on the adjustable phase parameter; the reflection intensity of the reflection operator is precisely controlled by the adjustable phase parameter, and when the adjustable phase parameter is a flat angle, the operator degenerates into the standard Grover diffusion operator, which serves as the generalized diffusion operator.

[0027] Preferably, the iterative execution of quantum operations consisting of the Oracle operator and the generalized diffusion operator on the quantum superposition state encoded with the trajectory index to obtain an output index includes:

[0028] Allocate n qubits as the master register on the quantum processor; where n = log2N N is the number of candidate trajectories;

[0029] The unitary operator UN is invoked to prepare a balanced superposition state containing all N candidate trajectory indices; the generalized diffusion operator and the Oracle operator are combined to form a single-iteration operator;

[0030] Repeat the iterative operator Q times, so that the probability amplitude corresponding to the trajectory index marked as a high-quality solution is coherently amplified;

[0031] The output index is obtained by measuring all the qubits of the main register and collapsing them with high probability.

[0032] Preferably, the output index is mapped to the corresponding candidate trajectory, and the classical trajectory optimization algorithm is called with the candidate trajectory as the initial solution for local fine-tuning and smoothing to obtain the optimized trajectory for the current round, including:

[0033] The output index is mapped back to the classic trajectory database to retrieve the trajectory parameters that uniquely correspond to the index of the candidate trajectory.

[0034] The trajectory parameters are dynamically and constrained using a high-precision classical simulator; the verified candidate trajectory is used as a high-quality initial solution, and a sequential quadratic programming algorithm is called for local fine-tuning and smoothing to generate the current round of optimized trajectory.

[0035] Preferably, adjusting the adjustable phase parameter and the cost threshold based on the performance evaluation of this quantum search includes:

[0036] A performance evaluation model is established to evaluate the performance of this search based on a weighted score of search time and solution quality; wherein, the solution quality is determined based on the percentile of the comprehensive cost of the current round of optimization trajectory in historical solutions;

[0037] If the solution quality is lower than a preset quality threshold and the current iteration count has not reached the upper limit, then the adjustable phase parameter is reduced by a first proportional coefficient.

[0038] If the search time exceeds a preset time threshold, the adjustable phase parameter is increased by a second proportional coefficient.

[0039] The proportion of trajectories marked as high-quality solutions in the total candidate trajectories during multiple rounds of statistical searches is calculated. If this proportion is consistently lower than a first proportion threshold, the cost threshold is increased to increase the number of search targets. If this proportion is consistently higher than a second proportion threshold, the cost threshold is decreased to focus on better solutions.

[0040] Secondly, this application provides a system for selecting the optimal trajectory of an aircraft based on tunable phase quantum search, including:

[0041] The trajectory encoding module is used to generate several candidate trajectories and calculate their comprehensive cost value according to flight mission constraints; assign a Boolean tag to each trajectory based on a preset cost threshold to identify a high-quality solution; and generate an Oracle query table that maps the trajectory index of the candidate trajectories to the Boolean tags.

[0042] The quantum search module is used to construct a quantum Oracle operator based on the Boolean tag and to determine an adjustable phase parameter according to task requirements to construct a generalized diffusion operator; iteratively executes quantum operations consisting of the Oracle operator and the generalized diffusion operator on the quantum superposition state encoded with the trajectory index to obtain an output index;

[0043] The optimization module maps the output index to the corresponding candidate trajectory, uses the candidate trajectory as the initial solution to call the classical trajectory optimization algorithm for local fine-tuning and smoothing, and obtains the optimized trajectory for the current round; it adjusts the adjustable phase parameter and the cost threshold based on the performance evaluation of this quantum search; wherein, based on the adjusted adjustable phase parameter and the cost threshold, the trajectory encoding step and the quantum search step are repeatedly executed, and when the preset termination condition is met, the currently obtained optimized trajectory is determined as the optimal trajectory of the flight mission.

[0044] Thirdly, this application provides an electronic device comprising: one or more processors, and a memory for storing one or more computer programs; the computer programs are configured to be executed by the one or more processors, the programs including steps for performing the aircraft optimal trajectory selection method based on tunable phase quantum search as described in the first aspect.

[0045] As can be seen from the above, the optimal trajectory selection method and system for aircraft based on tunable phase quantum search provided in this application, through the deep integration of classical-quantum hybrid computing architecture, tunable phase quantum search and closed-loop feedback adaptive optimization mechanism, brings performance improvement and intelligent evolution capability compared with the prior art, specifically reflected in the following aspects:

[0046] 1. A quantum-classical hybrid computing architecture was constructed, achieving quadratic-level acceleration in searching massive solution spaces and breaking through the real-time bottleneck of traditional planning. The system intelligently divides the computational tasks: classical computing units are responsible for high-fidelity environment modeling, trajectory generation, and complex cost evaluation, generating a candidate trajectory library of arbitrary size; quantum computing units act as coprocessors, dedicated to executing the core parallel search algorithm. By preparing arbitrary cardinality superposition states and running adjustable-phase Grover iterations, this architecture can reduce the query complexity of optimal solution search from the theoretical O(N) of classical algorithms to O(N1 / 2). This hybrid paradigm fully leverages the maturity of classical computing and the parallelism advantages of quantum computing, making it possible to perform global optimization on tens of thousands of candidate trajectories within millisecond to second-level time windows, meeting the stringent real-time requirements of scenarios such as urban air traffic control and drone swarms.

[0047] 2. By introducing a flexible and adjustable fixed rotation phase mechanism, the online reconfigurability and multi-task adaptiveness of the search strategy are achieved, solving the problem of rigidity in the application of traditional quantum algorithms. This invention is based on an adjustable rotation phase φ. The search strategy generation module can dynamically select the value of φ (e.g., 0.1π, 0.8π, or π / 3) online according to specific task requirements. Smaller phases tend towards high-success-rate, fine-grained searches, while larger phases tend towards faster convergence. This selection mechanism gives the core of a single quantum search algorithm strong policy flexibility. Users or upper-level systems can dynamically adjust the search behavior based on real-time trade-offs between speed and accuracy, thus efficiently handling various complex scenarios from task pre-planning to dynamic replanning with a unified framework, greatly enhancing the system's practicality and applicability.

[0048] 3. A parameter adaptive closed loop based on feedback learning is established, enabling continuous self-optimization of system performance and endowing the planning system with intelligent evolution capabilities. This invention constructs a complete "planning-evaluation-optimization" closed loop through a feedback learning module and an adaptive adjustment module. After each task execution, the system automatically analyzes the efficiency of the quantum search (such as the number of iterations and time consumption) and the actual performance of the output trajectory. Based on this data, the system can automatically adjust the key parameters of the next round of planning through a rule engine, such as dynamically optimizing the rotation phase φ, correcting the cost function weight w, and updating the threshold Cth. This capability allows the system to continuously learn from experience, adapting to different aircraft models, mission types, and even the noise characteristics of the quantum hardware itself, thereby achieving increasingly better intelligent performance and significantly reducing the manual costs of later maintenance and parameter tuning. Attached Figure Description

[0049] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 This is a flowchart illustrating the steps of the optimal trajectory selection method for aircraft based on tunable phase quantum search disclosed in an embodiment of the present invention.

[0051] Figure 2 This is a schematic diagram illustrating the specific implementation process of the aircraft trajectory generation method disclosed in the embodiments of the present invention;

[0052] Figure 3 This is a schematic diagram of the structure of the optimal trajectory selection system for an aircraft based on tunable phase quantum search disclosed in an embodiment of the present invention;

[0053] Figure 4This is a schematic diagram of the framework of another aircraft optimal trajectory generation system disclosed in an embodiment of the present invention. Detailed Implementation

[0054] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which these embodiments belong; the terminology used herein and in the specification of the application is for the purpose of describing particular embodiments only and is not intended to limit these embodiments; the terms "comprising" and "having," and any variations thereof, in the specification of these embodiments and the foregoing drawings, are intended to cover non-exclusive inclusion. The terms "first," "second," etc., in the specification of these embodiments and the foregoing drawings are used to distinguish different objects, not to describe a particular order.

[0055] The implementation details of the technical solution in this embodiment are described below:

[0056] Firstly, this embodiment provides a method for selecting the optimal trajectory of an aircraft based on tunable phase quantum search, such as... Figure 1 As shown, the method includes:

[0057] S101, Trajectory Encoding Steps: Based on flight mission constraints, generate several candidate trajectories and calculate their comprehensive cost value; based on a preset cost threshold, assign a Boolean tag to each trajectory to identify a high-quality solution; and generate an Oracle query table that maps the trajectory index of the candidate trajectories to the Boolean tags.

[0058] Furthermore, the flight mission constraints include the starting position, the ending position, airspace constraints, and the aircraft dynamics model; the airspace constraints include obstacles, no-fly zones, and terrain restrictions.

[0059] Furthermore, the comprehensive cost value includes calculating the multidimensional cost vector C(τi) of the candidate trajectory τi. i ):

[0060] ;

[0061] Where wL, wR, wF, wT∈[0,1] are weighting coefficients; R(τi) is the threat exposure cost; T(τi) is the flight time cost; L(τi) is the flight path cost; and F(τi) is the endurance consumption cost.

[0062] Specifically, in this embodiment, step S101 is the initialization step, executed by the classical computing module, aiming to transform the continuous flight trajectory planning problem into a discrete combinatorial optimization problem that can be efficiently processed by subsequent quantum search algorithms. It specifically includes the following sub-steps:

[0063] (1.1) Candidate trajectory generation:

[0064] The velocity and position of the aircraft change over time. By performing Euler integrals on the moving physical quantities (such as velocity and position), the trajectory of the target can be obtained. Let the target be at position r and velocity v at time t, then the velocity and position of the target after Δt are:

[0065]

[0066] Where Δt is the time step. To introduce a physical feasibility constraint layer, we embed an acceleration constraint ac into the above traditional Euler integral. This constraint acceleration ac is not a fixed constant, but a dynamically adjusted function of the target aerodynamic parameters, control capabilities, and fuel consumption factors. This model can adaptively adjust based on the target type and flight phase to ensure that the generated trajectory is more physically plausible. Therefore, the above equation becomes:

[0067]

[0068] Where, ac = γ1· Φ_aerodynamic + γ2· Φ_control + γ3· Φ_fuel and Φ_aerodynamic are the aerodynamic constraint potential energy (based on the target aerodynamic parameters); Φ_control is the control capability constraint potential energy (based on the control surface response model); Φ_fuel is the fuel consumption constraint potential energy.

[0069] This mathematical model system extends the traditional, simple Euler integral into a modified model that senses changes in physical quantities, enabling it to more accurately simulate the dynamic evolution of a target over time in a real environment.

[0070] (1.2) Trajectory library generation:

[0071] Based on the start point S, end point T, and airspace constraints (such as obstacles and no-fly zones) input from the flight mission, N physically feasible candidate trajectories are generated in the state space using classical motion models and sampling algorithms (such as Fast Expanding Random Tree, RRT), forming an initial trajectory set. Where N is any positive integer actually generated by the algorithm, not limited to powers of 2. Each trajectory τi is defined by a series of discrete waypoint sequences.

[0072] (1.3) Multi-objective cost assessment and labeling:

[0073] For each candidate trajectory τi in the trajectory library, its multidimensional cost vector C(τi) is computed in parallel, including trajectory length L, threat exposure R, fuel consumption F, and flight time T. C(τi) is defined as a convex combination of the costs of each dimension.

[0074]

[0075] Where wL, wR, wF, wT∈[0,1] are weighting coefficients; R(τi) is the threat exposure cost; T(τi) is the flight time cost; L(τi) is the flight path cost; and F(τi) is the endurance consumption cost. Its specific representation is as follows:

[0076]

[0077]

[0078]

[0079]

[0080] Where, p k Represents a three-dimensional spatial position; ω j These are the weighting coefficients of threat source j; the function j( d It is defined as a monotonically decreasing function; α , β , γ This is a positive coefficient related to the aerodynamic efficiency of the aircraft and the specific impulse of the engine; g0=(0,0,g) T Here is the vector of gravitational acceleration; g is the standard gravitational acceleration. Let be the scalar value of the average airspeed planned on flight segment k.

[0081] Furthermore, in this embodiment, the step of assigning a Boolean label to each trajectory to identify a high-quality solution based on a preset cost threshold includes: defining a Boolean labeling function f(τi) to generate the determination result of the Boolean label; wherein:

[0082]

[0083] If C(τ) i )≤C th If f(τi) = 1, it is marked as a high-quality solution; otherwise, f(τi) = 0. th This is the preset cost threshold.

[0084] In this embodiment, a comprehensive cost threshold C is set based on the current task stage and performance requirements. th Based on this threshold, a Boolean flag function f(τ) is defined as described above. i ).like Then f(τ) i If )=1, it is marked as a "high-quality solution"; otherwise, it is 0, and the set size is M. This function defines the objective of the quantum search.

[0085] Preferably, generating an Oracle lookup table that maps the trajectory index of the candidate trajectory to the Boolean tag includes: establishing a one-to-one mapping relationship between the unique index of each candidate trajectory and its tag value based on the determination result of the Boolean tag, forming the Oracle lookup table; encapsulating the Oracle lookup table and the adjustable phase parameter into a quantum instruction package, and sending it to the quantum processor to construct the quantum Oracle operator.

[0086] Specifically, in this embodiment, the sub-steps further include (1.4) generating a quantum search strategy, (1.5) generating an Oracle query table, and (1.6) packaging quantum instructions.

[0087] (1.4) Quantum search strategy generation:

[0088] Based on task requirements, the system dynamically determines the key control parameter for invoking the quantum search algorithm—a fixed rotation phase φ (i.e., the adjustable phase parameter). Phase φ can be any angle within the range (0, π). In the first round of search, three typical strategy modes are provided: Mode A (Exact Search): A smaller φ (e.g., 0.1π) is selected to achieve a higher success rate, used for offline planning. Mode B (Fast Search): A larger φ (e.g., 0.8π) is selected to quickly obtain feasible solutions with fewer iterations, used for online replanning. Mode C (Robust Search): When there is no prior knowledge of the number of high-quality solutions M, the suggested φ = π / 3 is used to ensure a performance lower bound. After the first round of search, the adaptive adjustment module dynamically adjusts the search phase based on the search results.

[0089] (1.5) Generate Oracle query tables

[0090] Based on the determination result of the labeling function f(τi), an "Oracle lookup table" is generated that maps the trajectory index i to its label value f(τi). Thus, the continuous trajectory planning problem is compiled into: searching a database of size N for all entries that satisfy f(τi) = 1.

[0091] (1.6) Generate Oracle query tables

[0092] The lookup table and the phase parameter φ determined by the decision are encapsulated together and sent to the quantum processing unit (i.e., the quantum processor) through the classical-quantum communication interface.

[0093] S102, Quantum search step: Construct a quantum Oracle operator based on the Boolean label, and determine an adjustable phase parameter according to the task requirements to construct a generalized diffusion operator; Iteratively execute quantum operations composed of the Oracle operator and the generalized diffusion operator on the quantum superposition state that encodes the trajectory index to obtain an output index.

[0094] Specifically, in this embodiment, the core execution of the quantum search step is the acceleration engine step, which is run by the quantum search execution module on the quantum processor. It strictly follows the tunable phase quantum search algorithm to perform amplitude amplification of the high-quality trajectory index in parallel on the quantum superposition state.

[0095] Furthermore, a quantum Oracle operator is constructed based on the Boolean tag, and an adjustable phase parameter is determined according to the task requirements to construct a generalized diffusion operator, including: introducing an auxiliary qubit, associating the trajectory index state with the Boolean tag through a controlled gate network, applying a phase flip operation to the state marked as a high-quality solution, and performing the inverse operation solution calculation to clear the auxiliary qubit, thereby realizing the negative phase tagging of the ground state corresponding to the high-quality solution, and thus obtaining the quantum Oracle operator;

[0096] The construction of the generalized diffusion operator includes: constructing a reflection operator with respect to the average probability amplitude based on the adjustable phase parameter; the reflection intensity of the reflection operator is precisely controlled by the adjustable phase parameter, and when the adjustable phase parameter is a flat angle, the operator degenerates into the standard Grover diffusion operator, which serves as the generalized diffusion operator.

[0097] Preferably, the iterative execution of quantum operations consisting of the Oracle operator and the generalized diffusion operator on the quantum superposition state encoded with the trajectory index to obtain an output index includes:

[0098] Allocate n qubits as the master register on the quantum processor; where n = log2N N is the number of candidate trajectories;

[0099] The unitary operator UN is invoked to prepare a balanced superposition state containing all N candidate trajectory indices; the generalized diffusion operator and the Oracle operator are combined to form a single-iteration operator;

[0100] Repeat the iterative operator Q times, so that the probability amplitude corresponding to the trajectory index marked as a high-quality solution is coherently amplified;

[0101] The output index is obtained by measuring all the qubits of the main register and collapsing them with high probability.

[0102] Specifically, in this embodiment, the quantum search step can be implemented as the following sub-steps.

[0103] (2.1) Quantum state initialization:

[0104] On the quantum processor, allocate n = (log₂N) qubits as master registers for encoding trajectory indices; simultaneously, allocate at least one auxiliary qubit as a working register for oracle calculations. Initialize all qubits to their ground state. .

[0105] (2.2) Preparation of arbitrary superposition states:

[0106] Assign n= log2N First, apply the Hadamard gate H to all bits in the main register to obtain 2 qubits. n The superposition of ground states is then used to design the unitary operator U through operations such as controlled rotating gates. N Prepare a uniform superposition state comprising N trajectories:

[0107]

[0108] in, The binary quantum state representation of trajectory index i; unitary operator U N It is a series of recursive Hadamard gates and controlled rotation gates. By constructing different fits for odd / even numbers, it achieves the preparation of a balanced superposition state for any positive integer N, thus breaking through the traditional quantum search requirement that the database size must be N=2. n Restrictions.

[0109] (2.3) Configuring and executing Oracle:

[0110] Based on the received "Oracle lookup table", a quantum Oracle operator O is constructed. This operator achieves phase flipping of the marked high-quality trajectory state (f(i)=1) through the standard quantum circuit mode of "computation-marking-solution computation", specifically including:

[0111] A. Calculation: Introduce and initialize auxiliary qubits, and implement the transformation through a controlled gate network: Load the classic tag information into the auxiliary bits.

[0112] B. Marking: For auxiliary bits, set to |1 Apply a phase flip (Z-gate) to the state, and perform Phase flipping operation.

[0113] C. Solution calculation: Execute U f † (U) f (The inverse operation) clears the auxiliary bits in the working register. Through Oracle operations, the final result is: For all high-quality solutions, the ground state acquires a negative phase.

[0114] (2.4) Configuring and executing flexible diffusion operators:

[0115] Based on the received phase parameter φ, construct the generalized diffusion operator:

[0116]

[0117] The operator performs a "reflection" operation with respect to the average probability amplitude, the intensity of which is precisely controlled by the fixed phase parameter φ determined in step "(1.4) Quantum Search Strategy Generation". When φ = π, the operator degenerates into the standard Grover diffusion operator.

[0118] (2.5) Iterative Amplification and Measurement:

[0119] By combining the Oracle operator with the diffusion operator, a single complete iteration operator is formed:

[0120] ;

[0121] Repeatedly execute G φ The operator Q times (Q can be estimated using a formula or set as an empirical value) coherently amplifies the probability amplitude corresponding to the high-quality trajectory index. Finally, measurements are performed on all qubits in the main register, and the system will collapse with high probability to a specific trajectory index i. result .

[0122] S103, Optimization steps: Map the output index to the corresponding candidate trajectory, use the candidate trajectory as the initial solution to call the classical trajectory optimization algorithm for local fine-tuning and smoothing, and obtain the current round of optimized trajectory; adjust the adjustable phase parameter and the cost threshold based on the performance evaluation of this quantum search; wherein, based on the adjusted adjustable phase parameter and the cost threshold, repeat the trajectory encoding step and the quantum search step, and when the preset termination condition is met, determine the currently obtained optimized trajectory as the optimal trajectory of the flight mission.

[0123] Preferably, the output index is mapped to the corresponding candidate trajectory, and the classical trajectory optimization algorithm is called with the candidate trajectory as the initial solution for local fine-tuning and smoothing to obtain the optimized trajectory for the current round, including:

[0124] The output index is mapped back to the classic trajectory database to retrieve the trajectory parameters that uniquely correspond to the index of the candidate trajectory.

[0125] The trajectory parameters are dynamically and constrained using a high-precision classical simulator; the verified candidate trajectory is used as a high-quality initial solution, and a sequential quadratic programming algorithm is called for local fine-tuning and smoothing to generate the current round of optimized trajectory.

[0126] Preferably, adjusting the adjustable phase parameter and the cost threshold based on the performance evaluation of this quantum search includes:

[0127] A performance evaluation model is established to evaluate the performance of this search based on a weighted score of search time and solution quality; wherein, the solution quality is determined based on the percentile of the comprehensive cost of the current round of optimization trajectory in historical solutions;

[0128] If the solution quality is lower than a preset quality threshold and the current iteration count has not reached the upper limit, then the adjustable phase parameter is reduced by a first proportional coefficient.

[0129] If the search time exceeds a preset time threshold, the adjustable phase parameter is increased by a second proportional coefficient.

[0130] The proportion of trajectories marked as high-quality solutions in the total candidate trajectories during multiple rounds of statistical searches is calculated. If this proportion is consistently lower than a first proportion threshold, the cost threshold is increased to increase the number of search targets. If this proportion is consistently higher than a second proportion threshold, the cost threshold is decreased to focus on better solutions.

[0131] Specifically, in this embodiment, the optimization step is the closed-loop optimization step of the present invention, executed by the classical post-processing and optimization module, which is responsible for verifying and optimizing the quantum search results, and achieving self-improvement of system performance through feedback learning. It specifically includes the following sub-steps:

[0132] (3.1) Trajectory Decoding:

[0133] The index i obtained from quantum measurement result Mapping back to the specific trajectory τ in the classic trajectory database result The trajectory was dynamically and constrained using a high-precision classical simulator. Subsequently, τ... result As a high-quality initial solution, a classic trajectory optimization algorithm (such as sequential quadratic programming) is called for local fine-tuning and smoothing, outputting a final trajectory τ that can be directly flown. final .

[0134] (3.2) Trajectory visualization processing:

[0135] The generated trajectory is visualized in multiple dimensions, including: a) 3D spatial trajectory map: displaying the complete path of the trajectory in the 3D airspace, using different colors and transparency to distinguish trajectory segments and confidence levels. b) 2D planar projection map: generating orthogonal projection views of the XY, XZ, and YZ planes respectively to assist in spatial relationship analysis. c) Dynamic profile map: plotting curves of key parameters such as velocity, acceleration, and altitude over time. d) Parameter statistical report: automatically calculating and generating text reports containing indicators such as average speed, maximum rate of climb, and total distance.

[0136] (3.3) Feedback Learning

[0137] This step is crucial for the system to possess intelligent evolution capabilities. By establishing a performance evaluation model, the quantum search is quantitatively analyzed, and finally, based on the evaluation results, the key parameters for the next round of planning are dynamically adjusted. The performance evaluation model is a quantitative scoring mechanism used to evaluate the performance parameters of the solution (such as time consumption and solution quality), and its comprehensive score is:

[0138] η = α * (1 - Rank) + β * (1 / T search )

[0139] Among them, T search The total time spent on the quantum search serves as an indicator of search efficiency; Rank represents the percentile of this cost in historical solutions, serving as an indicator of solution quality; α and β are weights that can be adjusted according to actual task requirements.

[0140] (3.4) Adaptive parameter adjustment

[0141] Based on performance evaluation, a rule engine is used to dynamically adjust the phase φ and cost threshold Cth parameters for the next round. For example, if the current solution is not optimal, φ is decreased for a more refined search in the next round; if the search is too slow, φ is increased to speed it up. Specifically:

[0142] A. Phase φ Adjustment: If the quality Rank of the solution found this time is lower than the preset quality threshold and the actual number of iterations has not reached the upper limit, then the adjustable phase parameter is reduced by the first proportional coefficient, and the next φ... new = λ * φ old (λ<1, e.g., 0.9), to perform a more refined search;

[0143] If the search takes too long, exceeding the preset time threshold, then the adjustable phase parameter φ is increased by the second proportional coefficient. new = μ * φ old (μ>1, e.g., 1.1), to accelerate convergence.

[0144] B. Cost threshold C th Adjustment: Calculate the proportion of labeled solutions ρ = M / N in recent historical multi-round searches. If ρ remains too low (e.g., <5%), i.e., below the first proportion threshold, then appropriately increase C. th To increase the number of search targets; if ρ remains too high (e.g., >30%), then reduce C. th To focus on a better solution.

[0145] Finally, update the parameter φ. new and C th(new) Store in the strategy knowledge base for the next execution of "(1.4) Quantum Search Strategy Generation".

[0146] like Figure 2 The diagram illustrates the specific implementation process of the aircraft trajectory generation method in this embodiment. First, the kinematic equations of different motion models are solved based on the input motion parameters to generate the aircraft trajectory. Then, based on the mission start point, end point, and airspace constraints, algorithms such as the classic Quick Random Tree (RRT) are used to package the generated trajectories into a database containing N feasible candidate trajectories. Next, state cost calculation and labeling are performed for each trajectory τ. i Parallel computation of its multidimensional cost C(τ) i At the same time, a cost threshold C is set according to the task requirements. th Define a Boolean tag function f(τ) i If C(τ) i )≤C th Then f(τ) i If τ = 1, it is marked as a "high-quality solution"; then a search strategy is generated, selecting an appropriate search phase mode (exact search, fast search, performance search) based on the solution requirements. Simultaneously, an Oracle lookup table is generated, mapping each trajectory index i to its f(τ). i The value is then encapsulated, along with the phase parameter φ, the Oracle lookup table, etc., and sent to the quantum processing unit. The quantum search unit then executes the standard Grover algorithm, involving quantum initialization, arbitrary superposition state preparation, Oracle lookup, flexible diffusion operator execution, iteration, and measurement, ultimately obtaining a trajectory index i with high probability. result Then index i result The trajectory is mapped back to the specific trajectory τresult and the trajectory output is obtained through visualization. Finally, the feedback learning and adaptive module analyzes the performance indicators of this quantum search (such as time consumption and solution quality) and dynamically adjusts the phase φ and cost threshold Cth of the next round of search based on the performance evaluation.

[0147] To enable those skilled in the art to better understand and implement this invention, a detailed description is provided below with reference to a specific embodiment. This embodiment takes multi-UAV urban logistics collaborative real-time trajectory planning as an example to fully demonstrate the implementation process of this method and system. The task scenario is: in an urban area with buildings (static threats), temporary no-fly zones (dynamic threats), and known airspace traffic (rule constraints), collaborative trajectories are planned for 10 logistics UAVs from a central warehouse to 10 different delivery points, requiring real-time, conflict-free operation and low total energy consumption. The software program of this system is deployed on a quantum-classical hybrid computing platform, which consists of a classical high-performance computing cluster and a noisy medium-scale quantum processor interconnected through a dedicated control interface. The specific implementation steps are as follows:

[0148] 1. System initialization and task parameter input

[0149] (1) User Operation: The operator imports a 3D electronic map of the city into the system's task management interface and delineates a 5km × 5km × 0.5km planning airspace. The operator marks the warehouse location (starting point), 10 delivery points (ending point), fixed building obstacles, and a temporarily issued spherical no-fly zone (center coordinates, radius) on the map. The operator sets the basic task parameters: the maximum planning time budget is 2 seconds, aiming to balance search speed and quality.

[0150] (2) System Execution - Platform and Module Initialization: The system wakes up all modules. The classical computing cluster loads map and constraint data and initializes classical modules such as trajectory generation, cost evaluation, and post-processing optimization. The quantum control interface completes calibration and connection with the quantum processor. The feedback learning module loads the initial strategy parameters (such as phase tendency value and cost weight) optimized for the "urban multi-aircraft logistics" task from the historical database.

[0151] 2. Classical trajectory generation, cost evaluation, and quantum policy generation

[0152] (1) System execution - Candidate trajectory library construction:

[0153] a) Trajectory Generation: For each UAV, the trajectory generation module considers its starting point, ending point, and dynamics model, and runs an improved fast random tree algorithm in parallel to quickly generate a total of N=5000 initial trajectories (an average of 500 trajectories per UAV). Due to the randomness of the algorithm's sampling, N is a natural number and is not forced to be 2. n .

[0154] b) Multi-dimensional cost assessment: The encoding module evaluates the cost of each track τ i Parallel computation of its multidimensional cost C(τ) i For example, for a track, calculate its length:

[0155]

[0156] Risks of crossing near building obstacles:

[0157]

[0158] Its battery consumption is estimated based on a simplified energy consumption model:

[0159]

[0160] And the estimated flight time:

[0161]

[0162] c) Cost Synthesis and Labeling: Based on the task preference of "low energy consumption first", a weight vector w = (wL, wR, wF, wT) = (0.2, 0.3, 0.4, 0.1) is set. First, the costs of each dimension are normalized to obtain... Then calculate the overall cost. The system generates a labeling function based on the maximum cost threshold Cth allowed in the current planning cycle:

[0163]

[0164] Based on this, a mapping is generated to the trajectory index i to f(τ). i Oracle query table.

[0165] (2) System Execution - Quantum Search Strategy Formulation: Based on the real-time requirement of "completing within 2 seconds", the search strategy generation module selects the fast search mode and decides that the fixed rotation phase used in this quantum algorithm call is φ=0.7π. This phase aims to obtain a feasible solution with fewer iteration steps. Subsequently, the phase φ and the Oracle lookup table are encapsulated into a quantum instruction package.

[0166] 3. Quantum Search Core Execution

[0167] System Execution - Quantum State Preparation and Search:

[0168] a) Command transmission: The classical platform sends quantum instruction packets to the quantum processor through the control interface.

[0169] b) Superposition state preparation: The quantum search execution module runs on the quantum processor. First, allocation... n = log2(5000) =13 qubits. Calling the unitary operator U N Through a series of controlled rotation operations, the quantum state is transformed from... Prepared as a uniform superposition state containing all 5000 track indices:

[0170] ;

[0171] c) Iterative Amplification: The module executes sequentially: 1) Based on the lookup table, construct the Oracle operator O, making... ;2) Applying a generalized diffusion operator with parameters φ=0.7π 3) Construct iterative operators This process is repeated approximately Q = 50 times. The number of times is predicted by the classical side based on a pre-defined estimation formula, as shown below:

[0172]

[0173] Where M is the estimated number of marked states.

[0174] d) Measurement: The final quantum state is measured to obtain a 13-bit binary index string with high probability, for example, i result =1011100101011 (bin) (Corresponding to decimal index 2987).

[0175] 4. Classic trajectory decoding, optimization, and conflict detection

[0176] (1) System execution - track extraction and refinement:

[0177] a) Decoding: The trajectory decoding module receives index i result =2987, quickly retrieve the corresponding track τ from the classic memory track library. 2987 .

[0178] b) Trajectory optimization: Trajectory optimization affects τ 2987 The process involves fine-tuning the trajectory. First, dynamic feasibility is verified. Then, using this trajectory as the initial solution, a local optimization algorithm is run to smooth and fine-tune it, generating an optimal candidate trajectory τ that can be flown directly. optimal .

[0179] c) Collision detection: τ optimal Spatiotemporal conflict detection is performed on the planned trajectories of other drones. If a conflict is found, the trajectories are marked, and the classic preprocessing module is notified to fine-tune the cost weights or constraints of the relevant drones (such as slightly increasing the interval time cost) in preparation for the next round of planning.

[0180] 5. Multi-aircraft cooperative loop and track allocation

[0181] (1) System execution - iterative loop: The system performs sequential or batch planning for 10 UAVs. Repeat steps S201 to S401, but in each loop, the encoding module incorporates the spatiotemporal information of the assigned tracks as new constraints to ensure that the tracks planned for new UAVs do not conflict with existing tracks.

[0182] (2) Track set output: Within a 2-second time budget, the system outputs a conflict-free cooperative track set for all 10 UAVs:

[0183]

[0184] 6. Trajectory visualization and task assignment

[0185] (1) System execution:

[0186] a) Visualization: The visualization module renders the 10 flight paths in a 3D city model, distinguishing them with different colors and displaying information such as estimated arrival time and energy consumption. Simultaneously, it generates speed profiles and airspace occupancy time-series diagrams for dispatcher monitoring.

[0187] b) Task issuance: After confirming that the plan is correct, the system will compile the final flight path set into flight commands that each UAV can execute and issue them through the data link.

[0188] 7. Feedback learning and adaptive updating

[0189] (1) System execution - closed-loop learning:

[0190] a) Performance evaluation: After the mission is completed, the feedback learning module collects data, such as the difference between the actual flight time and the planned flight time, and whether any unexpected collision avoidance maneuvers occurred.

[0191] b) Parameter Adjustment: The adaptive adjustment module analyzes the data. For example, if it is found that the actual flight time of most drones is longer than the planned prediction, it may indicate that the time weight w in the cost model is incorrect. T The velocity estimate may be overly optimistic. Based on this, the module generates adjustment suggestions: when performing a similar task next time, the search strategy generation module should use a slightly smaller phase (e.g., adjust φ from 0.7π to 0.65π) for a more refined (slightly slower but more accurate) search; at the same time, the time-of-flight cost model coefficients in the encoding module need to be calibrated.

[0192] Through the seven logically rigorous steps outlined above, this embodiment fully demonstrates how the method and system described in this invention begin with task input, perform problem modeling and strategy formulation through classical computation, utilize quantum processors to achieve parallel and rapid search of a large-scale solution space, obtain a practical solution through classical post-processing, and continuously optimize through closed-loop learning. The entire process realizes an intelligent hybrid computing closed loop of "perception-decision-quantum acceleration-optimization-learning," effectively solving the three major problems mentioned in the background art: insufficient real-time planning, rigid application of traditional quantum algorithms, and lack of system adaptability. This fully demonstrates the technical superiority and practical value of this invention.

[0193] The technical solution provided in this embodiment, through the deep integration of classical-quantum hybrid computing architecture, tunable phase quantum search, and closed-loop feedback adaptive optimization mechanism, brings performance improvement and intelligent evolution capabilities compared to existing technologies, specifically reflected in the following aspects:

[0194] 1. A quantum-classical hybrid computing architecture was constructed, achieving quadratic-level acceleration in searching massive solution spaces and breaking through the real-time bottleneck of traditional planning. The system intelligently divides the computational tasks: classical computing units are responsible for high-fidelity environment modeling, trajectory generation, and complex cost evaluation, generating a candidate trajectory library of arbitrary size; quantum computing units act as coprocessors, dedicated to executing the core parallel search algorithm. By preparing arbitrary cardinality superposition states and running adjustable-phase Grover iterations, this architecture can reduce the query complexity of optimal solution search from the theoretical O(N) of classical algorithms to O(N1 / 2). This hybrid paradigm fully leverages the maturity of classical computing and the parallelism advantages of quantum computing, making it possible to perform global optimization on tens of thousands of candidate trajectories within millisecond to second-level time windows, meeting the stringent real-time requirements of scenarios such as urban air traffic control and drone swarms.

[0195] 2. By introducing a flexible and adjustable fixed rotation phase mechanism, the online reconfigurability and multi-task adaptiveness of the search strategy are realized, solving the problem of rigidity in the application of traditional quantum algorithms. Based on the adjustable rotation phase φ, the search strategy generation module can dynamically select the value of φ (e.g., 0.1π, 0.8π, or π / 3) online according to specific task requirements (such as offline precise planning, online emergency obstacle avoidance, or unknown target information). Smaller phases tend to favor high-success-rate fine-grained searches, while larger phases tend to favor fast convergence. This selection mechanism gives the core of a single quantum search algorithm strong policy flexibility. Users or upper-level systems can dynamically adjust the search behavior based on real-time trade-offs between speed and accuracy, thereby efficiently handling various complex scenarios from task pre-planning to dynamic replanning with a unified framework, greatly enhancing the system's practicality and applicability.

[0196] 3. A parameter adaptive closed loop based on feedback learning is established, enabling continuous self-optimization of system performance and endowing the planning system with intelligent evolution capabilities. This invention constructs a complete "planning-evaluation-optimization" closed loop through a feedback learning module and an adaptive adjustment module. After each task execution, the system automatically analyzes the efficiency of the quantum search (such as the number of iterations and time consumption) and the actual performance of the output trajectory. Based on this data, the system can automatically adjust the key parameters of the next round of planning through a rule engine, such as dynamically optimizing the rotation phase φ, correcting the cost function weight w, and updating the label threshold Cth. This capability allows the system to continuously learn from experience, adapting to different aircraft models, mission types, and even the noise characteristics of the quantum hardware itself, thereby achieving increasingly better intelligent performance and significantly reducing the manual costs of later maintenance and parameter tuning.

[0197] Secondly, this embodiment also provides an optimal trajectory selection system for aircraft based on tunable phase quantum search, such as... Figure 3 As shown, it includes:

[0198] The trajectory encoding module 301 is used to generate several candidate trajectories and calculate their comprehensive cost value according to flight mission constraints; assign a Boolean tag to each trajectory based on a preset cost threshold to identify a high-quality solution; and generate an Oracle query table that maps the trajectory index of the candidate trajectories to the Boolean tags.

[0199] The quantum search module 302 is used to construct a quantum Oracle operator based on the Boolean tag and determine an adjustable phase parameter according to the task requirements to construct a generalized diffusion operator; iteratively executes quantum operations composed of the Oracle operator and the generalized diffusion operator on the quantum superposition state encoded with the trajectory index to obtain an output index;

[0200] The optimization module 303 is used to map the output index to the corresponding candidate trajectory, use the candidate trajectory as the initial solution to call the classical trajectory optimization algorithm for local fine-tuning and smoothing, and obtain the optimized trajectory for the current round; adjust the adjustable phase parameter and the cost threshold based on the performance evaluation of this quantum search; wherein, based on the adjusted adjustable phase parameter and the cost threshold, the trajectory encoding step and the quantum search step are repeatedly executed, and when the preset termination condition is met, the currently obtained optimized trajectory is determined as the optimal trajectory of the flight mission.

[0201] This system can be used to perform the aircraft optimal trajectory selection method based on tunable phase quantum search described in the first aspect, which will not be elaborated further here.

[0202] Furthermore, this embodiment also provides an implementation method for an optimal trajectory generation system for aircraft based on tunable phase quantum search. This embodiment, based on a classical-quantum hybrid computing architecture, with flexible phase decision-making and feedback adaptation as its core, and quantum parallel search as its acceleration engine, constructs a full-process intelligent planning system from problem modeling and quantum accelerated search to result optimization and self-learning. This solves the problems of low search efficiency caused by computational bottlenecks in existing trajectory planning methods, the disconnect between applications and real-world scenarios, and the rigidity of system parameters lacking self-optimization capabilities. The system relies on a high-performance classical computing server, a noisy medium-scale quantum processor, and a dedicated classical-quantum control and communication interface in terms of hardware. In terms of software, the system mainly consists of an aircraft trajectory generation module, a problem encoding module, a search strategy generation module, a quantum search execution module, a trajectory decoding module, a visualization module, a feedback learning module, and an adaptive adjustment module. Its system framework is as follows: Figure 4 As shown.

[0203] 1. Aircraft trajectory generation module:

[0204] This module acts as the system's "solution space builder," responsible for generating a set of physically feasible candidate trajectories within the classical computational domain, based on the task input. The module receives the start and end points of the flight mission, airspace constraints, and a dynamic model. Using classical motion planning algorithms such as fast random trees, it samples and generates N initial candidate trajectories {τi} in the continuous state space. Its core function is to construct an initial trajectory database of arbitrary positive integer size N, providing specific, discretized search objects for subsequent quantum searches. This module ensures the realism and physical feasibility of the objects processed by the quantum algorithm.

[0205] 2. Encoding module

[0206] This module acts as the system's "problem translator," responsible for encoding continuous trajectory optimization problems into discrete logic problems that can be processed by quantum computers. The module performs parallel, multi-dimensional cost evaluation on each trajectory τi output by the trajectory generation module, calculating its comprehensive cost C(τi), including range, threat exposure, and energy consumption. Subsequently, based on a preset cost threshold Cth, a binary labeling function f(τi) is defined to classify trajectories as either "high-quality solutions" (labeled 1) or "low-quality solutions" (labeled 0). Finally, the module generates an "Oracle lookup table," which establishes a one-to-one mapping between the unique index i of each trajectory and its label value f(i), completing the crucial encoding from physical trajectory to logical decision.

[0207] 3. Search Strategy Generation Module

[0208] This module acts as the system's "decision-making brain," responsible for determining the core control parameter of the quantum search—the fixed rotation phase φ. Based on the specific needs of the task (such as offline precise planning, online real-time replanning, or robust search with unknown target information), the module selects the corresponding phase value from a pre-defined strategy library. For example, it might choose a small phase φ = 0.1π to pursue a high success rate, or a large phase φ = 0.8π to pursue a faster search speed. When the number of target solutions is completely unknown, the module uses a fixed robustness setting of φ = π / 3. The output of this module—the phase parameter φ—and the lookup table output by the encoding module together constitute the complete "instruction set" driving the quantum search.

[0209] 4. Quantum Search Execution Module

[0210] This module is the system's "quadratic acceleration engine," physically executing the search algorithm on the quantum processor. It receives an instruction set from the classical domain and executes it sequentially: 1) Invoking the unitary operator UN to prepare a balanced superposition state covering all N trajectory indices on n qubits; 2) Constructing and applying the quantum Oracle operator O based on the lookup table to perform phase flipping on the high-quality solution's labeled state; 3) Constructing and applying the generalized diffusion operator Dφ based on the given phase φ; 4) Combining O and Dφ into the iterative operator Gφ and repeating it Q times to coherently amplify the probability amplitude of the high-quality solution; 5) Measuring the final quantum state to obtain a trajectory index iresult with high probability. This module is the hardware implementation of the algorithm's theoretical advantages.

[0211] 5. Trajectory Decoding Module

[0212] This module acts as the system's "result interpreter," responsible for mapping the output of the quantum domain back to its specific meaning in the classical domain. It receives the binary index `iresult` measured by the quantum search execution module and retrieves the complete trajectory parameter `τresult`, uniquely corresponding to that index, through a single fast query operation in the classical trajectory database. This module achieves the crucial transformation from quantum information (a digital index) to engineering information (an executable flight path), and is an indispensable "decoding" component in the quantum-classical hybrid architecture.

[0213] 6. Visualization Module

[0214] This module serves as the system's "human-computer interface," responsible for transforming abstract trajectory data into intuitive, multi-dimensional graphics and reports. Developed based on a professional graphics library, the module provides visualizations such as 3D spatial trajectory rendering, 2D multi-plane projection, and velocity / acceleration / altitude time profiles. Simultaneously, it automatically generates text reports summarizing key statistical indicators from single or multiple comparative trajectories, such as total range, average speed, maximum overload, and estimated energy consumption, providing users with comprehensive situational awareness and decision support.

[0215] 7. Feedback Learning Module

[0216] This module acts as the system's "performance evaluator," responsible for establishing a quantitative evaluation system for the planning process. After each planning task is completed, this module collects data from multiple sources, including quantum search time, number of iterations, cost ranking of the final solution, and actual flight verification data of the solution (if available). By modeling and analyzing this historical performance data, the module can assess the effectiveness of the current parameter configuration (such as phase φ and cost threshold Cth), identify performance bottlenecks or optimization directions, and provide data-driven decision-making basis for the system's self-optimization.

[0217] 8. Adaptive Adjustment Module

[0218] This module serves as the system's "self-optimization engine," crucial for achieving an intelligent closed loop. It receives performance evaluations from the feedback learning module and dynamically adjusts key parameters of downstream modules based on preset optimization rules or lightweight learning algorithms. For example, if feedback indicates poor quality of recent search results, this module instructs the search strategy generation module to appropriately reduce the phase φ in the next task for a more refined search; if feedback indicates excessive search time, it suggests increasing the phase φ to accelerate the search. Simultaneously, it can guide the encoding module to adjust the cost threshold Cth to balance the quantity and quality of the target solutions. In this way, the system can continuously adapt to different task types and environmental changes.

[0219] Thirdly, this embodiment also provides an electronic device, the electronic device comprising: one or more processors, and a memory for storing one or more computer programs; the computer programs are configured to be executed by the one or more processors, the programs comprising steps for performing the aircraft optimal trajectory selection method based on tunable phase quantum search as described in the first aspect.

[0220] This electronic device can be used to perform the aircraft optimal trajectory selection method based on tunable phase quantum search as described in the first aspect, which will not be elaborated further here.

[0221] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for selecting the optimal trajectory of an aircraft based on tunable phase quantum search, characterized in that, include: Trajectory coding steps: Based on flight mission constraints, generate several candidate trajectories and calculate their comprehensive cost; Based on a preset cost threshold, a Boolean label is assigned to each trajectory to identify a high-quality solution; And generate an Oracle query table that maps the trajectory index of the candidate trajectory to the Boolean tag; Quantum search steps: Construct a quantum Oracle operator based on the Boolean label, and determine an adjustable phase parameter according to task requirements to construct a generalized diffusion operator; An output index is obtained by iteratively performing quantum operations consisting of the Oracle operator and the generalized diffusion operator on the quantum superposition state that encodes the trajectory index; Optimization steps: Map the output index to the corresponding candidate trajectory, use the candidate trajectory as the initial solution to call the classical trajectory optimization algorithm for local fine-tuning and smoothing, and obtain the optimized trajectory for the current round; adjust the adjustable phase parameter and the cost threshold based on the performance evaluation of this quantum search; wherein, based on the adjusted adjustable phase parameter and the cost threshold, repeat the trajectory encoding step and the quantum search step, and when the preset termination condition is met, determine the currently obtained optimized trajectory as the optimal trajectory of the flight mission.

2. The method for selecting the optimal trajectory of a spacecraft based on tunable phase quantum search according to claim 1, characterized in that, The flight mission constraints include the starting position, the ending position, airspace constraints, and the aircraft dynamics model; the airspace constraints include obstacles, no-fly zones, and terrain restrictions.

3. The method for selecting the optimal trajectory of a spacecraft based on tunable phase quantum search according to claim 2, characterized in that, The comprehensive cost includes calculating the multidimensional cost vector C(τ) of the candidate trajectory τi. i ): ； Among them, w L ,w R ,w F ,w T ∈[0,1] represents the weight coefficient; The cost of exposing the threat; In exchange for flight time; Cost of flight path; The cost of reduced battery life; The method of assigning a Boolean label to each trajectory based on a preset cost threshold to identify high-quality solutions includes: defining a Boolean labeling function f(τ). i ) is used to generate the determination result of the Boolean flag; wherein: If C(τ) i )≤C th Then f(τ) i If f(τ) = 1, mark it as a high-quality solution; otherwise, mark it as f(τ). i )=0; C th This is the preset cost threshold.

4. The method for selecting the optimal trajectory of a spacecraft based on tunable phase quantum search according to claim 3, characterized in that, The generation of the trajectory index mapping the candidate trajectories to the Boolean-tagged Oracle query table includes: Based on the determination result of the Boolean flag, a one-to-one mapping relationship is established between the unique index of each candidate trajectory and the candidate trajectory flag value, forming the Oracle query table; The Oracle lookup table and the adjustable phase parameter are encapsulated into a quantum instruction package and sent to the quantum simulator to construct the quantum Oracle operator.

5. The method for selecting the optimal trajectory of an aircraft based on tunable phase quantum search according to claim 4, characterized in that, The quantum Oracle operator is constructed based on the Boolean notation, and an adjustable phase parameter is determined according to task requirements to construct a generalized diffusion operator, including: An auxiliary qubit is introduced, and the trajectory index state is associated with the Boolean tag through a controlled gate network. A phase flip operation is applied to the state marked as a high-quality solution, and the inverse operation solution is calculated to clear the auxiliary qubit, thereby realizing the negative phase tagging of the ground state corresponding to the high-quality solution, and thus obtaining the quantum Oracle operator. The construction of the generalized diffusion operator includes: constructing a reflection operator with respect to the average probability amplitude based on the adjustable phase parameter; the reflection intensity of the reflection operator is precisely controlled by the adjustable phase parameter, and when the adjustable phase parameter is a flat angle, the reflection operator degenerates into the standard Grover diffusion operator, which serves as the generalized diffusion operator.

6. The method for selecting the optimal trajectory of an aircraft based on tunable phase quantum search according to claim 5, characterized in that, The iterative execution of quantum operations consisting of the Oracle operator and the generalized diffusion operator on the quantum superposition state encoded with the trajectory index to obtain an output index includes: n qubits are allocated as the master register on the quantum simulator; where N is the number of candidate trajectories; The unitary operator UN is invoked to prepare a balanced superposition state containing all N candidate trajectory indices; the generalized diffusion operator and the Oracle operator are combined to form a single-iteration operator; Repeat the iterative operator Q times, so that the probability amplitude corresponding to the trajectory index marked as a high-quality solution is coherently amplified; The output index is obtained by measuring all the qubits of the main register and collapsing them with high probability.

7. The method for selecting the optimal trajectory of a spacecraft based on tunable phase quantum search according to claim 1, characterized in that, The output index is mapped to the corresponding candidate trajectory. Using this candidate trajectory as the initial solution, a classic trajectory optimization algorithm is called for local fine-tuning and smoothing to obtain the optimized trajectory for the current round, including: The output index is mapped back to the classic trajectory database to retrieve the trajectory parameters that uniquely correspond to the index of the candidate trajectory. The trajectory parameters are dynamically and constrained using a high-precision classical simulator; the verified candidate trajectory is used as a high-quality initial solution, and a sequential quadratic programming algorithm is called for local fine-tuning and smoothing to generate the current round of optimized trajectory.

8. The method for selecting the optimal trajectory of a spacecraft based on tunable phase quantum search according to claim 7, characterized in that, The adjustment of the adjustable phase parameter and the cost threshold based on the performance evaluation of this quantum search includes: A performance evaluation model is established to evaluate the performance of this search based on a weighted score of search time and solution quality; wherein, the solution quality is determined based on the percentile of the comprehensive cost of the current round of optimization trajectory in historical solutions; If the solution quality is lower than a preset quality threshold and the current iteration count has not reached the upper limit, then the adjustable phase parameter is reduced by a first proportional coefficient. If the search time exceeds a preset time threshold, the adjustable phase parameter is increased by a second proportional coefficient. The proportion of trajectories marked as high-quality solutions in the historical multi-round search is counted. If this proportion is consistently lower than a first proportion threshold, the cost threshold is increased; if this proportion is consistently higher than a second proportion threshold, the cost threshold is decreased.

9. A system for selecting the optimal trajectory of an aircraft based on tunable phase quantum search, characterized in that, include: The trajectory encoding module is used to generate several candidate trajectories and calculate their comprehensive cost value based on flight mission constraints. Based on a preset cost threshold, a Boolean label is assigned to each trajectory to identify a high-quality solution; And generate an Oracle query table that maps the trajectory index of the candidate trajectory to the Boolean tag; A quantum search module is used to construct a quantum Oracle operator based on the Boolean tag and to determine an adjustable phase parameter according to task requirements to construct a generalized diffusion operator. An output index is obtained by iteratively performing quantum operations consisting of the Oracle operator and the generalized diffusion operator on the quantum superposition state that encodes the trajectory index; The optimization module maps the output index to the corresponding candidate trajectory, uses the candidate trajectory as the initial solution to call the classical trajectory optimization algorithm for local fine-tuning and smoothing, and obtains the optimized trajectory for the current round; it adjusts the adjustable phase parameter and the cost threshold based on the performance evaluation of this quantum search; and it repeatedly executes trajectory encoding and quantum search based on the adjusted adjustable phase parameter and the cost threshold. When the preset termination condition is met, the currently obtained optimized trajectory is determined as the optimal trajectory for the flight mission.

10. An electronic device, the electronic device comprising: One or more processors, a memory for storing one or more computer programs; characterized in that the computer programs are configured to be executed by the one or more processors, the programs including steps for performing the aircraft optimal trajectory selection method based on tunable phase quantum search as described in any one of claims 1-8.

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