Quantum simulation method for wild soybean population degradation process

Abstract

This invention discloses a quantum simulation method for the degradation process of wild soybean populations, relating to the fields of quantum computing simulation and ecological protection technology. The invention collects core ecological parameters of wild soybean and performs differentiated quantum encoding and entanglement initialization; constructs a total ecological Hamiltonian that includes intraspecific competition, genetic drift, environmental selection pressure, and non-commutation characteristics, and optimizes the quantum gate decomposition sequence using weighted sorting driven by ecological sensitivity and hardware noise; employs adaptive decomposition order selection and dynamic step size optimization based on real-time coherent sensing; performs hierarchical zero-noise extrapolation on key quantum circuits and probability error elimination on non-key circuits; and combines classical population dynamics models for closed-loop verification and backtracking optimization. This effectively solves the problems of population degradation processes under multi-factor non-commutation coupling and quantum hardware noise interference, significantly improving the simulation accuracy and noise resistance of multi-generational population degradation trajectories.

Description

Technical Field

[0001] This invention relates to the field of quantum computing simulation and ecological protection technology, specifically a quantum simulation method for the degradation process of wild soybean populations. Background Technology

[0002] Wild soybean is a Class II protected wild plant in my country and a core genetic resource for improving cultivated soybean germplasm and breeding for stress resistance. It has key strategic value for ensuring the safety of soybean seed industry and the stability of biodiversity. Currently, affected by multiple factors such as habitat destruction, environmental pollution, human interference and genetic bottlenecks, the natural population size of wild soybean continues to shrink and genetic diversity is rapidly lost, and the problem of population degradation is becoming increasingly serious. The degradation process of wild soybean population is a typical multi-factor noncommutative coupled evolutionary system. Environmental stress, gene variation, population reproduction and other influencing factors are intertwined and dynamically balanced. Traditional linear simulation models are difficult to accurately characterize the nonlinear coupling effect and dynamic evolution law of each factor, and the simulation accuracy and adaptability have obvious shortcomings. As quantum simulation technology is gradually applied to ecological evolution research, its high-dimensional parallel computing advantage can be adapted to the simulation of complex ecosystem evolution. Population states are encoded as qubits, stress factors are mapped as quantum operators, and multi-factor coupling corresponds to a sequence of non-commutative operators. However, due to the non-commutativity between operators, accurate simulation requires deep quantum circuits, which are severely affected by decoherence, gate errors, and readout noise on current noisy medium-scale quantum hardware, resulting in a sharp decline in simulation fidelity. Furthermore, existing quantum error mitigation schemes are insufficient to suppress correlation noise caused by multi-factor non-commutative coupling structures. Therefore, it is urgent to develop a simulation method that balances the accuracy of non-commutative coupling simulation with the suppression of quantum hardware noise interference for the quantum simulation of wild soybean population degradation.

[0003] To address the aforementioned shortcomings, a technical solution is provided. Summary of the Invention

[0004] The purpose of this invention is to solve the problems of population degradation process under multi-factor non-commutative coupling and quantum hardware noise interference, and to propose a quantum simulation method for the degradation process of wild soybean population.

[0005] The objective of this invention can be achieved through the following technical solutions: Quantum simulation methods for the degradation process of wild soybean populations include: S1: Collect core ecological parameters of wild soybean, divide them into fast-changing and slow-changing parameters, and map them to quantum bit groups respectively. Allocate the number of coding bits based on the contribution sensitivity of degradation index, and establish initial entanglement based on the correlation coefficient of ecological parameters. S2: Construct the total ecological Hamiltonian describing the dynamics of wild soybean population degradation, and calculate the comprehensive ranking weight of ecological sensitivity and hardware noise cumulative error for each Hamiltonian quantum term. Decompose the terms in descending order of weight and map them to a quantum gate sequence. S3: Apply higher-order Trotter decomposition to Hamiltonian terms corresponding to fast-varying parameters and lower-order decomposition to terms corresponding to slow-varying parameters; calculate the coherence security factor and adjust the evolution step size; remap the quantum gate based on the bit coherence remaining time; S4: Based on the comprehensive ranking weight, the quantum gate circuits corresponding to Hamiltonian quantum terms are divided into critical quantum circuits and non-critical quantum circuits, and hierarchical error suppression is performed on the quantum circuits. S5: Obtain the population gene flow adjacency matrix, form an isomorphic entangled structure based on hardware coupling graph topological mapping; calculate the concurrent entanglement degree and determine strong and weak entanglement, and reconstruct the multi-generation degeneration time series trajectory; S6: Based on the simplified classical population dynamics function, the quantum simulation results are subjected to time residual verification and backtracking optimization of quantum circuit parameters.

[0006] Furthermore, the specific operation steps of S1 are as follows: Core ecological parameters of wild soybean populations were collected and classified into fast-changing and slow-changing ecological parameters based on their change cycles. By constructing a hardware coherence-parameter variation matching strategy through the decoherence time of qubits, fast-changing ecological parameters are mapped to high-quality qubit groups, and slow-changing ecological parameters are mapped to ordinary-quality qubit groups. Calculate the sensitivity of each ecological parameter to the population degradation index, and allocate the number of coding bits differently based on the level of sensitivity. Based on the Pearson correlation coefficient between ecological parameters, a multi-qubit controlled rotation gate is used to establish initial entanglement between different qubit groups, so that the degree of entanglement is positively correlated with the ecological correlation of parameters; by integrating fast-changing parameter branches, slow-changing parameter branches and auxiliary qubits, the overall preparation of hierarchical quantum initial states is completed.

[0007] Furthermore, the specific operation steps of S2 are as follows: Construct the total ecological Hamiltonian, which includes cross-coupled terms such as intraspecific competition, genetic drift, environmental selection pressure, and non-commutation characteristics; Using population degradation indicators as the evaluation object, the ecological sensitivity of each Hamiltonian quantum term is calculated, the average fidelity of the basic quantum gate of the pre-collected quantum processor is calculated, and the cumulative error of hardware noise after the decomposition of each sub-term is estimated. The comprehensive ranking weight is calculated based on ecological sensitivity and noise error. The Hamiltonian quantum terms are then arranged in descending order based on the comprehensive ranking weight. The time evolution operator decomposition is performed on the sorted non-commutative Hamiltonians using the symmetric Lie-Trotter approximation method. The time evolution operators obtained from the decomposition are mapped one by one to the basic quantum gate combination sequence to form an optimized quantum gate sequence.

[0008] Furthermore, the specific operation steps of S3 are as follows: Before executing the quantum simulation, obtain the decoherence time of the qubit and the average fidelity of the two-qubit gate, and establish a hardware coherence and noise parameter library; Based on the hierarchical quantum encoding results of S1, differential Trotter decomposition is performed on the Hamiltonian quantum terms: For the Hamiltonian quantum terms corresponding to rapidly varying parameters, a fourth-order Suzuki-Trotter decomposition is used; for the Hamiltonian quantum terms corresponding to slowly varying parameters, a first-order Trotter decomposition is used. Real-time monitoring of the minimum decoherence time of qubits, calculation of coherence security factor, adjustment of single-step evolution duration and acquisition of total Trotter steps; Before each evolution step, qubit remapping is performed based on the bit coherence remaining time, and high-weight, high-frequency quantum gates are preferentially allocated to the physical bits with the longest coherence remaining time. During the simulation, the quantum bit coherence state is recalibrated based on a preset period. If the decoherence time decay exceeds the threshold, the hardware parameter library is updated and the step size calculation and bit remapping process is automatically restarted.

[0009] Furthermore, the specific operation steps of S4 are as follows: Based on the comprehensive sorting weight of Hamiltonian quantum terms, critical and non-critical quantum gate circuits are divided; Zero-noise extrapolation processing based on ecological sensitivity classification is performed on key quantum gate circuits. A noise scaling factor is selected and the circuit noise is stretched using digital gate expansion technology. A hierarchical fitting network is constructed based on high-sensitivity and medium-sensitivity groups and extrapolated to the zero-noise limit. Non-critical quantum gate circuits are treated using the probability error elimination method of quantum process tomography; The total correction observation is obtained by weighted fusion of the critical circuit correction results and the non-critical circuit elimination results. Set an ecological prediction tolerance error threshold, and use a resampling method to evaluate the fusion results. If the evaluation result is higher than the ecological prediction tolerance error threshold, the measurement configuration will be automatically optimized and adjusted, including increasing the noise scaling factor density of zero-noise extrapolation or increasing the sampling number of probability error elimination and tomographic measurement, until the evaluation result is lower than the ecological prediction tolerance error threshold.

[0010] Furthermore, the specific operation steps of S5 are as follows: The adjacency matrix of the gene flow of wild soybean populations was obtained, and topological mapping was performed based on the physical coupling graph of the quantum processor to ensure that the entangled structure of the qubits and the coupling relationship of the population remain isomorphic. Measurements were performed on the corresponding basis vectors for the qubits encoding population density, genetic heterozygosity, and allele frequency, and the relevant information on population density and genetic diversity was obtained by reading and decoding. Traverse all pairs of qubits encoding ecological parameters, calculate the concurrent entanglement degree of each pair of qubits, and classify them into strongly entangled pairs and weakly entangled bits based on the degree of entanglement. Perform Belli joint measurement on strongly entangled pairs and single-bit measurement on weakly entangled bits. Based on the population density and genetic heterozygosity data of each population recorded in evolutionary generations, the sampling points are connected sequentially in chronological order to reconstruct the time series trajectory of multi-generational degradation of wild soybean populations, and the degradation trajectory and degradation characteristic parameters are output.

[0011] Furthermore, the specific operation steps of S6 are as follows: Population degradation rate and genetic diversity loss rate were extracted from quantum simulation degradation trajectories as verification indicators; A simplified classical population dynamics function is constructed and a numerical solution of the function at the same generational evolution scale as the quantum simulation is output, which serves as a benchmark reference sequence for the quantum simulation results; Calculate the temporal residuals between the quantum simulation results and the benchmark reference sequence. If the temporal residuals are all below the ecological prediction tolerance error threshold, the quantum simulation results are deemed to have passed the verification. If any time-series residual exceeds the ecological prediction tolerance error threshold, then circuit parameter backtracking optimization is triggered. The circuit parameter backtracking optimization sequentially performs step size checks, error suppression checks, and measurement statistics checks. After completing the backtracking optimization process steps, the complete process from S3 to S6 is rerun with the corrected circuit parameters and measurement configuration until the temporal residuals of degradation rate and genetic diversity loss rate are both lower than the ecological prediction tolerance error threshold. The validated trajectory data is input into the risk prediction model, which outputs the confidence interval for population extinction risk, the ranking of degradation driving factors, and statistical information on key hardware for quantum hardware execution.

[0012] Compared with the prior art, the beneficial effects of the present invention are: This invention collects core ecological parameters of wild soybean populations and performs differentiated encoding and entanglement initialization of fast and slow variables based on the decoherence time of quantum hardware. It prioritizes and decomposes non-commutative Hamiltonian quantum terms using a combination of ecological sensitivity and quantum gate fidelity. Through adaptive Trotter decomposition order selection, dynamic step size adjustment, and bit remapping based on real-time coherence sensing, it alleviates the constraint of finite coherence time on long-term simulations. Furthermore, by employing zero-noise extrapolation of critical circuits based on ecological sensitivity grading, elimination of probabilistic errors in non-critical circuits, and closed-loop classical verification backtracking optimization, it significantly improves the simulation accuracy of wild soybean population degradation trajectories under multi-factor coupling, computational robustness in noisy quantum hardware environments, and the reliability of long-term evolution predictions. Attached Figure Description

[0013] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0014] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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.

[0015] Example: like Figure 1 As shown, a quantum simulation method for the degradation process of wild soybean populations includes: S1: Differentiated quantum encoding and entanglement initialization based on hardware coherence characteristics and sensitivity, the specific implementation process is as follows: Initial population density of the target wild soybean population. Genetic heterozygosity, environmental carrying capacity Core ecological parameters include environmental stress intensity, instantaneous selection coefficient, genetic drift coefficient, and effective population size; Population density, instantaneous selection coefficient, and instantaneous stress fluctuation terms with a change period of less than 10 generations are classified as rapidly changing ecological parameters; parameters with a change period of more than 50 generations, such as genetic drift coefficient, are classified as slowly changing ecological parameters. In quantum processors, the decoherence time of each qubit is obtained via Ramsey interferometry. Construct a hardware coherence-parameter variation matching strategy: Prioritize mapping rapidly changing ecological parameters to High-quality physical quantum bit sets; Mapping slowly varying ecological parameters to decoherence time in A set of ordinary-quality qubits within a given range; Simultaneously, quantum state encoding is performed on core ecological parameters, including: normalized population density encoding, genetic heterozygosity encoding, and environmental stress factor encoding; Define population degradation index for The percentage decrease in population density relative to the initial value after generation is used to calculate the percentage decrease in population density after generation. Ecological parameters Population degradation indicators Contribution sensitivity The calculation formula is: ,in, This indicates the application of all ecological parameters. Sum of the absolute values ​​of the partial derivatives This indicates the index for iterating through all ecological parameters. Indicates the sign of partial derivatives; Based on the number of base-coded bits Based on this, and combined with preset high-sensitivity and low-sensitivity thresholds, differentiated precision allocation is performed: When the contribution sensitivity exceeds the preset high-sensitivity threshold, it is determined to be a high-sensitivity parameter, and the following is adopted: Bit encoding improves precision; When the contribution sensitivity is lower than the preset low-sensitivity threshold, it is determined to be a low-sensitivity parameter, and the following is adopted: Bit encoding saves resources; When the contribution sensitivity is between the preset low sensitivity threshold and the high sensitivity threshold, the following is adopted: Bit encoding achieves a balance between precision and resource allocation; Based on the Pearson correlation coefficient between ecological parameters such as population density, genetic heterozygosity, environmental stress factors, environmental carrying capacity, and genetic drift coefficient in actual ecosystems, initial weak entanglement is established between qubit groups of different qualities through multi-qubit controlled rotating gates CRX and CRY. The angle of the rotating gate is determined by the value of the Pearson correlation coefficient, so that the initial entanglement between qubits is positively correlated with the ecological correlation of the parameters to be simulated. By integrating fast-parameter quantum branches, slow-parameter quantum branches, and auxiliary qubits, the overall hierarchical quantum initial state is prepared.

[0016] S2: Based on the joint drive of ecological impact sensitivity and quantum hardware, the non-pariquitous Hamiltonian decomposition and quantum gate sequence optimization are implemented as follows: Constructing a total ecological Hamiltonian to describe the dynamics of wild soybean population degradation. It includes intraspecific competition items. Genetic drift term Environmental selection pressure item And cross-coupling terms describing the non-commutative properties between terms, specifically in the form of: ,in, Indicates the first The Hamiltonian term and the first The coupling coefficient between Hamiltonian terms Indicates the first One Hamiltonian quantum term, Indicates the first One Hamiltonian quantum term, It is a non-commutative operator that satisfies ; The intraspecific competition term is expressed using a quadratic form with a local population density operator: ,in, Indicates the number of patches in a wild soybean population. Indicates the first Population number operator corresponding to each patch This represents a parameter indicating the intensity of intraspecific competition. Unit operator The genetic drift term uses the quantum Pauli operator to characterize the random walk process of allele frequencies: ,in, This represents the total number of target gene loci, i.e., the number of target gene loci being studied. Indicates the first Genetic drift angular frequency at each gene locus The effective population size refers to the number of individuals in a wild soybean population that actually participate in reproduction and can transmit genes. Indicates the first Pauli's theory of gene loci corresponding to qubits Operators; The environmental selection pressure term is constructed based on the relationship between the intensity of environmental stress and the phase modulation of qubits, and together with the intraspecific competition term and the genetic drift term, it constitutes a non-commutative Hamiltonian system. Using population degradation indicators as the evaluation object, the finite difference method is used to calculate the ecological sensitivity of each Hamiltonian quantum term to the degradation indicator. The calculation formula is: ,in, Preset small perturbation amount This represents the calculated value of the degradation index corresponding to the original Hamiltonian. Indicates the first Calculated values ​​of the degradation index after perturbation of each Hamiltonian quantum term; The average fidelity of fundamental quantum gates such as single-qubit rotation gates and two-qubit controlled gates on this quantum processor was pre-collected. ; Based on average fidelity and the expected quantum circuit depth after this decomposition. Calculate the estimated cumulative error caused by hardware noise after the decomposition of this term. The calculation formula is: ; The smaller the cumulative error estimate, the weaker the hardware noise accumulation effect after the current Hamiltonian quantum term is decomposed into a sequence of quantum gates; Through formula Define the comprehensive ranking weights that combine ecological sensitivity and error characteristics as constraints. ; All Hamiltonian quantum terms are sorted in descending order based on the comprehensive ranking weight values, thus achieving priority decomposition and execution of Hamiltonian quantum terms with high ecological impact and low noise error. The symmetric Lie-Trotter approximation decomposition method is used to perform time evolution operator decomposition on the sorted noncommutative Hamiltonians. Commutative correction terms are inserted into the noncommutative sub-items to reduce the decomposition error caused by the noncommutative property. The specific form is as follows: ,in, This represents the time evolution operator corresponding to the total ecological Hamiltonian. ≈ indicates approximation. Symmetric Lie-Trotter decomposition is a numerical approximation; the smaller the step size, the more accurate the result. This indicates that all Hamiltonian terms are multiplied sequentially. This represents the product of all Hamiltonian quantum terms in reverse order. This indicates the duration of a single evolutionary step, corresponding to the number of generations in wild soybean. Represents the imaginary unit; The time evolution operators obtained from the decomposition are mapped one by one to the basic quantum gate combination sequence to form an optimized quantum gate sequence.

[0017] S3: Adaptive decomposition order selection, dynamic step size optimization, and bit remapping based on real-time coherent sensing are implemented as follows: Before the quantum simulation is executed, the decoherence time of each qubit is obtained by Ramsey interferometry, and the average fidelity of all two-qubit gate pairs is obtained by random benchmark testing method, and a quantum hardware coherence and noise parameter library is established. Based on the hierarchical quantum encoding results of S1, differential Trotter decomposition is performed on the Hamiltonian quantum terms: For the Hamiltonian quantum terms corresponding to the fast-varying parameters allocated to the high-quality qubit group in S1, a fourth-order Suzuki-Trotter decomposition is performed, specifically as follows: , in, These correspond to the first and second type Hamiltonian sub-items of the rapidly changing ecological parameters, respectively. All are fourth-order Suzuki-Trotter fixed coefficients, satisfying: , ; For the Hamiltonian quantum terms corresponding to the slowly varying parameters assigned to the ordinary quality qubit group, a first-order Trotter decomposition is performed, specifically in the following form: ,in, This represents the total Hamiltonian corresponding to the slowly changing ecological parameter. The first Hamiltonian obtained by decomposing the slow-varying total Hamiltonian One Hamiltonian quantum term; Real-time acquisition of the minimum decoherence time of all qubits involved in the computation Through formula The coherence time safety factor was calculated. ,in, Indicates the total execution time of a single-step circuit; When the coherence time safety factor is greater than the preset safety threshold If the current step size is determined to cause decoherence, a Trotter step size reduction operation is performed, updating the step size according to the following formula: ; At the same time, an additional reduction factor based on average fidelity is introduced. Adjust the step size: ; The coherence time safety factor is recalculated with the corrected step size. The process is iterated until the coherence time safety factor is less than or equal to the preset safety threshold. The current single-step evolution time is then output as the final single-step evolution time. The total evolution time is compared with the final single-step evolution time of the output, and then rounded down to determine the total number of Trotter steps that satisfy the hardware coherence constraints. Before each evolution begins, the coherent remaining time of each qubit is obtained based on the difference between the decoherence time of each qubit and the accumulated execution time of the quantum circuit. The hardware resource-aware qubit remapping algorithm is executed, which determines S2 as a high-weight, high-frequency quantum gate and prioritizes its execution on the physical qubit with the longest remaining coherence time. During the simulation, the coherent state recalibration of the qubits is triggered at predetermined intervals: the decoherence time of each qubit is remeasured. If the decoherence time of any qubit decreases by more than a preset threshold compared to the reference value in the initial parameter library, it is determined that the hardware coherence environment has deteriorated significantly. The coherence and noise parameter library is updated immediately, and the step size calculation and dynamic qubit remapping process are automatically re-executed to adapt to the real-time state of the current hardware.

[0018] S4. Hierarchical error suppression of quantum circuits based on ecological sensitivity, the specific implementation process is as follows: Based on the comprehensive ranking weights obtained from S2, the quantum gate circuits corresponding to the Hamiltonian quantum terms with weights in the top 30% are extracted and marked as key quantum gate circuits, while the rest are non-key quantum gate circuits. Perform zero-noise extrapolation of key quantum gates based on ecological sensitivity classification, including: By selecting a set of noise scaling factors and using digital gate expansion technology, each quantum gate in the original circuit is replaced with a combination of its quantum gate and hermitian conjugate gate, and the circuit noise level is amplified proportionally based on the noise scaling factors. The stretched circuit is measured under each scaling factor to obtain the expected value of the degradation-related observations. ; Based on the ecological sensitivity of the Hamiltonian quantum terms to which the key quantum gates belong, they are divided into a high-sensitivity group and a medium-sensitivity group, and a hierarchical fitting network is constructed: For the highly sensitive group, an exponential fitting network is used, with the expression as follows: ; For the sensitive group, a linear fitting network is used, and the expression is: ; The response curve is fitted using the least squares method, and the fitting result is extrapolated to... The zero-noise limit value of the observed quantity is obtained. ; in, This indicates that the noise scaling factor is... At that time, the expected value of the measurement of degradation-related observations, Indicates the noise response amplitude coefficient. Indicates the noise attenuation coefficient; For non-critical quantum gates, a probabilistic error elimination method based on quantum process tomography is used to obtain the observations after error elimination. ; The zero-noise extrapolation results of critical circuits and the probability error elimination results of non-critical circuits are weighted and fused to obtain the total correction observations: ,in, The preset contribution weights of key quantum gate circuits are indicated. Set an ecological prediction tolerance error threshold and use the Bootstrap resampling method to evaluate the total correction observations; If the evaluation result is higher than the ecological prediction tolerance error threshold, the measurement configuration will be automatically optimized and adjusted, including increasing the noise scaling factor density of zero-noise extrapolation or increasing the sampling number of probability error elimination and tomographic measurement, until the evaluation result is lower than the ecological prediction tolerance error threshold, thus completing adaptive error suppression for the current hardware noise environment.

[0019] S5: Quantum tomography measurement and trajectory reconstruction based on hardware topology awareness and entanglement degree adaptation, the specific implementation process is as follows: The adjacency matrix, which characterizes the gene flow intensity among wild soybean populations, is used as input. Based on the physical coupling graph of the quantum processor, a mapping algorithm that minimizes the depth of the SWAP gate cascade is used to map the inter-population coupling relationship defined by the adjacency matrix to the entangled topology of qubits: for physically directly coupled qubit pairs, they are preferentially assigned to high gene flow population pairs; for population pairs without direct physical connection but requiring entanglement, virtual entangled links are constructed by searching for the shortest SWAP path, so that the qubit entangled topology is isomorphic to the actual population geographical network. The qubits encoding population density are used in the computational basis. The measurement was performed, and the binary readout result was reverse-encoded into a normalized population density. The qubits encoding genetic heterozygosity and allele frequency were rotated by Hadamard and then measured to extract population genetic variation information, including allele frequency, genetic heterozygosity, gene frequency fluctuation and genetic drift intensity. Traverse all pairs of qubits encoding ecological parameters and calculate the concurrent entanglement degree between each pair of qubits. The calculation formula is: ,in, These represent the non-negative eigenvalues ​​obtained by spinning the reduced density matrices of two qubits, and are sorted in descending order of value. The reduced density matrix of a qubit refers to the second-order composite quantum density description matrix obtained by extracting any two qubits from the overall multi-qubit system and ignoring all degrees of freedom of the qubits. The larger the value of concurrent entanglement, the stronger the ecological coupling and quantum entanglement among soybean populations. When the concurrent entanglement degree is higher than the preset threshold, it is determined to be a strongly entangled bit pair, and Belki joint measurement is performed to simultaneously obtain the joint probability distribution of the corresponding population density and allele frequency. When the concurrent entanglement degree is lower than the preset threshold, it is determined to be a weakly entangled or independent bit, and a single-qubit measurement is performed; Based on the generation number corresponding to each Trotter step, the average population density and average genetic heterozygosity of each population are recorded; based on the time order, all sampling points are connected sequentially to reconstruct the multi-generation degeneration time series trajectory, and output a high-fidelity multi-generation population degeneration time series trajectory and related degeneration characteristic parameters, including the initial degeneration rate, the time of the accelerated degeneration inflection point, and the half-life of genetic diversity.

[0020] S6: Closed-loop backtracking optimization and risk prediction of quantum circuit parameters based on classical verification. The specific implementation process is as follows: The population degradation rate and genetic diversity loss rate were extracted from the reconstructed quantum simulation degradation trajectory as verification indicators. Construct a simplified classical population dynamics function, specifically in the following form: ; ; in, They represent the first generation and first population density of generations This represents the intrinsic growth rate, referring to the maximum instantaneous growth rate without density constraints. Indicates the variance of environmental selection pressure; They represent the first generation and first Genetic variance of generations This represents the coupling coefficient between selection pressure and genetic heterozygosity, which is a preset proportional constant. Based on the same generational evolution scale as quantum simulation, the simplified classical population dynamics function is solved to obtain the numerical solution of the function at the same time scale, which serves as a benchmark reference sequence for the quantum simulation results. Through formula The timing residuals between the quantum simulation results and the benchmark reference sequence were calculated. ,in, Indicates the first The temporal residuals corresponding to each generation The first quantum simulation obtained The rate of generational degradation or the rate of loss of genetic diversity, This indicates the rate of degradation or loss of genetic diversity corresponding to the baseline reference sequence; Obtain the ecological prediction tolerance error threshold set by S4. If the time-series residuals are all lower than the ecological prediction tolerance error threshold, the quantum simulation results are deemed to have passed the verification. If any time-series residual exceeds the ecological prediction tolerance error threshold, the quantum simulation is determined to have deviated from the reliable range, triggering the quantum circuit parameter backtracking optimization process; The quantum circuit parameter backtracking optimization process is executed automatically based on the following priority order: Trotter step size check: Extract the final single-step evolution time and Trotter decomposition order used in S3, estimate the decomposition error using the Lie-Trotter remainder formula, and if the decomposition error is greater than the preset threshold, halve the step size and re-execute S3 to S6. Error suppression check: Check the goodness of fit of the high-sensitivity and medium-sensitivity groups of key quantum gate circuits in the S4 hierarchical zero-noise extrapolation; if the goodness of fit of any group is lower than the preset threshold, it indicates that the current extrapolation does not adequately characterize the noise response curve and the confidence of the zero-noise limit is insufficient; then for the underfitting groups, add noise scaling factor parameters to the original noise scaling factor set, re-execute the S4 stretching circuit measurement and expand the fitting dataset, and extrapolate again to... Obtain the corrected zero-noise limit; recalculate the total corrected observations and update the population density and genetic heterozygosity trajectories output by the quantum simulation; Goodness-of-fit is calculated based on various noise scaling factors. Below the measured observations The ratio of the sum of squared residuals to the predicted values ​​to the total sum of squared deviations; Measurement statistics check: If the standard error of the total corrected observations in S4 is higher than half of the ecological prediction tolerance error threshold, it indicates that the current measurement statistics are insufficient. In this case, the number of measurements in S5 will be doubled and S5 will be executed again. After completing the backtracking optimization process steps, the complete process from S3 to S6 is rerun with the corrected circuit parameters and measurement configuration. The updated quantum simulation average population degradation rate and genetic diversity loss rate are calculated, and the temporal residuals are compared again with the benchmark reference sequence output by the simplified classical population dynamics function. The closed-loop verification and backtracking optimization process is repeated until the temporal residuals of the degradation rate and genetic diversity loss rate are both lower than the ecological prediction tolerance error threshold. The verified degradation trajectory data is input into the preset population extinction risk prediction model, which outputs the confidence interval of the extinction risk time and the ranking of degradation driving factors, along with key hardware statistics of this quantum circuit execution, including the total number of gates, circuit depth, actual gate fidelity, total number of trotter steps, and final single-step evolution time.

[0021] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A quantum simulation method for the process of degeneration of wild soybean populations, characterized by, include: S1: Collect core ecological parameters of wild soybean, divide them into fast-changing and slow-changing parameters, and map them to quantum bit groups respectively. Allocate the number of coding bits based on the contribution sensitivity of degradation index, and establish initial entanglement based on the correlation coefficient of ecological parameters. S2: Construct the total ecological Hamiltonian describing the dynamics of wild soybean population degradation, and calculate the comprehensive ranking weight of ecological sensitivity and hardware noise cumulative error for each Hamiltonian quantum term. Decompose the terms in descending order of weight and map them to a quantum gate sequence. S3: Apply higher-order Trotter decomposition to Hamiltonian terms corresponding to fast-varying parameters and lower-order decomposition to terms corresponding to slow-varying parameters; calculate the coherence security factor and adjust the evolution step size; remap the quantum gate based on the bit coherence remaining time; S4: Based on the comprehensive ranking weight, the quantum gate circuits corresponding to Hamiltonian quantum terms are divided into critical quantum circuits and non-critical quantum circuits, and hierarchical error suppression is performed on the quantum circuits. S5: Obtain the population gene flow adjacency matrix and form an isomorphic entangled structure based on hardware coupled graph topological mapping; Calculate concurrent entanglement degree and determine strong and weak entanglement to reconstruct the trajectory of multi-generation degenerate time series; S6: Based on the simplified classical population dynamics function, the quantum simulation results are subjected to time residual verification and backtracking optimization of quantum circuit parameters.

2. The quantum simulation method for the process of wild soybean population degradation according to claim 1, characterized in that, The specific operation steps of S1 are as follows: Core ecological parameters of wild soybean populations were collected and classified into fast-changing and slow-changing ecological parameters based on their change cycles. By constructing a hardware coherence-parameter variation matching strategy through the decoherence time of qubits, fast-changing ecological parameters are mapped to high-quality qubit groups, and slow-changing ecological parameters are mapped to ordinary-quality qubit groups. Calculate the sensitivity of each ecological parameter to the population degradation index, and allocate the number of coding bits differently based on the level of sensitivity. Based on the Pearson correlation coefficient between ecological parameters, a multi-qubit controlled rotation gate is used to establish initial entanglement between different qubit groups, so that the degree of entanglement is positively correlated with the ecological correlation of parameters; by integrating fast-changing parameter branches, slow-changing parameter branches and auxiliary qubits, the overall preparation of hierarchical quantum initial states is completed.

3. The quantum simulation method for the process of wild soybean population degradation according to claim 1, characterized in that, The specific operation steps of S2 are as follows: Construct the total ecological Hamiltonian, which includes cross-coupled terms such as intraspecific competition, genetic drift, environmental selection pressure, and non-commutation characteristics; Using population degradation indicators as the evaluation object, the ecological sensitivity of each Hamiltonian quantum term is calculated, the average fidelity of the basic quantum gate of the pre-collected quantum processor is calculated, and the cumulative error of hardware noise after the decomposition of each sub-term is estimated. The comprehensive ranking weight is calculated based on ecological sensitivity and noise error. The Hamiltonian quantum terms are then arranged in descending order based on the comprehensive ranking weight. The time evolution operator decomposition is performed on the sorted non-commutative Hamiltonians using the symmetric Lie-Trotter approximation method. The time evolution operators obtained from the decomposition are mapped one by one to the basic quantum gate combination sequence to form an optimized sorted quantum gate sequence.

4. The quantum simulation emulation method for the process of wild soybean population degradation according to claim 1, characterized in that, The specific operation steps of S3 are as follows: Before executing the quantum simulation, obtain the decoherence time of the qubit and the average fidelity of the two-qubit gate, and establish a hardware coherence and noise parameter library; Based on the hierarchical quantum encoding results of S1, differential Trotter decomposition is performed on the Hamiltonian quantum terms: For the Hamiltonian quantum terms corresponding to rapidly varying parameters, a fourth-order Suzuki-Trotter decomposition is used; for the Hamiltonian quantum terms corresponding to slowly varying parameters, a first-order Trotter decomposition is used. Real-time monitoring of the minimum decoherence time of qubits, calculation of coherence security factor, adjustment of single-step evolution duration and acquisition of total Trotter steps; Before each evolution step, qubit remapping is performed based on the bit coherence remaining time, and high-weight, high-frequency quantum gates are preferentially allocated to the physical bits with the longest coherence remaining time. During the simulation, the quantum bit coherence state is recalibrated based on a preset period. If the decoherence time decay exceeds the threshold, the hardware parameter library is updated and the step size calculation and bit remapping process is automatically restarted.

5. The quantum simulation emulation method for the process of wild soybean population degradation according to claim 1, characterized in that, The specific operation steps of S4 are as follows: Based on the comprehensive sorting weight of Hamiltonian quantum terms, critical and non-critical quantum gate circuits are divided; Zero-noise extrapolation processing based on ecological sensitivity classification is performed on key quantum gate circuits. A noise scaling factor is selected and the circuit noise is stretched using digital gate expansion technology. A hierarchical fitting network is constructed based on high-sensitivity and medium-sensitivity groups and extrapolated to the zero-noise limit. Non-critical quantum gate circuits are treated using the probability error elimination method of quantum process tomography; The total correction observation is obtained by weighted fusion of the critical circuit correction results and the non-critical circuit elimination results. Set an ecological prediction tolerance error threshold, and use a resampling method to evaluate the fusion results. If the evaluation result is higher than the ecological prediction tolerance error threshold, the measurement configuration will be automatically optimized and adjusted, including increasing the noise scaling factor density of zero-noise extrapolation or increasing the sampling number of probability error elimination and tomographic measurement, until the evaluation result is lower than the ecological prediction tolerance error threshold.

6. The quantum simulation emulation method for the process of wild soybean population degradation according to claim 1, characterized in that, The specific operation steps of S5 are as follows: The adjacency matrix of the gene flow of wild soybean populations was obtained, and topological mapping was performed based on the physical coupling graph of the quantum processor to ensure that the entangled structure of the qubits and the coupling relationship of the population remain isomorphic. Measurements were performed on the corresponding basis vectors for the qubits encoding population density, genetic heterozygosity, and allele frequency, and the relevant information on population density and genetic diversity was obtained by reading and decoding. Traverse all pairs of qubits encoding ecological parameters, calculate the concurrent entanglement degree of each pair of qubits, and classify them into strongly entangled pairs and weakly entangled bits based on the degree of entanglement. Perform Belli joint measurement on strongly entangled pairs and single-bit measurement on weakly entangled bits. Based on the population density and genetic heterozygosity data of each population recorded in evolutionary generations, the sampling points are connected sequentially in chronological order to reconstruct the time series trajectory of multi-generational degradation of wild soybean populations, and the degradation trajectory and degradation characteristic parameters are output.

7. The quantum simulation emulation method for the process of wild soybean population degradation according to claim 1, characterized in that, The specific operation steps of S6 are as follows: Population degradation rate and genetic diversity loss rate were extracted from quantum simulation degradation trajectories as verification indicators; A simplified classical population dynamics function is constructed and a numerical solution of the function at the same generational evolution scale as the quantum simulation is output, which serves as a benchmark reference sequence for the quantum simulation results; Calculate the temporal residuals between the quantum simulation results and the benchmark reference sequence. If the temporal residuals are all below the ecological prediction tolerance error threshold, the quantum simulation results are deemed to have passed the verification. If any time-series residual exceeds the ecological prediction tolerance error threshold, then circuit parameter backtracking optimization is triggered. The circuit parameter backtracking optimization sequentially performs step size checks, error suppression checks, and measurement statistics checks. After completing the backtracking optimization process steps, the complete process from S3 to S6 is rerun with the corrected circuit parameters and measurement configuration until the temporal residuals of degradation rate and genetic diversity loss rate are both lower than the ecological prediction tolerance error threshold. The validated trajectory data is input into the risk prediction model, which outputs the confidence interval for population extinction risk, the ranking of degradation driving factors, and statistical information on key hardware for quantum hardware execution.

Images