A centrifuge parameter optimization simulation software system

Abstract

The present application relates to the technical field of numerical simulation, in particular to a centrifuge parameter optimization simulation software system, comprising a quantum-classical hybrid data preprocessing unit, a quantum computing simulation and model construction unit, a quantum optimization algorithm execution unit, a quantum-classical hybrid simulation verification unit, and a quantum knowledge base and dynamic learning unit. The present application realizes efficient search of a multivariable parameter space and rapid convergence of 15-dimensional parameters by synergistically running a variational quantum feature solver, a quantum approximate optimization algorithm, and a quantum gradient descent and classical ADAM algorithm fusion, improves global optimization efficiency, and, with the aid of dynamic task scheduling, cross-scale feature fusion, and error mutual feedback optimization, guarantees the accuracy of the synergistic simulation of the nanometer to micrometer separation process and corrects the quantum computing decoherence error.

Description

Technical Field

[0001] This invention relates to the field of numerical simulation technology, specifically to a centrifuge parameter optimization simulation software system. Background Technology

[0002] In the fields of biomedicine, nanomaterials, and chemical engineering, centrifuges are core equipment used to separate, purify, or concentrate mixtures (including solid-liquid and liquid-liquid systems) using centrifugal force. Their working principle is to generate centrifugal acceleration through high-speed rotation, causing substances with different densities, particle sizes, or sedimentation coefficients to move differently in the centrifugal field, thereby achieving component separation. Typical applications include blood component separation, nanoparticle purification, and cell collection.

[0003] However, in practical applications, traditional centrifuge parameter optimization faces significant technical bottlenecks: on the one hand, the centrifugation process involves the coupling of multiple dimensions of parameters such as rotational speed, temperature gradient, and centrifugation time, and classical algorithms suffer from low search efficiency and are prone to getting trapped in local optima in high-dimensional parameter spaces; on the other hand, the need for cross-scale simulation (such as the quantum-level simulation of nanoparticle sedimentation trajectories and the classical calculation of macroscopic fluid flow fields) leads to insufficient simulation accuracy, and the unavoidable decoherence error in the quantum computing process further exacerbates the result deviation; in addition, the traditional knowledge management model is difficult to effectively reuse historical optimization experience, resulting in low efficiency in parameter transfer between different centrifuge models.

[0004] Based on this, the present invention provides a centrifuge parameter optimization simulation software system to solve the above-mentioned technical problems. Summary of the Invention

[0005] The purpose of this invention is to provide a centrifuge parameter optimization simulation software system. This invention integrates a collaborative variable quantum feature solver, a quantum approximation optimization algorithm, and quantum gradient descent with the classical ADAM algorithm to achieve efficient multivariable parameter space search and rapid convergence of 15-dimensional parameters, improving global optimization efficiency. Furthermore, through dynamic task scheduling, cross-scale feature fusion, and error feedback optimization, it ensures the accuracy of collaborative simulation of separation processes from nanometer to micrometer scales and corrects quantum computing decoherence errors. Utilizing quantum-enhanced distributed learning and knowledge evolution mechanisms, it achieves cross-platform data collaboration, adaptive task migration, and historical experience quantum state compression updates, providing knowledge support and strategy evolution capabilities for parameter optimization.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] This invention provides a centrifuge parameter optimization simulation software system, comprising a quantum-classical hybrid data preprocessing unit, a quantum computing simulation and model building unit, a quantum optimization algorithm execution unit, a quantum-classical hybrid simulation verification unit, and a quantum knowledge base and dynamic learning unit, wherein:

[0008] The quantum-classical hybrid data preprocessing unit is used to encode the physicochemical properties of biomolecules and nanomaterials into quantum states and to perform parallel dimensionality reduction of high-dimensional data using quantum Fourier transform.

[0009] The quantum computing simulation and model building unit: uses the quantum Monte Carlo method to simulate the diffusion behavior of nanoparticles in a centrifugal field, and constructs a parameterized quantum circuit to describe the cell membrane shear stress model;

[0010] The quantum optimization algorithm execution unit is used to perform efficient search and global optimization of the multivariable parameter space in the centrifugation process by cooperating with a variable quantum feature solver, a quantum approximation optimization algorithm and quantum gradient descent.

[0011] The quantum-classical hybrid simulation verification unit is used to perform collaborative simulation and error feedback optimization of the separation process from nanoscale to micrometer scale through a dynamic quantum-classical task scheduling engine and a cross-scale feature fusion channel.

[0012] The quantum knowledge base and dynamic learning unit are based on a quantum-enhanced distributed learning and knowledge evolution mechanism, enabling cross-platform data collaboration, adaptive task migration, and continuous retention of historical experience.

[0013] The quantum-classical hybrid data preprocessing unit includes a quantum state encoding module, a quantum Fourier transform module, and a classical-quantum interface module, wherein:

[0014] The quantum state encoding module is used to map the physicochemical properties of biomolecules and nanomaterials into the amplitude and phase of quantum bits.

[0015] The quantum Fourier transform module is used to extract features and reduce dimensionality of high-dimensional data through quantum Fourier transform, and to compress redundant information.

[0016] The classical-quantum interface module is used to enable bidirectional interaction between quantum state data and classical storage systems, supporting hybrid computing pipelines.

[0017] The quantum computing simulation and model building unit includes a quantum Monte Carlo simulation module, a parameterized quantum circuit construction module, and a multiphysics coupling module, wherein:

[0018] The quantum Monte Carlo simulation module simulates the Brownian motion and diffusion behavior of nanoparticles in a centrifugal field based on the quantum random walk algorithm.

[0019] The parameterized quantum circuit construction module is used to construct a cell membrane shear stress model through a 4-layer parameterized quantum circuit.

[0020] The multiphysics coupling module is used to quantize the Navier-Stokes equations and perform multi-field coupling simulations of centrifugal fields, temperature fields, and concentration fields.

[0021] The quantum optimization algorithm execution unit includes a variational quantum feature solver module, a quantum approximation optimization algorithm module, and a hybrid gradient module, wherein:

[0022] The variable quantum feature solver module is used to optimize the Hamiltonian ground state of the centrifugal acceleration-time parameter through the variable quantum feature solver.

[0023] The quantum approximation optimization algorithm module is used to solve the multi-objective Pareto front for temperature gradient and rotational speed using a quantum approximation optimization algorithm.

[0024] The hybrid gradient module is used to combine quantum gradient descent with the classical ADAM algorithm for fast convergence in the 15-dimensional parameter space.

[0025] The quantum approximation optimization algorithm module uses a quantum approximation optimization algorithm to solve for the multi-objective Pareto front of temperature gradient and rotational speed. The specific operation is as follows:

[0026] A1: Problem coding: Temperature gradient The optimization objective of rotational speed ω is transformed into a weighted Hamiltonian:

[0027]

[0028] In the formula, α∈[0,1], and H ω These are the Hamiltonian costs associated with the temperature gradient and rotational speed, respectively.

[0029] A2: QAOA Execution: Constructing a parameterized quantum circuit containing p-layer unitary operators:

[0030]

[0031] In the formula, H M For mixing Hamiltonian, These are the parameters to be optimized.

[0032] A3: Frontier Generation: Obtain the Pareto optimal solution set by scanning the weight α, satisfying:

[0033]

[0034] In the formula, Let st represent the set of all solutions obtained by scanning α, and let st denote the "subjectto" constraint.

[0035] The hybrid gradient module combines quantum gradient descent with the classical ADAM algorithm to achieve fast convergence in the 15-dimensional parameter space. The specific operation is as follows:

[0036] B1: Quantum gradient calculation: Calculating the gradient of the expected value of a quantum circuit using the parameter shifting rule.

[0037]

[0038] In the formula, For parameter θ i The corresponding quantum gradient, h(θ), is the Hamiltonian of the quantum circuit containing the parameter θ. i Let i be the i-th parameter to be optimized;

[0039] B2: Classical Momentum Correction: The quantum gradient is input into the ADAM optimizer, and the parameter update formula for the ADAM optimizer is as follows:

[0040]

[0041] in:

[0042] (First moment deviation correction)

[0043] (First moment deviation correction)

[0044] m t =β1m t-1 +(1-β1)g t (First moment of gradient)

[0045] (Second moment of gradient)

[0046] In the formula, η is the learning rate, and g t Let β1 and β2 be the quantum gradient values ​​of the t-th iteration, and β1 and β2 be the momentum decay coefficients, where β1 = 0.9 and β2 = 0.999.

[0047] B3: Hybrid Convergence Criterion: When three consecutive iterations satisfy... The optimization terminates when the time is reached, where L(θ) is the loss function value.

[0048] The quantum-classical hybrid simulation verification unit includes a dynamic task scheduling engine module, a cross-scale feature fusion module, and an error mutual feedback optimization module, wherein:

[0049] The dynamic task scheduling engine module: calculates task weights in real time based on quantum annealing and dynamically allocates quantum and classical computing resources;

[0050] The cross-scale feature fusion module is used to align and fuse the data of nanoparticle sedimentation trajectories predicted by quantum computing with the fluid flow field simulated by classical CFD.

[0051] The error feedback optimization module is used to dynamically adjust model parameters and correct decoherence errors in quantum computing by comparing quantum and classical simulation results.

[0052] The error feedback optimization module dynamically adjusts model parameters and corrects decoherence errors in quantum computing by comparing quantum and classical simulation results. The specific operations are as follows:

[0053] C1: Result Comparison:

[0054] Computational quantum prediction of nanoparticle sedimentation trajectory q i Compared with the results of classical CFD simulations c i Root mean square error (RMSE):

[0055]

[0056] In the formula, n is the number of samples, q i and c i These are the particle positions at the i-th time step;

[0057] When the RMSE exceeds the dynamic threshold T1, the parameter correction process is triggered. The formula for calculating T1 is:

[0058]

[0059] In the formula, σ exp Let σ be the standard deviation of the experimental data. sim The standard deviation of the simulation results;

[0060] C2: Parameter Adjustment:

[0061] Update the parameters θ of the quantum computing model using a Bayesian optimization algorithm:

[0062]

[0063] In the formula, D represents experimental data or high-confidence simulation results, and α represents the learning rate;

[0064] The key adjustment focuses on parameters that affect the consistency of cross-scale simulations, including the nanoparticle diffusion coefficient D. p The fluid viscosity μ is adjusted using the following formula:

[0065]

[0066] In the formula, β and γ are adjustment coefficients, 0.05≤β, γ≤0.2;

[0067] C3: Decoherence error correction:

[0068] The decoherence error ∈ is estimated using zero-noise extrapolation techniques.

[0069] ∈=f(λ1,λ2,…,λ m )·[E(λ1)-E(λ2)]

[0070] In the formula, λ i E(λ) is the noise scaling factor. i ) represents the noise intensity λ i The expected value at time f is the extrapolation function;

[0071] Error compensation is achieved by inserting a virtual quantum gate U(∈), and the specific formula is as follows:

[0072]

[0073] In the formula, For the primitive quantum circuit, U(∈) represents the error compensation operation.

[0074] The quantum knowledge base and dynamic learning unit include a QLSTM memory module, a policy transfer module, and a knowledge distillation module, wherein:

[0075] The QLSTM memory module is used to extract spatiotemporal features of cross-platform process data using quantum long short-term memory networks.

[0076] The strategy transfer module is used to adaptively adjust the parameter recommendation strategy for different centrifuge models using a quantum strategy gradient algorithm.

[0077] The knowledge distillation module is used to compress historical optimization experience into an updatable quantum state density matrix.

[0078] The knowledge distillation module compresses historical optimization experience into an updatable quantum state density matrix, as follows:

[0079] (a) Historical data encoding: Mapping historical process parameters and their corresponding optimization target values ​​to quantum characteristic states:

[0080]

[0081] In the formula, τ is the adjustable temperature coefficient, N is the size of the historical dataset, and θ i For the i-th set of historical process parameters, L i Corresponding to θ i The optimization objective value, |θ i >For parameter θ i The quantum encoded state;

[0082] (b) Variable quantum compression: The quantum state density matrix is ​​optimized by parameterizing quantum circuits to minimize the following loss function:

[0083]

[0084] In the formula, S(·‖·) is the quantum relative entropy, and λ is the regularization coefficient;

[0085] (c) Dynamic update trigger: When new data (θ) is triggered... new ,L new The fidelity of the current density matrix ρ satisfies F(ρ,|ψ) new ><ψ new When |) < κ, repeat steps (a)-(b) to update the knowledge, where κ is the preset threshold and fidelity is 0.

[0086] Compared with the prior art, the beneficial effects of the present invention are:

[0087] This invention achieves efficient multivariable parameter space search and rapid convergence of 15-dimensional parameters by integrating a collaborative variable quantum feature solver, a quantum approximation optimization algorithm, and quantum gradient descent with the classical ADAM algorithm, thereby improving global optimization efficiency. Furthermore, it ensures the accuracy of collaborative simulation of the separation process from nanometer to micrometer scale and corrects quantum computing decoherence errors by leveraging dynamic task scheduling, cross-scale feature fusion, and error feedback optimization. Finally, it utilizes quantum-enhanced distributed learning and knowledge evolution mechanisms to complete cross-platform data collaboration, adaptive task migration, and historical experience quantum state compression and update, providing knowledge support and strategy evolution capabilities for parameter optimization. Attached Figure Description

[0088] Figure 1 This is a system diagram of a centrifuge parameter optimization simulation software system according to the present invention.

[0089] Figure 2 This is a system architecture diagram of a centrifuge parameter optimization simulation software system according to the present invention.

[0090] Figure 3 This is a flowchart of error mutual feedback in a centrifuge parameter optimization simulation software system of the present invention.

[0091] Explanation of icon numbers:

[0092] 100. Quantum-Classical Hybrid Data Preprocessing Unit; 101. Quantum State Encoding Module; 102. Quantum Fourier Transform Module; 103. Classical-Quantum Interface Module; 200. Quantum Computing Simulation and Model Building Unit; 201. Quantum Monte Carlo Simulation Module; 202. Parametric Quantum Circuit Construction Module; 203. Multiphysics Coupling Module; 300. Quantum Optimization Algorithm Execution Unit; 301. Variable Quantum Feature Solver Module; 302. Quantum Approximate Optimization Algorithm Module; 303. Hybrid Gradient Module; 400. Quantum-Classical Hybrid Simulation Verification Unit; 401. Dynamic Task Scheduling Engine Module; 402. Cross-Scale Feature Fusion Module; 403. Error Mutual Feedback Optimization Module; 500. Quantum Knowledge Base and Dynamic Learning Unit; 501. QLSTM Memory Module; 502. Policy Transfer Module; 503. Knowledge Distillation Module. Detailed Implementation

[0093] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0094] Example:

[0095] like Figures 1-3 As shown, this embodiment provides a centrifuge parameter optimization simulation software system, including a quantum-classical hybrid data preprocessing unit 100, a quantum computing simulation and model building unit 200, a quantum optimization algorithm execution unit 300, a quantum-classical hybrid simulation verification unit 400, and a quantum knowledge base and dynamic learning unit 500. Specifically: the quantum-classical hybrid data preprocessing unit 100 encodes the physicochemical properties of biomolecules and nanomaterials into quantum states and uses quantum Fourier transform for parallel dimensionality reduction of high-dimensional data; the quantum computing simulation and model building unit 200 uses the quantum Monte Carlo method to simulate the diffusion behavior of nanoparticles in a centrifugal field and constructs a parameterized... The quantum circuit describes the cell membrane shear stress model; the quantum optimization algorithm execution unit 300 is used to efficiently search and globally optimize the multivariable parameter space in the centrifugation process by cooperating with a variable quantum feature solver, a quantum approximation optimization algorithm, and quantum gradient descent; the quantum-classical hybrid simulation verification unit 400 is used to perform cooperating simulation and error feedback optimization of the separation process from nanoscale to micrometer scale through a dynamic quantum-classical task scheduling engine and a cross-scale feature fusion channel; the quantum knowledge base and dynamic learning unit 500 is used to perform cross-platform data collaboration, adaptive task migration, and continuous retention of historical experience based on a quantum-enhanced distributed learning and knowledge evolution mechanism.

[0096] It should be noted that the quantum-classical hybrid data preprocessing unit 100 encodes the physicochemical properties of biomolecules and nanomaterials into quantum states and reduces their dimensionality. Then, the quantum computing simulation and model building unit 200 simulates the behavior of nanoparticles and builds a cell membrane model. The quantum optimization algorithm execution unit 300 then collaboratively optimizes the multivariate parameter space. Finally, the quantum-classical hybrid simulation verification unit 400 realizes cross-scale collaborative simulation and error correction. Finally, the quantum knowledge base and dynamic learning unit 500 complete the knowledge evolution and strategy transfer.

[0097] In this embodiment, it should also be noted that the quantum-classical hybrid data preprocessing unit 100 includes a quantum state encoding module 101, a quantum Fourier transform module 102, and a classical-quantum interface module 103, wherein: the quantum state encoding module 101 is used to map the physicochemical properties of biomolecules and nanomaterials into the amplitude and phase of qubits; the quantum Fourier transform module 102 is used to extract features and reduce the dimensionality of high-dimensional data through quantum Fourier transform, and compress redundant information; the classical-quantum interface module 103 is used to realize bidirectional interaction between quantum state data and classical storage systems, and support hybrid computing pipelines.

[0098] It should be noted that the quantum state encoding module 101 maps the physicochemical properties of biomolecules and nanomaterials to the amplitude and phase of qubits. After high-dimensional data feature extraction and dimensionality reduction by the quantum Fourier transform module 102, the classical-quantum interface module 103 realizes bidirectional data interaction between the quantum state and the classical system.

[0099] Furthermore, it should be noted that the quantum state encoding module 101 converts the physicochemical parameters of biomolecules and nanomaterials into a superposition state of N qubits through amplitude encoding: Where α i This represents the complex amplitude value corresponding to the i-th feature. The quantum Fourier transform module 102 reduces the dimensionality of high-dimensional data (such as the 1000-dimensional surface energy distribution of nanoparticles) through quantum parallelism, compressing redundant features to 10-20 dimensions, and reducing the computational complexity from O(ni) to O(ni). 2 ) decreases to O(log 2 The classical-quantum interface module 103 uses the OpenQASM 3.0 standard to realize bidirectional transmission of quantum state data and classical storage, supporting 10^5 quantum-classical data exchanges per second.

[0100] In this embodiment, it should also be noted that the quantum computing simulation and model building unit 200 includes a quantum Monte Carlo simulation module 201, a parameterized quantum circuit building module 202, and a multiphysics coupling module 203, wherein: the quantum Monte Carlo simulation module 201 simulates the Brownian motion and diffusion behavior of nanoparticles in a centrifugal field based on the quantum random walk algorithm; the parameterized quantum circuit building module 202 is used to build a cell membrane shear stress model through four layers of parameterized quantum circuits; and the multiphysics coupling module 203 is used to quantize the Navier-Stokes equations and perform multi-field coupling simulation of centrifugal field, temperature field, and concentration field.

[0101] It should be noted that the quantum Monte Carlo simulation module 201 simulates the quantum diffusion behavior of nanoparticles, establishes a cell membrane mechanical model through the parameterized quantum circuit construction module 202, and then realizes the quantum coupling calculation of centrifugation field-temperature field-concentration field through the multi-physics field coupling module 203.

[0102] Furthermore, it should be noted that the Quantum Monte Carlo Simulation Module 201, based on Feynman path integral theory, decomposes the Brownian motion of nanoparticles (1-100 nm in diameter) in a centrifugal field into quantum random walks, generating 10^4 paths per simulation round and calculating the probability distribution of particle settling rates. The four-layer parameterized quantum circuit includes tunable quantum gates such as RY and CZ gates, and by optimizing circuit parameters θ1-θ8, it models the probability of cell membrane damage under shear stress.

[0103] In this embodiment, it should also be noted that the quantum optimization algorithm execution unit 300 includes a variable quantum feature solver module 301, a quantum approximation optimization algorithm module 302, and a hybrid gradient module 303, wherein: the variable quantum feature solver module 301 is used to optimize the Hamiltonian ground state of the centrifugal acceleration-time parameter using a variable quantum feature solver; the quantum approximation optimization algorithm module 302 is used to solve the multi-objective Pareto front of temperature gradient and rotational speed using a quantum approximation optimization algorithm; the specific operation is as follows: A1: Problem encoding: convert the temperature gradient The optimization objective of rotational speed ω is transformed into a weighted Hamiltonian:

[0104]

[0105] In the formula, α∈[0,1], and H ω The cost Hamiltonians for temperature gradient and rotational speed are respectively; A2: QAOA execution: Constructing a parameterized quantum circuit with p-layer unitary operators:

[0106]

[0107] In the formula, H MFor mixing Hamiltonian, For the parameters to be optimized; A3: Frontier generation: Obtain the Pareto optimal solution set by scanning the weight α, satisfying:

[0108]

[0109] In the formula, Let be the set of solutions obtained by scanning α, and st denote the "subject to" constraint. Hybrid gradient module 303: used to combine quantum gradient descent with the classical ADAM algorithm for fast convergence in the 15-dimensional parameter space. Specific operations are as follows: B1: Quantum gradient calculation: calculates the gradient of the expected value of the quantum circuit using the parameter shift rule:

[0110]

[0111] In the formula, For parameter θ i The corresponding quantum gradient, H(θ), is the Hamiltonian of the quantum circuit containing the parameter θ. i B1: The i-th parameter to be optimized; B2: Classical momentum correction: Input the quantum gradient into the ADAM optimizer, and the parameter update formula of the ADAM optimizer is:

[0112]

[0113] in:

[0114] (First moment deviation correction)

[0115] (First moment deviation correction)

[0116] m t =β1m t-1 +(1-β1)g t (First moment of gradient)

[0117] (Second moment of gradient)

[0118] In the formula, η is the learning rate, and g t Let be the quantum gradient value of the t-th iteration, and β1 and β2 be the momentum decay coefficients, β1 = 0.9 and β2 = 0.999; B3: Mixed convergence criterion: when three consecutive iterations satisfy ... The optimization terminates when the time is reached, where L(θ) is the loss function value.

[0119] Among them, it should be noted that the variational quantum eigensolver module 301 optimizes the quantum ground state of the centrifugal acceleration-time parameter. The quantum approximate optimization algorithm module 302 uses weighted Hamiltonian encoding and the QAOA circuit to solve the multi-objective Pareto front, and then the hybrid gradient module 303 combines quantum gradient descent and the classical ADAM algorithm to achieve efficient convergence of 15-dimensional parameters.

[0120] Furthermore, it should be noted that for the optimization of the centrifugal acceleration a and time, the variational quantum eigensolver module 301 constructs the Hamiltonian H = w1H a + w2H t , where H a = (a - a t arget) 2 |a><a|, and the ground state is solved through the VQE algorithm to find the optimal balance point of energy consumption-separation efficiency.

[0121] In this embodiment, it should also be noted that the quantum-classical hybrid simulation verification unit 400 includes a dynamic task scheduling engine module 401, a cross-scale feature fusion module 402, and an error mutual feedback optimization module 403, where: Dynamic task scheduling engine module 401: Based on the real-time calculation of task weights by quantum annealing, dynamically allocate quantum and classical computing resources; Cross-scale feature fusion module 402: Used to align and fuse the data of the nanoparticle sedimentation trajectory predicted by quantum computing and the fluid flow field simulated by classical CFD; Error mutual feedback optimization module 403: Used to dynamically adjust the model parameters and correct the decoherence error in quantum computing by comparing the quantum and classical simulation results. The specific operations are as follows: C1: Result comparison: Calculate the root mean square error RMSE between the nanoparticle sedimentation trajectory q i predicted by quantum and the classical CFD simulation result c i :

[0122]

[0123] where n is the number of samples, and q i and c i are the particle positions at the i-th time step respectively; when the RMSE exceeds the dynamic threshold T1, trigger the parameter correction process, and the calculation formula of T1 is:

[0124]

[0125] where σ exp is the standard deviation of experimental data, and σ sim is the standard deviation of simulation results; C2: Parameter adjustment: Update the quantum computing model parameters θ through the Bayesian optimization algorithm:

[0126]

[0127] In the formula, D represents experimental data or high-confidence simulation results, and α represents the learning rate; the focus is on adjusting parameters that affect the consistency of cross-scale simulations, including the nanoparticle diffusion coefficient D. p The fluid viscosity μ is adjusted using the following formula:

[0128]

[0129] In the formula, β and γ are adjustment coefficients, 0.05≤β, γ≤0.2; C3: Decoherence error correction: The decoherence error ∈ is estimated using zero-noise extrapolation technique:

[0130] ∈=f(λ1,λ2,…,λ m )·[E(λ1)-E(λ2)]

[0131] In the formula, λ i E(λ) is the noise scaling factor. i ) represents the noise intensity λ i The expected value at time f is the extrapolation function; error compensation is performed by inserting a virtual quantum gate U(∈), and the specific formula is as follows:

[0132]

[0133] In the formula, For the primitive quantum circuit, U(∈) represents the error compensation operation.

[0134] It should be noted that the dynamic task scheduling engine module 401 intelligently allocates computing resources, and the cross-scale feature fusion module 402 realizes the data alignment and feature fusion of nanoscale quantum simulation and micrometer-scale classical CFD. Then, the error mutual feedback optimization module 403 performs quantum-classical result comparison, dynamic parameter adjustment and decoherence error correction.

[0135] Furthermore, it should be noted that the task weight matrix is ​​solved based on the quantum annealing algorithm. For example, the calculation of nanoparticle sedimentation trajectory is assigned to the quantum processor (weight 0.8), and the fluid flow field simulation is assigned to the classical CPU (weight 0.2). The task assignment delay is <10ms.

[0136] In this embodiment, it should also be noted that the quantum knowledge base and dynamic learning unit 500 includes a QLSTM memory module 501, a policy transfer module 502, and a knowledge distillation module 503, wherein: the QLSTM memory module 501 is used to extract spatiotemporal features of cross-platform process data using a quantum long short-term memory network; the policy transfer module 502 is used to adaptively adjust the parameter recommendation strategy for different centrifuge models through a quantum policy gradient algorithm; and the knowledge distillation module 503 is used to compress historical optimization experience into an updatable quantum state density matrix. The specific operations are as follows: (a) Historical data encoding: Mapping historical process parameters and their corresponding optimization target values ​​to quantum feature states:

[0137]

[0138] In the formula, τ is the adjustable temperature coefficient, N is the size of the historical dataset, and θ i For the i-th set of historical process parameters, L i Corresponding to θ i The optimization objective value, |θ i >For parameter θ i (a) Quantum encoded states; (b) Variable quantum compression: Optimize the quantum state density matrix through parameterized quantum circuits to minimize the following loss function:

[0139]

[0140] In the formula, S(·‖·) is the quantum relative entropy, and λ is the regularization coefficient; (c) Dynamic update trigger: when new data (θ) new ,L new The fidelity of the current density matrix ρ satisfies F(ρ,|ψ) new ><ψ new When |) < κ, repeat steps (a)-(b) to update the knowledge, where κ is the preset threshold and fidelity is 0.

[0141] It should be noted that the QLSTM memory module 501 extracts the spatiotemporal features of the process data, the strategy transfer module 502 realizes the adaptive transfer of the parameter recommendation strategy, and the knowledge distillation module 503 compresses the historical experience into an updatable quantum state density matrix.

[0142] Furthermore, it should be noted that the QLSTM memory module 501 employs a recursive quantum circuit structure incorporating a quantum gating mechanism, enabling it to model long-term dependencies in time-series data. Its input is a discretized sequence of time-step process parameters, and its output is the hidden-state quantum representation at the corresponding time step, used for subsequent policy generation and feature extraction.

[0143] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0144] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A centrifuge parameter optimization simulation software system, characterized by, The quantum-classical hybrid data preprocessing unit (100), the quantum computing simulation and model construction unit (200), the quantum optimization algorithm execution unit (300), the quantum-classical hybrid simulation verification unit (400), and the quantum knowledge base and dynamic learning unit (500), wherein: The quantum-classical hybrid data preprocessing unit (100) is used for encoding the physicochemical properties of biomolecules and nanomaterials into quantum states, and performing high-dimensional data parallel dimension reduction by using quantum Fourier transform; The quantum computing simulation and model construction unit (200) adopts a quantum Monte Carlo method to simulate the diffusion behavior of nanoparticles in a centrifugal field, and constructs a parameterized quantum circuit to describe a cell membrane shear stress model; The quantum optimization algorithm execution unit (300) is used for efficiently searching and globally optimizing the multivariate parameter space in the centrifugal process by cooperatively running a variational quantum feature solver, a quantum approximate optimization algorithm, and a quantum gradient descent; The quantum-classical hybrid simulation verification unit (400) is used for cooperatively simulating the nanoscale to microscale separation process and error mutual feedback optimization by using a dynamic quantum-classical task scheduling engine and a cross-scale feature fusion channel; The quantum knowledge base and dynamic learning unit (500) performs cross-platform data cooperation, task adaptive migration, and historical experience continuous reservation based on a quantum-enhanced distributed learning and knowledge evolution mechanism; The quantum computing simulation and model construction unit (200) includes a quantum Monte Carlo simulation module (201), a parameterized quantum circuit construction module (202), and a multi-physical field coupling module (203), wherein: The quantum Monte Carlo simulation module (201) simulates the Brownian motion and diffusion behavior of nanoparticles in a centrifugal field based on a quantum random walk algorithm; The parameterized quantum circuit construction module (202) is used for constructing a cell membrane shear stress model by using a 4-layer parameterized quantum circuit; The multi-physical field coupling module (203) is used for quantumizing the Navier-Stokes equation to perform multi-field coupling simulation of the centrifugal field, temperature field, and concentration field; The quantum optimization algorithm execution unit (300) includes a variational quantum feature solver module (301), a quantum approximate optimization algorithm module (302), and a hybrid gradient module (303), wherein: The variational quantum feature solver module (301) is used for optimizing the Hamiltonian ground state of the centrifugal acceleration-time parameter by using a variational quantum feature solver; The quantum approximate optimization algorithm module (302) is used for solving the multi-objective Pareto frontier of the temperature gradient and the rotational speed by using a quantum approximate optimization algorithm; The hybrid gradient module (303) is used for combining quantum gradient descent and classical ADAM algorithm to quickly converge the 15-dimensional parameter space.

2. The centrifuge parameter optimization simulation software system of claim 1, wherein, The quantum-classical hybrid data preprocessing unit (100) includes a quantum state encoding module (101), a quantum Fourier transform module (102), and a classical-quantum interface module (103), wherein: The quantum state encoding module (101) is used for mapping the physicochemical properties of biomolecules and nanomaterials into quantum bit amplitudes and phases. The quantum Fourier transform module (102) is used for feature extraction and dimensionality reduction of high-dimensional data through quantum Fourier transform, and compression of redundant information. The classical-quantum interface module (103) is used for realizing the bidirectional interaction of quantum state data and classical storage systems, and supporting hybrid computing pipelines.

3. The centrifuge parameter optimization simulation software system of claim 1, wherein, In the quantum approximate optimization algorithm module (302), a quantum approximate optimization algorithm is used to solve the multi-objective Pareto frontier of temperature gradient and rotating speed, and the specific operation is as follows:

4. The centrifuge parameter optimization simulation software system of claim 1, wherein, In the hybrid gradient module (303), quantum gradient descent and classical ADAM algorithm are combined to realize fast convergence of 15-dimensional parameter space, and the specific operation is as follows:

5. The centrifuge parameter optimization simulation software system of claim 1, wherein, The quantum-classical hybrid simulation verification unit (400) includes a dynamic task scheduling engine module (401), a cross-scale feature fusion module (402), and an error mutual feedback optimization module (403), wherein: The dynamic task scheduling engine module (401) calculates the task weight in real time based on quantum annealing, and dynamically allocates quantum and classical computing resources; The cross-scale feature fusion module (402) is used for data alignment and feature fusion of the nanoparticle settling trajectory predicted by quantum computing and the fluid flow field simulated by classical CFD; The error mutual feedback optimization module (403) is used for dynamically adjusting model parameters and correcting decoherence errors in quantum computing by comparing quantum and classical simulation results.

6. A centrifuge parameter optimization simulation software system according to claim 5, wherein, In the error mutual feedback optimization module (403), the quantum and classical simulation results are compared, the model parameters are dynamically adjusted, and the decoherence error in quantum computing is corrected, and the specific operation is as follows:

7. The centrifuge parameter optimization simulation software system of claim 1, wherein, The quantum knowledge base and dynamic learning unit (500) includes a QLSTM memory module (501), a strategy migration module (502), and a knowledge distillation module (503), wherein: The QLSTM memory module (501) is used for spatiotemporal feature extraction of cross-platform process data using a quantum long short-term memory network; The strategy migration module (502) is used for adaptively adjusting the parameter recommendation strategy of different centrifuge models through a quantum policy gradient algorithm; The knowledge distillation module (503) is used for compressing historical optimization experience into an updateable quantum state density matrix.

8. A centrifuge parameter optimization simulation software system according to claim 7, wherein, In the knowledge distillation module (503), the historical optimization experience is compressed into an updateable quantum state density matrix, and the specific operation is as follows:

Images