Centrifugal machine parameter optimization simulation software system

Abstract

The invention relates to the technical field of numerical simulation, in particular to a centrifugal machine parameter optimization simulation software system which comprises a quantum-classical mixed data preprocessing unit, a quantum calculation simulation and model building unit, a quantum optimization algorithm execution unit, a quantum-classical mixed simulation verification unit and a quantum knowledge base and dynamic learning unit. According to the method, the variable component sub-feature solver, the quantum approximation optimization algorithm and the quantum gradient descent are cooperatively operated and fused with the classical ADAM algorithm, efficient search of a multivariable parameter space and rapid convergence of 15-dimensional parameters are achieved, the global optimization efficiency is improved, and by means of dynamic task scheduling, cross-scale feature fusion and error mutual feedback optimization, the algorithm is more efficient and efficient. The collaborative simulation precision of the separation process from the nanoscale to the micron is guaranteed, and quantum calculation de-coherence errors are corrected.

Description

Technical Field

[0001] The invention relates to the technical field of numerical simulation, in particular to a centrifuge parameter optimization simulation software system. Background Art

[0002] In the fields of biomedicine, nanomaterials, chemical industry, etc., centrifuges are core equipment that use centrifugal force to separate, purify or concentrate mixed liquids (including solid-liquid, liquid-liquid and other systems). Their working principle is to generate centrifugal acceleration through high-speed rotation, so that substances with different densities, particle sizes or sedimentation coefficients produce differentiated movements in the centrifugal field, thereby achieving component separation. Typical applications include blood component separation, nanoparticle purification, cell collection and other scenarios.

[0003] However, in practical applications, traditional centrifuge parameter optimization faces significant technical bottlenecks: on the one hand, the centrifugation process involves the coupling of multi-dimensional parameters such as rotational speed, temperature gradient, and centrifugation time. Classical algorithms have low search efficiency and are prone to falling into local optimality in high-dimensional parameter space; on the other hand, cross-scale simulation requirements (such as the coordination of quantum-level simulation of nanoparticle sedimentation trajectories and classical calculations of macroscopic fluid flow fields) lead to insufficient simulation accuracy, and the inevitable decoherence errors in the quantum computing process further aggravate the result deviation; in addition, traditional knowledge management models make it difficult to effectively reuse historical optimization experience, resulting in inefficient parameter migration of 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 present invention aims to provide a centrifuge parameter optimization simulation software system. This system synergizes a variational quantum feature solver, a quantum approximate optimization algorithm, and quantum gradient descent with the classical ADAM algorithm to achieve efficient multivariable parameter space search and rapid 15-dimensional parameter convergence, improving global optimization efficiency. Furthermore, through dynamic task scheduling, cross-scale feature fusion, and error mutual feedback optimization, it ensures the accuracy of collaborative simulations of nanoscale to micron-scale separation processes and corrects quantum computation decoherence errors. Leveraging quantum-enhanced distributed learning and knowledge evolution mechanisms, it enables cross-platform data collaboration, adaptive task migration, and compressed updates of historical quantum states, providing knowledge support and strategy evolution capabilities for parameter optimization.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The present invention provides a centrifuge parameter optimization simulation software system, including 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, a quantum knowledge base, and a dynamic learning unit, wherein:

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

[0009] The quantum computing simulation and model building unit uses a 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 efficiently search and globally optimize the multivariable parameter space in the centrifugal process by collaboratively running the variational quantum eigensolver, the quantum approximate optimization algorithm, and the quantum gradient descent;

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

[0012] The quantum knowledge base and dynamic learning unit: based on quantum-enhanced distributed learning and knowledge evolution mechanism, carry out cross-platform data collaboration, task adaptive 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 physical and chemical properties of biomolecules and nanomaterials into quantum bit amplitude and phase;

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

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

[0017] The quantum computing simulation and model building unit includes a quantum Monte Carlo simulation module, a parameterized quantum circuit building module, and a multi-physics field coupling module, wherein:

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

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

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

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

[0022] The variational quantum eigensolver module is used to optimize the Hamiltonian ground state of the centrifugal acceleration-time parameter through the variational quantum eigensolver;

[0023] The quantum approximate optimization algorithm module is used to solve the multi-objective Pareto frontier of temperature gradient and speed using the quantum approximate optimization algorithm;

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

[0025] The quantum approximate optimization algorithm module uses the quantum approximate optimization algorithm to solve the multi-objective Pareto frontier of temperature gradient and speed. The specific operations are as follows:

[0026] A1: Question code: The temperature gradient The optimization objective with respect to the speed ω is transformed into a weighted Hamiltonian:

[0027]

[0028] Where, α∈[0,1], and H ω are the cost Hamiltonians for temperature gradient and rotation speed, respectively;

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

[0030]

[0031] Where H M is the mixing Hamiltonian, is the parameter to be optimized;

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

[0033]

[0034] Where, is the set of all solutions obtained by scanning α, and st represents the "subject to" constraint.

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

[0036] B1: Quantum gradient calculation: Use the parameter shift rule to calculate the gradient of the expected value of the quantum circuit:

[0037]

[0038] Where, is the parameter θ i The corresponding quantum gradient, h(θ) is the quantum circuit Hamiltonian with parameter θ, θ i is the i-th parameter to be optimized;

[0039] B2: Classical momentum correction: Input the quantum gradient into the ADAM optimizer. The parameter update formula of the ADAM optimizer is:

[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] Where η is the learning rate, g t is the quantum gradient value of the t-th iteration, β1 and β2 are momentum attenuation coefficients, β1 = 0.9, β2 = 0.999;

[0047] B3: Hybrid convergence judgment: When three consecutive iterations meet The optimization is terminated when , 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 the nanoparticle sedimentation trajectory predicted by quantum computing with the fluid flow field simulated by classical CFD;

[0051] The error mutual 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 mutual 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] Calculating quantum-predicted nanoparticle sedimentation trajectories q i Compared with the classic CFD simulation results i Root mean square error RMSE:

[0055]

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

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

[0058]

[0059] Where, σ exp is the standard deviation of experimental data, σ sim is the standard deviation of the simulation results;

[0060] C2: Parameter adjustment:

[0061] Update the quantum computing model parameters θ through the Bayesian optimization algorithm:

[0062]

[0063] Where D is the experimental data or high-confidence simulation results, and α is the learning rate;

[0064] Focus on adjusting parameters that affect cross-scale simulation consistency, including the nanoparticle diffusion coefficient D p , fluid viscosity μ, the adjustment formula is:

[0065]

[0066] Where β and γ are adjustment coefficients, 0.05≤β, γ≤0.2;

[0067] C3: Decoherence error correction:

[0068] The decoherence error ∈ is estimated using the zero-noise extrapolation technique:

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

[0070] Where λ i is the noise scaling factor, E(λ i ) is the noise intensity λ i The expected value when , f is the extrapolation function;

[0071] Error compensation is performed by inserting a virtual quantum gate U(∈). The specific formula is:

[0072]

[0073] Where, is the original quantum circuit, and U(∈) is the error compensation operation.

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

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

[0076] The strategy migration module is used to adaptively adjust the parameter recommendation strategy of different centrifuge models through the quantum policy gradient algorithm;

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

[0078] The knowledge distillation module compresses historical optimization experience into an updateable quantum state density matrix. The specific operations are as follows:

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

[0080]

[0081] Where τ is the adjustable temperature coefficient, N is the size of the historical data set, and θ i is the historical process parameters of group i, L i Corresponding to θ i The optimization target value, |θ i > is the parameter θ i The quantum coded state of

[0082] (b) Variational quantum compression: Optimize the quantum state density matrix by parameterizing the quantum circuit to minimize the following loss function:

[0083]

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

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

[0086] Compared with the prior art, the present invention has the following beneficial effects:

[0087] The present invention achieves efficient search in multivariable parameter space and rapid convergence of 15-dimensional parameters by collaboratively running a variational quantum feature solver, a quantum approximate optimization algorithm, and a fusion of quantum gradient descent and the classical ADAM algorithm, thereby improving global optimization efficiency. Furthermore, with the help of dynamic task scheduling, cross-scale feature fusion, and error mutual feedback optimization, the collaborative simulation accuracy of the nanoscale to micron-scale separation process is ensured and quantum computing decoherence errors are corrected. By utilizing quantum-enhanced distributed learning and knowledge evolution mechanisms, cross-platform data collaboration, task adaptive migration, and historical experience quantum state compression and update are accomplished, providing knowledge support and strategy evolution capabilities for parameter optimization. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

[0090] Figure 3 The present invention provides a flow chart of mutual error feedback in a centrifuge parameter optimization simulation software system.

[0091] Description of Figure 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. Parameterized quantum circuit building module; 203. Multi-physics field coupling module; 300. Quantum optimization algorithm execution unit; 301. Variational 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 migration module; 503. Knowledge distillation module. DETAILED DESCRIPTION

[0093] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0094] Example:

[0095] like Figure 1-Figure 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, wherein: the quantum-classical hybrid data preprocessing unit 100 is used to encode the physical and chemical properties of biomolecules and nanomaterials into quantum states, and use quantum Fourier transform to perform 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 the centrifugal field and build 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 collaboratively running the variational quantum feature solver, quantum approximate optimization algorithm and quantum gradient descent; the quantum-classical hybrid simulation verification unit 400 is used to perform collaborative simulation and error mutual feedback optimization of the nanoscale to microscale separation process 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 based on quantum-enhanced distributed learning and knowledge evolution mechanisms to perform cross-platform data collaboration, task adaptive migration and continuous retention of historical experience.

[0096] Among them, it should be noted that after the quantum-classical hybrid data preprocessing unit 100 encodes the physical and chemical properties of biological molecules and nanomaterials into quantum states and reduces the dimension, the quantum computing simulation and model construction unit 200 simulates the behavior of nanoparticles and constructs a cell membrane model. The quantum optimization algorithm execution unit 300 collaboratively optimizes the multivariable parameter space, and then 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 migration.

[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 physical and chemical properties of biomolecules and nanomaterials into quantum bit amplitude and phase; the quantum Fourier transform module 102 is used to perform feature extraction and dimensionality reduction on 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 physical and chemical properties of biomolecules and nanomaterials into the amplitude and phase of quantum bits. After the quantum Fourier transform module 102 performs high-dimensional data feature extraction and dimensionality reduction, 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 physical and chemical parameters of biomolecules and nanomaterials into a superposition state of N quantum bits through amplitude encoding: where α i Represents the complex amplitude value corresponding to the i-th dimension feature. The quantum Fourier transform module 102 reduces the dimension 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 the computational complexity is reduced from O(n 2 ) is reduced to O(log 2 n). The classical-quantum interface module 103 uses the OpenQASM 3.0 standard to implement 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 construction unit 200 includes a quantum Monte Carlo simulation module 201, a parameterized quantum circuit construction module 202, and a multi-physics 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 the quantum random walk algorithm; the parameterized quantum circuit construction module 202: is used to construct a cell membrane shear stress model through a 4-layer parameterized quantum circuit; the multi-physics field coupling module 203: is used to quantize the Navier-Stokes equations and perform multi-field coupling simulation of the 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, the parameterized quantum circuit construction module 202 establishes a cell membrane mechanical model, and then the multi-physics field coupling module 203 realizes the quantized coupling calculation of the centrifugal field-temperature field-concentration field.

[0102] Furthermore, it should be noted that the quantum Monte Carlo simulation module 201, based on Feynman's path integral theory, decomposes the Brownian motion of nanoparticles (1-100 nm in diameter) in a centrifugal field into quantum random walks. Each simulation generates 10^4 paths and calculates the probability distribution of the particle sedimentation rate. A four-layer parameterized quantum circuit, including tunable quantum gates such as RY and CZ gates, models the probability of cell membrane damage under shear stress by optimizing circuit parameters θ1-θ8.

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

[0104]

[0105] Where, α∈[0,1], and H ω are the cost Hamiltonians for temperature gradient and rotation speed, respectively; A2: QAOA execution: construct a parameterized quantum circuit containing p-layer unitary operators:

[0106]

[0107] Where H Mis the mixing Hamiltonian, is the parameter to be optimized; A3: Frontier generation: Obtain the Pareto optimal solution set by scanning the weight α, satisfying:

[0108]

[0109] Where, is the set of solutions obtained by scanning α, and st represents the "subject to" constraint. Hybrid Gradient Module 303: This module combines quantum gradient descent with the classical ADAM algorithm for rapid convergence in the 15-dimensional parameter space. The specific operations are as follows: B1: Quantum Gradient Calculation: Calculate the gradient of the expected value of the quantum circuit using the parameter shift rule:

[0110]

[0111] Where, is the parameter θ i The corresponding quantum gradient, H(θ) is the quantum circuit Hamiltonian with parameter θ, θ i is the i-th parameter to be optimized; B2: Classical momentum correction: input the quantum gradient into the ADAM optimizer, 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] Where η is the learning rate, g t is the quantum gradient value of the t-th iteration, β1 and β2 are momentum attenuation coefficients, β1 = 0.9, β2 = 0.999; B3: mixed convergence judgment: when three consecutive iterations meet The optimization is terminated when , 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 frontier, 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 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 data and fuse features between 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 model parameters and correct the decoherence error in quantum computing by comparing 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 : [[ID=​​​​​​​​​​​​​​​​​​​​​​

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

[0128]

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

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

[0131] Where λ i is the noise scaling factor, E(λ i ) is the noise intensity λ i The expected value when , f is the extrapolation function; error compensation is performed by inserting a virtual quantum gate U(∈), the specific formula is:

[0132]

[0133] Where, is the original quantum circuit, and U(∈) is 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 micron-scale classical CFD, and 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 when solving the task weight matrix based on the quantum annealing algorithm, for example, the calculation of nanoparticle sedimentation trajectories 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 strategy migration module 502, and a knowledge distillation module 503, wherein: the QLSTM memory module 501 is used to extract the spatiotemporal features of cross-platform process data using a quantum long short-term memory network; the strategy migration module 502 is used to adaptively adjust the parameter recommendation strategy for different centrifuge models using a quantum policy gradient algorithm; and the knowledge distillation module 503 is used to compress historical optimization experience into an updateable quantum state density matrix. The specific operations are as follows: (a) Historical data encoding: Mapping historical process parameters and their corresponding optimization target values ​​into quantum characteristic states:

[0137]

[0138] Where τ is the adjustable temperature coefficient, N is the size of the historical data set, and θ i is the historical process parameters of group i, L i Corresponding to θ i The optimization target value, |θ i > is the parameter θ i (b) Variational quantum compression: Optimize the quantum state density matrix by parameterizing the quantum circuit to minimize the following loss function:

[0139]

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

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

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

[0143] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these 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 any one or more embodiments or examples.

[0144] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to the specific embodiments described. Obviously, many modifications and variations are possible based on the content of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A centrifuge parameter optimization simulation software system, characterized in that: The system comprises 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), wherein: The quantum-classical hybrid data preprocessing unit (100) is used to encode the physical and chemical properties of biomolecules and nanomaterials into quantum states, and perform parallel dimensionality reduction of high-dimensional data using quantum Fourier transform; The quantum computing simulation and model building unit (200) uses a 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; The quantum optimization algorithm execution unit (300) is used to efficiently search and globally optimize the multivariable parameter space in the centrifugal process by collaboratively 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 to perform collaborative simulation and error mutual feedback optimization of the nanoscale to microscale separation process through a dynamic quantum-classical task scheduling engine and a cross-scale feature fusion channel; The quantum knowledge base and dynamic learning unit (500) are based on quantum-enhanced distributed learning and knowledge evolution mechanisms to perform cross-platform data collaboration, task adaptive migration, and continuous retention of historical experience.

2. A centrifuge parameter optimization simulation software system according to claim 1, characterized in that, The quantum-classical hybrid data preprocessing unit (100) comprises 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 physical and chemical properties of biomolecules and nanomaterials into quantum bit amplitude and phase; The quantum Fourier transform module (102) is used to extract features and reduce 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 system, and supports hybrid computing pipeline.

3. A centrifuge parameter optimization simulation software system according to claim 1, characterized in that: The quantum computing simulation and model building unit (200) comprises a quantum Monte Carlo simulation module (201), a parameterized quantum circuit building module (202), and a multi-physics 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 to construct a cell membrane shear stress model through a four-layer parameterized quantum circuit; The multi-physics field 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.

4. A centrifuge parameter optimization simulation software system according to claim 1, characterized in that, 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 characteristic solver module (301) is used to optimize the Hamiltonian ground state of the centrifugal acceleration-time parameter through the variational quantum characteristic solver; The quantum approximate optimization algorithm module (302) is used to solve the multi-objective Pareto frontier of temperature gradient and rotation speed using the quantum approximate optimization algorithm; The hybrid gradient module (303) is used to combine quantum gradient descent with the classical ADAM algorithm to achieve rapid convergence in a 15-dimensional parameter space.

5. A centrifuge parameter optimization simulation software system according to claim 4, characterized in that, The quantum approximate optimization algorithm module (302) uses a quantum approximate optimization algorithm to solve the multi-objective Pareto frontier of temperature gradient and rotation speed, and the specific operation is as follows: A1: Question code: The temperature gradient The optimization objective with respect to the speed ω is transformed into a weighted Hamiltonian: Where, α∈[0,1], and H ω are the cost Hamiltonians for temperature gradient and rotation speed, respectively; A2: QAOA execution: Constructing a parameterized quantum circuit with p-layer unitary operators: Where H M is the mixing Hamiltonian, is the parameter to be optimized; A3: Frontier Generation: Obtain the Pareto optimal solution set by scanning the weight α, satisfying: Where, is the set of all solutions obtained by scanning α, and st represents the "subject to" constraint.

6. A centrifuge parameter optimization simulation software system according to claim 4, characterized in that: The hybrid gradient module (303) combines quantum gradient descent with the classical ADAM algorithm to perform rapid convergence in the 15-dimensional parameter space. The specific operations are as follows: B1: Quantum gradient calculation: Use the parameter shift rule to calculate the gradient of the expected value of the quantum circuit: Where, is the parameter θ i The corresponding quantum gradient, H(θ) is the quantum circuit Hamiltonian with parameter θ, θ i is the i-th parameter to be optimized; B2: Classical momentum correction: Input the quantum gradient into the ADAM optimizer. The parameter update formula of the ADAM optimizer is: in: (One-level deviation calibration) (One-level deviation calibration) m t =β1m t-1 +(1-β1)g t (first moment of gradient) (second moment of gradient) Where η is the learning rate, g t is the quantum gradient value of the t-th iteration, β1 and β2 are momentum attenuation coefficients, β1 = 0.9, β2 = 0.999; B3: Hybrid convergence judgment: When three consecutive iterations meet The optimization is terminated when , where L(θ) is the loss function value.

7. A centrifuge parameter optimization simulation software system according to claim 1, characterized in that: The quantum-classical hybrid simulation verification unit (400) comprises 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 task weights in real time based on quantum annealing and dynamically allocates quantum and classical computing resources; The cross-scale feature fusion module (402) is used to perform data alignment and feature fusion between the nanoparticle sedimentation trajectory predicted by quantum computing and the fluid flow field simulated by classical CFD; The error mutual feedback optimization module (403) is used to dynamically adjust model parameters and correct decoherence errors in quantum computing by comparing quantum and classical simulation results.

8. A centrifuge parameter optimization simulation software system according to claim 7, characterized in that: The error mutual feedback optimization module (403) dynamically adjusts model parameters and corrects decoherence errors in quantum calculations by comparing quantum and classical simulation results. The specific operations are as follows: C1: Result comparison: Calculating quantum-predicted nanoparticle sedimentation trajectories q i Compared with the classic CFD simulation results i Root mean square error RMSE: Where n is the number of samples, q i and c i are the particle positions at the i-th time step respectively; When the RMSE exceeds the dynamic threshold T1, the parameter correction process is triggered. The calculation formula of T1 is: Where, σ exp is the standard deviation of experimental data, σ sim is the standard deviation of the simulation results; C2: Parameter adjustment: Update the quantum computing model parameters θ through the Bayesian optimization algorithm: Where D is the experimental data or high-confidence simulation results, and α is the learning rate; Focus on adjusting parameters that affect cross-scale simulation consistency, including the nanoparticle diffusion coefficient D p , fluid viscosity μ, the adjustment formula is: Where β and γ are adjustment coefficients, 0.05≤β, γ≤0.2; C3: Decoherence error correction: The decoherence error ∈ is estimated using the zero-noise extrapolation technique: ∈=f(λ1,λ2,…,λ m )·[E(λ1)-E(λ2)] Where λ i is the noise scaling factor, E(λ i ) is the noise intensity λ i The expected value when , f is the extrapolation function; Error compensation is performed by inserting a virtual quantum gate U(∈). The specific formula is: Where, is the original quantum circuit, and U(∈) is the error compensation operation.

9. A centrifuge parameter optimization simulation software system according to claim 1, characterized in that: 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 to extract spatiotemporal features of cross-platform process data using a quantum long short-term memory network; The strategy migration module (502) is used to adaptively adjust the parameter recommendation strategy of different centrifuge models through the quantum policy gradient algorithm; The knowledge distillation module (503) is used to compress historical optimization experience into an updateable quantum state density matrix.

10. A centrifuge parameter optimization simulation software system according to claim 9, characterized in that: The knowledge distillation module (503) compresses historical optimization experience into an updateable quantum state density matrix. The specific operation is as follows: (a) Historical data encoding: Mapping historical process parameters and their corresponding optimization target values ​​into quantum characteristic states: Where τ is the adjustable temperature coefficient, N is the size of the historical data set, and θ i is the historical process parameters of group i, L i Corresponding to θ i The optimization target value, |θ i > is the parameter θ i The quantum coded state of (b) Variational quantum compression: Optimize the quantum state density matrix by parameterizing the quantum circuit to minimize the following loss function: Where S(·‖·) is the quantum relative entropy, and λ is the regularization coefficient; (c) Dynamic update trigger: When new data (θ new ,L new ) and the fidelity of the current density matrix ρ satisfies F(ρ,|ψ new ><ψ new |)<κ, re-execute steps (a)-(b) to update knowledge, where κ is the preset threshold and fidelity

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