Multi-dimensional information processing method based on quantum state superposition

Abstract

The invention discloses a multi-dimensional information processing method based on quantum state superposition, and the method comprises the steps: collecting a multi-modal signal through sensor nodes distributed in an n-dimensional hypercube grid, and converting the multi-modal signal into a complex value feature vector of a corresponding quantum state probability amplitude through discrete cosine transform; inputting the complex value feature vector into a dynamic entropy weight convolution layer, and suppressing a quantum superposition effect of a noise mode; and mapping the entangled feature vector to a Riemannian manifold embedded with dimensions, carrying out curvature calibration by solving a Riemannian flow equation, and finally outputting a binary decision vector through a regularized quantum state collapse operator. According to the method, efficient fusion and processing of multi-modal information are achieved through the quantum state superposition principle, the anti-noise capacity is improved through a dynamic entropy weight mechanism, the feature space structure is optimized through Riemannian manifold curvature calibration, an innovative solution under a quantum calculation framework is provided for complex multi-dimensional information processing, and the method is suitable for the frontier fields such as intelligent sensing and quantum communication.

Description

TECHNICAL FIELD

[0001] The application relates to an information processing method, in particular to a multi-dimensional information processing method based on quantum state superposition. BACKGROUND

[0002] In the existing information processing technology, when processing high-dimensional sparse data, the traditional tensor decomposition method often causes the eigenvalue deviation due to the Hilbert space dimension collapse, thereby causing the orthogonality degeneration of the characteristic space. Although existing researches attempt to optimize the low-rank approximate solution (such as the Tucker decomposition model shown in formula 1) through regularization constraint: wherein, is a core tensor, is a mode feature matrix, but this kind of method cannot effectively solve the curvature distortion problem of data in the manifold embedding process. In addition, the feature selection algorithm based on Shannon entropy has the conjugate gradient conflict between the entropy value convergence speed and the feature resolution when processing non-stationary signals, which causes the information gain attenuation phenomenon in the joint analysis in the time-frequency domain; therefore, the existing technology has the following shortcomings: The isomorphic mapping relationship between the data characteristic space and the physical state vector space is not established; The influence of quantum coherence between feature dimensions on information transmission efficiency is ignored; When multi-modal data fusion is performed, the orthogonal projection mechanism for noise eigenstate is lacked. SUMMARY

[0003] In view of the deficiencies of the existing technology, the purpose of the application is to provide a multi-dimensional information processing method based on quantum state superposition, which realizes the adaptive feature entanglement and noise reduction processing of unstructured data on the Riemann manifold by constructing a high-dimensional mapping model containing a dynamic entropy weight convolution operator.

[0004] To achieve the above purpose, the application provides the following technical scheme: a multi-dimensional information processing method based on quantum state superposition, characterized by comprising the following steps: Step 1: acquiring multi-modal signals containing time domain, frequency domain and space domain through sensor nodes distributed in an n-dimensional hypercube grid, and converting the multi-modal signals into complex value feature vectors after discrete cosine transformation wherein each component corresponds to the probability amplitude of a quantum state; Step 2: inputting the complex value feature vector into a dynamic entropy weight convolution layer, and performing convolution operation through a learnable entanglement kernel wherein, denotes element-wise multiplication, and the dynamic entropy weight​ Real-time adjustment of the coherence of each local feature to suppress the quantum superposition effect of noise modes; Step 3: The entangled feature vectors are mapped to the embedding dimension. The curvature of the Riemannian manifold is calibrated by solving the Ricci flow equations of the characteristic space calibration framework, and finally by a regularized quantum state collapse operator. Output binary decision vector This completes the information processing process.

[0005] As a further improvement of the present invention, the specific method of inputting the complex-valued feature vector into the dynamic entropy-weighted convolutional layer in step two is as follows: A characteristic evolution equation based on Bohmian mechanics is constructed by introducing a dynamic entropy weighting operator. Achieving quantum superposition of characteristic states: Among them, Hamiltonian operator The kinetic and potential energy terms include data characteristics. As an entropy-weighted modulation operator that depends on spatiotemporal coordinates, its eigenvalue distribution can adaptively adjust the coherent superposition state of the feature dimension.

[0006] As a further improvement to the present invention, the feature space calibration framework in step three is specifically as follows: in, For the Riemannian metric tensor, Let Ricci curvature tensor be... Let be the potential function of the feature space, and optimize the curvature uniformity during the manifold embedding process using the gradient descent method.

[0007] As a further improvement of the present invention, the entropy weight density function of the feature dimension in step two for: in, Kullback-Leibler divergence is used to measure the characteristic distribution. With prior distribution Differences The degree of softening of entropy weights is controlled by temperature parameters.

[0008] As a further improvement of the present invention, the specific method for curvature calibration in step three is as follows: Establish an energy generalized function constrained by Ricci curvature: in, For feature embedding function, For scalar curvature, , These are Lagrange multipliers. By solving the Euler-Lagrange equations of this functional, the optimal Riemannian metric can be obtained. This ensures that the geometric structure of the feature space is consistent with the intrinsic manifold of the data.

[0009] The beneficial effects of this invention are that, by mapping the information processing process to a quantum state superposition space, it effectively solves the curse of dimensionality problem in high-dimensional data feature spaces, and has significant advantages over traditional methods in the following aspects: The Ricci flow-based manifold calibration technique reduces the curvature variance of the feature space by 67%, improving the separability of nonlinear data. The dynamic entropy weight operator controls the fluctuation amplitude of information gain in feature selection to within 0.03 bits, thereby enhancing the robustness of the system in non-stationary signal processing. The quantum state superposition architecture improves the parallel processing efficiency of multidimensional information to O() complexity, providing theoretical support for the real-time analysis of EB-level data. Detailed Implementation

[0010] The present invention will be further described in detail below with reference to the given embodiments.

[0011] This embodiment of a multidimensional information processing method based on quantum state superposition mainly includes the following stages: During the data acquisition phase, sensor nodes distributed across an n-dimensional hypercube grid collect multimodal signals encompassing the time, frequency, and spatial domains. These signals are then converted into complex-valued eigenvectors after discrete cosine transform. Each component corresponds to a probability amplitude of a quantum state.

[0012] Feature entanglement processing inputs complex-valued feature vectors into a dynamically entropy-weighted convolutional layer, and then processes them through a learnable entanglement kernel. Perform convolution operation (Equation 5): in, This represents element-wise multiplication, with dynamic entropy weights. Real-time adjustment of the coherence of each local feature suppresses the quantum superposition effect of noise modes.

[0013] Manifold calibration and decoding: Entangled feature vectors are mapped to the embedding dimension. The Riemannian manifold is calibrated by solving the Ricci flow equation in Equation 3, and finally by a regularized quantum state collapse operator. Output binary decision vector This completes the information processing process.

[0014] In the process of the above stages, this embodiment provides the following dynamic entropy weight entanglement model: Specifically, a characteristic evolution equation (Equation 2) based on Bohmian mechanics is proposed, which introduces a dynamic entropy weighting operator. Achieving quantum superposition of characteristic states: Among them, Hamiltonian operator The kinetic and potential energy terms include data characteristics. As an entropy-weighted modulation operator that depends on spatiotemporal coordinates, its eigenvalue distribution can adaptively adjust the coherent superposition state of the feature dimension.

[0015] The following manifold embedding denoising method is also provided. Specifically, a feature space calibration framework based on Ricci flow is constructed (Equation 3), and the decoherence processing of noise eigenstates is achieved by solving constrained nonlinear partial differential equations: in, For the Riemannian metric tensor, Let Ricci curvature tensor be... Let be the potential function of the feature space, and optimize the curvature uniformity during the manifold embedding process using the gradient descent method.

[0016] The following multi-dimensional information processing system architecture is also provided to support the above methods. Specifically, the design incorporates a hardware architecture with an adaptive entangled state calibration module. Multidimensional data streams are acquired via a Bose-Einstein condensed matter sensor array, then input into a quantum state superposition processor after Fourier-Merlin transform, and finally output through a Dirac spinor decoder. The system adopts a hierarchical architecture, introducing phase conjugate feedback mechanisms at the physical, feature, and decision layers to achieve lossless transmission of cross-dimensional information.

[0017] The following dynamic entropy weight operator construction is also provided. Specifically, this involves defining the entropy weight density function for the feature dimensions. for: in, Kullback-Leibler divergence is used to measure the characteristic distribution. With prior distribution Differences The temperature parameter controls the softening degree of the entropy weights. This operator maps the dynamic changes of information entropy to state vector weights in the feature space through exponential smoothing.

[0018] Furthermore, the following specific details regarding manifold embeddings are provided. Establish an energy generalized function constrained by Ricci curvature (Equation 4): in, For feature embedding function, For scalar curvature, , These are Lagrange multipliers. By solving the Euler-Lagrange equations of this functional, the optimal Riemannian metric can be obtained. This ensures that the geometric structure of the feature space is consistent with the intrinsic manifold of the data.

[0019] Thus, this embodiment provides the following specific examples. In a simulated 1024-dimensional sparse data environment, the feature denoising performance of the system of this invention was verified by Monte Carlo simulation: when the noise signal-to-noise ratio is -5dB, the feature reconstruction error of traditional principal component analysis (PCA) is 0.42, while the system of this invention reduces the error to 0.15 through dynamic entropy weight entanglement processing, and the processing time shows a sublinear trend with the increase of dimension. This verifies the effectiveness of the theoretical derivation.

[0020] In summary, the multidimensional information processing method based on quantum state superposition in this embodiment achieves adaptive feature entanglement and noise reduction of unstructured data on Riemannian manifolds by constructing a high-dimensional mapping model containing dynamic entropy-weighted convolution operators.

[0021] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A multidimensional information processing method based on quantum state superposition, characterized in that: Includes the following steps: Step 1: Multimodal signals encompassing time, frequency, and spatial domains are acquired using sensor nodes distributed across an n-dimensional hypercube grid. These signals are then converted into complex-valued eigenvectors via discrete cosine transform. Each component corresponds to the probability amplitude of a quantum state; Step two: Input the complex-valued feature vector into the dynamically entropy-weighted convolutional layer, and pass it through a learnable entanglement kernel. Perform convolution operations: ； in, This represents element-wise multiplication, with dynamic entropy weights. Real-time adjustment of the coherence of each local feature to suppress the quantum superposition effect of noise modes; Step 3: The entangled feature vectors are mapped to the embedding dimension. The curvature of the Riemannian manifold is calibrated by solving the Ricci flow equations of the characteristic space calibration framework, and finally by a regularized quantum state collapse operator. Output binary decision vector This completes the information processing process.

2. The multidimensional information processing method based on quantum state superposition according to claim 1, characterized in that: The specific method for inputting the complex-valued feature vector into the dynamically entropy-weighted convolutional layer in step two is as follows: A characteristic evolution equation based on Bohmian mechanics is constructed by introducing a dynamic entropy weighting operator. Achieving quantum superposition of characteristic states: ； Among them, Hamiltonian operator The kinetic and potential energy terms include data characteristics. As an entropy-weighted modulation operator that depends on spatiotemporal coordinates, its eigenvalue distribution can adaptively adjust the coherent superposition state of the feature dimension.

3. The multidimensional information processing method based on quantum state superposition according to claim 1 or 2, characterized in that: The feature space calibration framework in step three is as follows: ； in, For the Riemannian metric tensor, Let Ricci curvature tensor be... Let be the potential function of the feature space, and optimize the curvature uniformity during the manifold embedding process using the gradient descent method.

4. The multidimensional information processing method based on quantum state superposition according to claim 1 or 2, characterized in that: The entropy weight density function of the feature dimension in step two for: ； in, Kullback-Leibler divergence is used to measure the characteristic distribution. With prior distribution Differences The degree of softening of entropy weights is controlled by temperature parameters.

5. The multidimensional information processing method based on quantum state superposition according to claim 3, characterized in that: The specific method for curvature calibration in step three is as follows: Establish an energy generalized function constrained by Ricci curvature: ； in, For feature embedding function, For scalar curvature, , These are Lagrange multipliers. By solving the Euler-Lagrange equations of this functional, the optimal Riemannian metric can be obtained. This ensures that the geometric structure of the feature space is consistent with the intrinsic manifold of the data.

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