128 results about "Quantum neural network" patented technology

The technology relates to enhancing performance of quantum sensor operation in a noisy environment. Quantum neural networks are used to pre- and post-process analog signals to which qubits of a quantum sensor are exposed. Intra-processing using additional quantum neural networks may also be employed. This approach filters out noise from both the input analog signal and the system itself to achieve a very high signal to noise ratio. This permits the system to detect induced dynamics associated during exposure of the qubits to the analog signals. This enables the quantum sensor to sense a change in a state of a system under test, which can be beneficial for areas such as imaging, magnetometry, sensing electric fields, optomechanical sensors, quantum radar and other areas where enhanced signal to noise ratios are desired.

The invention relates to a power equipment defect detection method and system. The method comprises the steps of firstly collecting historical defect image data of power equipment, and preprocessing to obtain a data set; s2, a deep learning model is built, the model architecture comprises a quantum neural network, a backbone network, a multi-scale feature fusion layer and a target position and category prediction layer, the deep learning model is trained by using the data set in S1, and a defect detection model is obtained; and finally, deploying the defect detection model to edge equipment, inputting the current target power equipment image into the defect detection model by the edge equipment, and outputting a power equipment defect detection result, thereby realizing power equipment defect detection. Compared with the prior art, the method has the advantages of being suitable for a complex inspection environment, accurate in detection, high in confidence degree and the like.

The invention discloses a seal removing and document repairing method based on quantum state collaborative regulation and control, and relates to the field of document image processing and quantum computing cross technology, the method comprises the following steps: obtaining to-be-processed information, and carrying out quantum-classical feature collaborative preparation; the character stroke continuity is guaranteed through quantum entangled state modeling and entanglement degree constraint iteration, texture decoupling is achieved through quantum wavelet transform and Gram-Schmidt orthogonalization, and adaptive filling is conducted in combination with a quantum generative adversarial network; dynamic quantum phase adjustment is used for counteracting superposition interference of stamps with different transparency, and quantum neural network noise reduction and multi-scale quantum Fourier sharpening are used for optimizing image quality; checking the repair result, if the repair result does not reach the standard, returning to the edge sharpening link to perform decoupling and filling the edge sharpening link to readjust the parameter; and for special scenes such as inclination, multi-color overprinting and ultra-thin frames, quantum rotation correction, color channel separation and boundary annihilation operator processing are used, finally, high-precision, high-naturalness and high-adaptability restoration of seal removal is achieved, and high fidelity of results is guaranteed.

The invention provides a signal decoding method based on a PSI5 interface, and relates to the technical field of signal decoding, and the decoding method comprises the following steps: processing and adjusting a received PSI5 signal to obtain an accurate identification signal; and extracting a clock signal from the PSI5 signal, designing a clock compensation algorithm according to the quantum neural network, and dynamically adjusting the clock signal to obtain clock synchronization data. And determining a data frame boundary, and decoding the accurate identification signal to obtain original data. And performing comparative analysis on the original data and the PSI5 signal to obtain comparative difference data, judging whether the comparative difference data accords with a preset error type or not, and performing corresponding processing. According to the invention, fractional calculus processing is carried out on the received signal, then signal distortion is corrected by using a channel equalization method, and the quantum neural network is constructed to dynamically adjust the clock signal, so that clock synchronization is realized, a reliable time reference is provided for accurate processing of the signal, and the accuracy of signal decoding is improved.

The invention provides a multi-mode environment cooperative regulation and control system for poultry egg preservation. The system comprises a sensor network and a server, wherein the sensor network is used for collecting poultry egg data; the server is used for constructing a digital twinborn model according to the poultry egg data; perceptual data output by the digital twin model are input into a quantum neural network model, and a predicted fresh-keeping result and an optimal environment regulation and control parameter combination are obtained; according to the optimal environment regulation and control parameter combination, driving a regulation and control actuator group and feeding back actually executed regulation and control parameter values to the digital twinborn model; comparing the difference between the actual fresh-keeping effect reflected by the digital twinborn model and the predicted fresh-keeping result, and optimizing a mapping strategy from the current poultry egg state to the optimal regulation and control parameter by taking the fresh-keeping period extension rate as a reward function; the change condition of biochemical indexes is monitored through a digital twinborn model, and a monitoring result serves as a feedback signal to be input into the reinforcement learning module for strategy adjustment. The fresh-keeping period can be prolonged, intelligent prediction is achieved, quality traceability is guaranteed, and the energy consumption cost is reduced.

The invention discloses a cardiovascular disease risk assessment system based on big data analysis, which relates to the technical field of medical big data and quantum and comprises a data acquisition module, a preprocessing module, a feature extraction module, a model training module, a risk assessment module and a result display module. Data acquisition integrates multi-source heterogeneous data, quantum encryption is used to guarantee security, and preprocessing is carried out by deep reinforcement learning cleaning and adaptive normalization; feature extraction is combined with quantum principal component analysis and an auto-encoder, and a quantum attention mechanism is introduced; the model training adopts quantum neural network ensemble learning of quantum annealing optimization; introducing fuzzy logic reasoning to correct probability and layering in risk assessment; results are displayed through virtual reality and augmented reality technologies, and report suggestions are automatically generated. The method has the advantages of prominent advantages, comprehensive multi-modal data acquisition, accurate advanced technology preprocessing and feature extraction, efficient and accurate quantum optimization model training, and combination of real-time monitoring and a knowledge graph, provides a basis for prevention and treatment of cardiovascular diseases, and promotes medical intellectualization and precision.

The invention relates to the technical field of digital twinborn simulation, and particularly discloses a digital twinborn simulation training system for the whole process of administrative law enforcement, and the system comprises an intelligent sensing layer which is used for collecting the multi-modal data of a law enforcement site in real time, carrying out the data transmission, preprocessing and encryption, and obtaining the monitoring data of the emotional response and physiological state of a law enforcement officer; the twinborn modeling layer is used for constructing a digital twinborn model of a law enforcement scene based on a quantum neural network and simulating a multi-agent game process in a law enforcement process; the scene generation layer is used for intelligently generating a law enforcement scene in combination with legal provisions and cases, simulating the evolution process of emergencies and performing rule constraint on physical interaction in a virtual environment; according to the simulation training system, various dynamic changes in a complex law enforcement environment can be reflected more accurately, and a more vivid and efficient training environment is provided for administrative personnel, so that the law enforcement ability and the ability of coping with complex conditions of the law enforcement personnel are effectively improved.

The invention discloses a machine room operation and maintenance management automatic inspection system based on intellectualization, and belongs to the technical field of intellectualization. Comprising a multi-source data fusion processing module, a hidden fault probability distribution module, a fault root cause analysis module, a dynamic adaptive baseline generation module, a multivariate strategy self-healing decision module, a cooperative control execution module and a feedback optimization module. According to the method, a full-link intelligent system of multi-source data fusion, quantum neural network prediction, graph neural network traceability, dynamic baseline generation, reinforcement learning decision, transactional scheduling execution and closed-loop optimization is constructed, the hidden fault recognition capability is improved through quantum classical hybrid calculation, and accurate root cause positioning is realized in combination with a causal atlas; and an optimal repair scheme is generated based on a dynamic baseline and a reinforcement learning strategy library, and finally, the reliability of cross-system operation is ensured through transactional scheduling, so that the fault prediction accuracy is remarkably improved.

The invention provides a Q / R dual-adaptive hybrid quantum filtering method for an OTFS (Over the The Over the File System) communication and inductance integrated system, and the method comprises the following steps: constructing an OTFS communication and inductance integrated signal model, and obtaining a target measurement vector; establishing a state vector for describing a target motion state, and constructing a state transition equation and a measurement equation; the method comprises the following steps: constructing a Q / R mixed quantum adaptive adjustment module based on dual-channel features to construct a dual-channel feature input vector, generating a dual-path adjustment factor through a mixed quantum neural network, and constructing an adaptive Q matrix and an adaptive R matrix; and performing adaptive state prediction and updating at each time step by using the adaptive Q and R matrixes constructed in the step 3 to realize tracking of the motion state of the target. According to the method, the hybrid quantum neural network is introduced for driving, collaborative optimization of filter parameters is achieved, and therefore the precision, robustness and response speed of target tracking are remarkably improved in a complex dynamic noise environment.

The invention discloses a quantum-classical machine learning hybrid wind power prediction method, which comprises the following steps of: acquiring historical wind power, a fan sensor and meteorological data, and setting the wind power as a learning target; the method comprises the following steps: decomposing formatted time characteristics, generating 6-dimensional periodic coding time characteristics through sine-cosine function mapping, splicing the 6-dimensional periodic coding time characteristics with meteorological characteristics, selecting first n characteristic vectors according to importance scores, coding the first n characteristic vectors into a quantum state, and coding a mapping angle by using a parameterized revolving door; and constructing a parameterized quantum circuit to capture high-dimensional nonlinear characteristics, obtaining an output expected value through terminal measurement, calculating a mean square error as a loss function, adjusting a parameter to minimize the loss function, and predicting wind power by using the trained quantum neural network. The technical problems that an existing model is large in parameter quantity, complex in hyper-parameter space, slow in training convergence, high in model black box performance and the like are solved.

The invention provides a network attack detection model training method, a training device, a detection method, a storage medium and computer equipment. The method comprises the following steps: acquiring a network attack training data set; preprocessing the attack training data set and then performing quantum state coding to form a first input quantum state; inputting the first input quantum state into the variable component sub-line, forming an optimal parameter through dynamic adjustment, and obtaining a corresponding first detection result based on the optimal parameter to represent whether a network attack behavior exists in the network flow data; endowing the optimal parameter to a variable component sub-neural network model to form a network attack detection model; the optimal parameters obtained through training are input into the variable component sub-neural network model to form the network attack detection model, the variable component sub-neural network model can be flexibly designed, and high detection precision can be achieved under the condition that a small number of sub-bits are used.

The present invention relates to a quantum machine learning method for multi-class classification, and the method comprises the steps of: applying a Quantum Convolution Neural Network (QCNN) quantum circuit to input data having q qubits, and outputting a feature vector based on Pauli-Z measurement; and applying a Quantum Neural Network (QNN) quantum circuit to the feature vector, and outputting a multi-class prediction vector with scalability increased compared to q qubits based on basis measurement.

The invention discloses a high-precision quantum sensing environment physical quantity real-time monitoring system and method, and belongs to the technical field of environment monitoring. The system comprises a quantum sensing front end, a signal excitation and regulation module, a quantum classical hybrid computing engine, an edge cloud collaborative transmission module and a terminal application module. The quantum sensing front end adopts a quantum entanglement sensing array integrated by NV color center diamond and a rubidium atom gas chamber to synchronously sense a magnetic field, temperature, pressure and vibration; the signal excitation and regulation module optimizes regulation parameters and inhibits noise through a quantum optimal control algorithm; a quantum-classical hybrid calculation engine is combined with a quantum neural network and classical multi-algorithm collaboration to realize efficient resolving and data fusion of quantum state features. According to the invention, the monitoring precision, the real-time response speed and the anti-interference capability are obviously improved, synchronous monitoring of multiple physical quantities is supported, and the system is suitable for various scenes such as industry, scientific research and deep space exploration, and has wide application value.

The invention discloses an intelligent power transmission and transformation monitoring and fault early warning system based on Internet of Things, which relates to the technical field of power transmission and transformation monitoring and Internet of Things and comprises a data acquisition layer, a network transmission layer, a data processing layer and an early warning display layer. The functions of quantum encryption communication and self-diagnosis repair are achieved; multi-element networking is adopted for network transmission, quantum key distribution and a block chain are combined, an SDN and SD-WAN fusion architecture is used, a three-layer architecture is used for data processing, a quantum neural network and knowledge graph assistance is used, an MR technology and a brain-computer interface are used for early warning display, and all layers collaboratively guarantee efficient operation of the system. The system has obvious advantages, data acquisition is comprehensive and safe, and the sensor can be self-diagnosed and repaired; network transmission is stable and reliable, and data security is guaranteed; data processing is accurate and efficient, and early warning accuracy is improved; the early warning display interactivity is high, the equipment can be scientifically evaluated, the maintenance decision is optimized, the cost is reduced, and the stable operation of the power transmission and transformation system is ensured.

The invention provides a focusing optimization method, device and equipment based on quantum deep learning. The method comprises the following steps: acquiring a continuous shooting image sequence of a target object; the method comprises the following steps: shooting a target object by using photographic equipment to obtain a continuous shooting image sequence; quantum image features corresponding to the continuous shooting image sequence are extracted; wherein the quantum image features comprise quantum bits, quantum dynamic trajectories and quantum depth-of-field distribution; inputting the quantum image features into a pre-trained quantum neural network to obtain a focus prediction result of the target object; and adjusting the focal length of the photographic equipment according to the focus prediction result. Through the above method, the focusing speed and precision are improved, so as to meet the shooting requirements in ultra-high-speed photography and complex scenes.

The invention discloses a light quantum chip which is combined with high-dimensional coding, is provided with two data coding layers and two trainable entanglement layers, is compatible with a data recoding technology, and can be used for variable component subtasks such as a quantum neural network. The chip structure comprises a cascaded Mach-Zehnder interferometer binary tree array, d groups of photon pair sources, d groups of controlled unitary gate modules, a multi-quantum bit control Z gate, four d-dimensional adjustable linear networks and two state tomography linear networks. The cascaded MZI binary tree array and the controlled unitary group jointly form a first coding layer and a parameter-containing entanglement layer, and the adjustable optical linear network and the multi-quantum bit control Z gate jointly form a second coding layer and a parameter-containing entanglement layer. The two state chromatography linear networks can further improve the computing power of the chip in a mode of changing a measurement basis or executing quantum state chromatography. According to the method, the expression ability of the light quantum neural network is improved, and the development of a light quantum computing chip and the application of the light quantum computing chip in actual tasks are facilitated.

The invention discloses a thermal imaging technical method, device, equipment and medium, and relates to the field of thermal imaging temperature drift compensation correction, and the method comprises the steps: collecting the original temperature data of an infrared sensor in the thermal imaging equipment; preprocessing the original temperature data, and converting the preprocessed temperature data into a superposition state and an entanglement state of quantum; according to the loss function and gradient of the quantum neural network, constructing a temperature drift compensation model based on the quantum neural network; and inputting the converted superposition state and entanglement state of the quantum into a temperature drift compensation model, and outputting data of the thermal imaging equipment after temperature drift compensation correction during operation. Therefore, quantum calculation is introduced into a dynamic temperature drift compensation and correction algorithm of the thermal imaging equipment, the precision of a temperature drift compensation model is enhanced by using the parallel calculation capability of a quantum superposition state and an entangled state, and the reaction rate of the thermal imaging equipment in a high-temperature environment is efficiently improved; and the problems of operation blockage, image distortion, abnormal shutdown and the like caused by excessive heating of the equipment are solved.

The invention discloses an anti-quantum computing attack data encryption method based on a deep quantum neural network, which is applied to the technical field of data processing, and comprises the following steps: obtaining quantum associated data and security constraint parameters in the fields of government affairs and finance, and completing classical data quantum bit coding, high-dimensional feature extraction and noise filtering through quantization preprocessing; generating high-dimensional quantum state data adaptive to the quantum neural network; a deep quantum neural network encryption architecture is constructed, a dynamic key pool is generated based on quantum superposition-entanglement characteristics, and a layered encryption scheme is formed in combination with a weight sub-update mechanism; encryption parameters are optimized through a quantum neural network reinforcement learning framework, robustness is improved, and a self-learning optimization strategy is generated; an evolution trajectory is monitored by means of a quantum neural network to construct an encryption execution closed loop, and finally comprehensive evaluation information containing indexes such as a training convergence rate and key efficiency is generated through a quantum gate inverse operation learning model and a Hash verification neural network, so that the quantum attack resistance is enhanced.

The invention discloses a remote sensing image recognition method, system, device and product based on quantum migration, and relates to the technical field of remote sensing image recognition, and the method comprises the steps: obtaining a pre-trained deep learning model used for recognizing a preset target, and extracting the high-level features of the deep learning model; encoding the high-level features into a quantum state through angle encoding, and constructing a multi-bit entangled state taking a specified quantum bit as a center to form a quantum neural network; forming a hybrid migration model according to the pre-trained deep learning model and the quantum neural network; and training the hybrid migration model, inputting the remote sensing image to be identified into the trained hybrid migration model, and identifying to obtain a preset target. By adopting the embodiment of the invention, the parameter quantity of the model can be reduced, and meanwhile, the accurate recognition requirement of the remote sensing image is met by utilizing the computing power of the classic computer and the global characteristics of the quantum computer.

The invention relates to the technical field of neural networks and vehicle professional language processing, in particular to a vehicle professional language text classification method and system based on a quantum recurrent neural network, and the method comprises the steps: enabling a word to be embedded into a pre-trained hybrid calculation model with a vector as an input feature, the hybrid calculation model comprises a quantum recurrent neural network uploaded in batches and a classical neural network, and the quantum recurrent neural network uploaded in batches is fused into the classical neural network; the quantum neural network uploaded in batches is characterized in that an input word embedding vector is divided into a plurality of batches according to a predefined quantum bit number, and the batches are connected through a variable component sub-circuit, so that a coding-variable layering hybrid model is formed; and running the hybrid calculation model, and outputting the text classification corresponding to the to-be-processed corpus data. According to the method, limited data resources are better utilized to carry out classification tasks, and meanwhile, the understanding ability of the model for complex vehicle professional language expression is improved.

Embodiments of the present application provide a quantum neural network classifier training method and device, electronic equipment and medium. The scheme is as follows: obtaining a training data set and a to-be-trained classifier; for each training sample data, classifying the training sample data by using the to-be-trained classifier to obtain a first predicted label; calculating a first loss value of the to-be-trained classifier according to a sample label corresponding to each training sample data and the first predicted label; when the to-be-trained classifier has not converged, adjusting the classifier parameters based on the first loss value, and returning to execute the step of classifying each training sample data by using the to-be-trained classifier to obtain the first predicted label corresponding to the training sample data until the to-be-trained classifier converges at the current time. Through the technical scheme provided by the embodiments of the present application, the optimization of the quantum neural network classifier is realized, and the classification accuracy and attack resistance of the quantum neural network classifier are improved.

The invention discloses a health state monitoring method and system based on big data multi-dimensional evaluation, and relates to the cross technical field of health monitoring, big data processing and quantum machine learning. Through multi-source health data collection and in combination with quantization coding and high-dimensional data dimension reduction technologies, core low-dimensional feature vectors are effectively extracted, further, nonlinear correlation features in the feature vectors are deeply mined through a quantum neural network model, comprehensive and accurate evaluation of the health state of the user is achieved, and the user health state evaluation accuracy is improved. The multi-dimensional and deep evaluation mode can reflect the health state of the user more accurately, and provides a more reliable basis for subsequent early warning and intervention.

The invention discloses an Ansatz self-adaptive intelligent design optimization method based on hardware awareness, and relates to the field of quantum machine learning, and the method comprises the following specific steps: obtaining a preset number of target task data sets which are labeled with labels, dividing the target task data sets into K groups, and further based on a load balancing strategy, obtaining a target task data set; dividing a preset number of quantum bits to form quantum circuit partitions; constructing a full binary merging tree, obtaining quantum bit information corresponding to each node, constructing an Ansatz training line in combination with a preset Ansatz structure, and further optimizing the Ansatz training line based on the target task data set; and deploying the obtained optimal Ansatz line into the target quantum neural network to realize classification of the target task. According to the method, the Ansatz line architecture is designed through dynamic structure design and task awareness optimization, the problem that hardware efficient Ansatz is limited by a fixed architecture is avoided, and the convergence speed and the training stability are remarkably improved.

The invention provides an original real-time electricity price prediction method based on a quantum complex neural network and Hilbert-Huang Transform (HHT), and the real-time electricity price prediction method based on the quantum complex neural network and the Hilbert-Huang Transform (HHT). Aiming at the non-stationarity of electricity price data on the historical level, firstly, the time internal correlation of each feature channel is extracted through an HHT time sequence analysis method, and complex multi-dimensional time sequence prediction is simplified into a simple regression task, so that a tedious time sequence modeling process is avoided; for the non-linear problem of electricity price data, a quantum neural network is used for capturing the coupling relation between different factors, and the calculation speed and the model efficiency are further improved by means of the parallelism of quantum calculation. Based on the two advantages, the method can realize accurate real-time electricity price prediction.

The present invention discloses a method for generating commodity images based on a quantum generative adversarial neural network, which relates to the technical field of data generation based on quantum computing. The present invention adopts a brand-new computing mode based on the basic principles of quantum mechanics, namely quantum computing. Due to the powerful parallelism and non-local characteristics of quantum neural networks, the newly obtained quantum generative neural network in the present invention is superior to classical neural networks in both the extraction of global features and the convergence speed of commodity images, and is simpler than classical neural networks in the design of quantum generative neural networks and the adjustment of hyperparameters, making it suitable for execution on current NISQ real quantum computers.

The application relates to a method, device and equipment for anti-deepfake speech based on quantum resonance peak disturbance, wherein the method comprises the following steps: reading an original speech signal, extracting a fundamental frequency and a formant frequency of the original speech signal; creating a parameterized quantum circuit to generate quantum noise; adding the quantum noise to the extracted fundamental frequency and formant frequency to obtain a disturbed signal; and preprocessing the disturbed signal to obtain a reconstructed speech signal. The optimized quantum noise generated by the quantum neural network can effectively interfere with the learning and generation of speech features by a deepfake model. Through the design of a loss function and the adjustment of an optimization algorithm, the speech after adding the noise is ensured to have the smallest difference in hearing from the original speech.

The invention discloses a periodic target electromagnetic scattering characteristic rapid simulation method based on a quantum neural network, and the method comprises the following steps: selecting finite periodic arrays with different structure gaps and array scales in a certain range, and generating a data set through employing a sub-global basis function method; selecting a classic neural network technology, designing a quantum neural network unit, and building a hybrid quantum neural network model; training and testing the hybrid quantum neural network model, and storing parameters in the model in an optimal state; and generating a sub-global basis function by using a sub-global basis function method, predicting an expansion coefficient of each unit in the periodic structure by using the trained mixed quantum neural network model, and finally calculating to obtain surface current distribution of the whole target periodic structure. According to the method, the hybrid neural network technology is utilized to avoid consuming a lot of time to generate the reduced impedance matrix, the consumed time can be effectively reduced while the accuracy is ensured, and the efficiency advantage brought by quantum equipment in the future is expected to be obtained.

The application discloses a coherent Ising machine and a model parameter optimization method and device thereof, a medium and a product. The method comprises the following steps: constructing an initial training population; the initial training population comprises a plurality of quantum neural network (QNN) instances configured with different parameter combinations; based on an evolutionary optimization algorithm, the initial training population is iteratively updated to obtain a target training population corresponding to a convergence condition; the optimal QNN instance in the target training population is determined, and the parameter combination of the optimal QNN instance is obtained. Embodiments of the application aim at the non-continuity and non-differentiability of the quantum state of the coherent Ising machine, and propose a solution combining a population evolutionary optimization algorithm and the characteristics of the coherent Ising machine: the parameters of the discrete QNN instances in the training population are iteratively updated based on the evolutionary optimization algorithm, and the optimal parameter combination is efficiently solved by simultaneously utilizing the parallel computing advantage of the coherent Ising machine, so that the model training efficiency is improved.

A method for modifying a quantum neural network, comprising: identifying via computer means information that needs to be erased from a trained quantum neural network; localizing via the computer means the identified information in the trained quantum neural network; erasing via the computer means the identified information from the trained quantum neural network without erasing from the trained quantum neural network other information that needs not to be erased; compressing via the computer means the quantum neural network which results when the identified information has been erased. Also, a system.

The application discloses a quantum neural network hybrid system based on a memristor Hopfield and a training method thereof, and aims to solve the problems of low storage precision of complex quantum weights, poor quantum-classical collaboration and high energy consumption of the quantum neural network. The system comprises a quantum computing layer, a classical interface layer and a memristor Hopfield layer, and constitutes a closed loop through error feedback. The quantum computing layer extracts quantum entanglement features by using a quantum bit and a quantum convolution layer, and outputs complex quantum weights; the classical interface layer converts the complex quantum weights into memristor conductance values through polar coordinate mapping; and the memristor Hopfield core layer adopts a double-end complex memristor array to complete storage and associative memory operation. The training method optimizes quantum gate parameters and memristor conductance values through closed loop feedback collaboration. The application can reduce precision loss, realize stable and low-energy consumption storage, and is suitable for quantum key storage, medical image diagnosis and other scenes.