122 results about "Hybrid computation" patented technology

The invention discloses a quantum classical hybrid computing task dynamic scheduling system and method based on SLURM. The system comprises a quantum task feature extraction module, a dynamic priority evaluation module, a dependency analysis module, a quantum perception backfilling module and a uniform resource abstraction layer module. The method comprises the following steps: extracting quantum features of a to-be-processed task and a computing resource to form a quantum feature set and caching the quantum feature set; the feature set is obtained in real time, task priorities are output through multi-dimensional evaluation, and a real-time priority sequence is generated; based on the task type and the feature set, forming a dependency relationship between the classic task and the quantum task, and converting the dependency relationship into a dependency constraint and / or resource reservation instruction; according to the task priority, the resource reservation instruction and the real-time resource state, a future idle period is predicted, and a short-time quantum task is inserted for backfilling; computing resources are distributed according to the task priority and the dependency constraint, and the resource utilization state is fed back to the backfill and priority evaluation module in real time. According to the method, efficient scheduling of hybrid computing tasks can be realized, and the overall performance of the system is remarkably improved.

The present disclosure provides a computer-implemented system for automated augmentation of differential equation models. The system stores differential equations representing mechanistic behavior of physical or computational processes and constructs a hybrid computational solver by embedding a trainable universal approximator with adjustable parameters into the differential equations, where approximator outputs augment time derivatives during numerical integration. An iterative training process adjusts parameters through numerical integration, monitors integration failures, assigns infinite penalty values to loss functions when failures occur, and computes gradients using automatic differentiation otherwise. The system computes sensitivity metrics via Jacobian matrix evaluation, classifies input / output subsets as significant based on threshold-exceeding sensitivity metrics, generates a reduced approximator operating on classified subsets, and replaces the universal approximator with the reduced version to create an optimized solver.

The invention provides a sparse and dense state calculation method, device and equipment and a readable storage medium, and the method comprises the steps: receiving an input data stream, analyzing the feature information of the input data stream, and marking a sensitivity level for the data stream; according to the sensitivity level of the data stream, a corresponding calculation strategy is called, and the calculation strategy at least comprises secret state calculation, hybrid calculation and plaintext calculation; and executing a calculation strategy for calculation associated with the data stream according to the calculation strategy corresponding to the data stream in AI training and / or AI reasoning. Through the technical scheme of the specification, the sensitivity level is marked for the data stream, and the secret state, mixing or plaintext calculation strategy is dynamically matched, so that the overall calculation overhead is remarkably reduced while the high-sensitivity data security is ensured, and the AI training and reasoning efficiency is 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.

According to the method for designing the reasonable arch axis of the large-span arch bridge based on the hybrid calculation algorithm and the self-adaptive load distribution, an analytic equation method, a curve fitting method and an intelligent optimization algorithm are combined, and by selecting the initial arch axis, introducing a dynamic load distribution model and applying a genetic algorithm and a particle swarm optimization algorithm, the reasonable arch axis of the large-span arch bridge is designed. Global and local optimization is carried out, and the optimal shape and load distribution of the arch axis are ensured. And finally outputting design parameters, such as a control point, a bending moment, thrust and the like. According to the method, the design precision, flexibility and safety can be effectively improved, the material utilization rate is optimized, the cost is reduced, and the method is particularly suitable for design of large-span bridges and has high practical value.

A method of performing computation using a hybrid quantum-classical computing system comprising a classical computer and a quantum processor includes computing, by use of a classical computer, short-range inter-particle interaction energies and self-energies of a group of interacting particles, transforming the quantum processor from an initial state to a charge-position encoded state, applying Quantum Fourier transformation to the quantum processor, measuring an estimated amplitude of the Fourier transformed superposition state on the quantum processor, computing long-range inter-particle interaction energies based on the measured estimated amplitude of the Fourier transformed superposition state, and computing and outputting a sum of the short-range inter-particle interaction energies, the self-energies of the system, and the long-range inter-particle interaction energies as a total inter-particle interaction energies of the system.

The invention relates to a hybrid computing resource optimization model. Network optimization for arranging computing subtasks in a hybrid computing environment is provided. The method includes receiving an input of a network of nodes and edges representing computational processes and composition information thereof, wherein the nodes are grouped depending on whether they use classical computing resources or quantum computing resources. The method generates workflow constraints, scheduling constraints, and computing resource allocation constraints. The method generates an objective function. And solving the optimization problem according to the objective function and all the constraints. The solution determines the best computational goal achieved, computational workflow through selection of nodes, computational job scheduling, and allocation of computational processes between classic and quantum computational resources. The computing workflow is then executed to achieve the best computing goal according to the computed job schedule and the allocation of the computing process between the classical computing resources and the quantum computing resources.

A hybrid quantum-classical computing system designed to enhance AI applications by integrating classical computational methods with quantum computing power. The system incorporates a classical computing unit and multiple quantum computing units, each specialized for distinct algorithmic tasks. A control module within the classical unit, using predetermined decision logic, manages the distribution of tasks, selectively engaging quantum units for their unique capabilities to optimize efficiency. This scalable architecture allows for significant advances in AI, tackling complex, resource-intensive tasks by effectively harnessing the strengths of both quantum superposition and classical processing.

The invention discloses an intelligent article selection scoring system based on fusion of a rule engine and machine learning. The system comprises a data preprocessing unit, a feature engineering unit, a hybrid calculation unit and a dynamic weighted fusion unit. The feature engineering unit is used for generating a feature data set, a statistical feature data set, a time sequence feature data set and a composite feature data set from the preprocessed intelligent product selection data; the hybrid calculation unit is used for synchronously executing preset business rule calculation and machine learning prediction; preset business rule calculation is completed through a business rule calculation function, and machine learning prediction is completed through a multi-dimensional linkage machine learning prediction model; and the dynamic weight fusion unit is used for fusing the preset business rule score and the machine learning prediction score according to the dynamic business rule weight factor, and finally obtaining an intelligent product selection score. According to the invention, scientific, automatic and high-precision commodity potential evaluation is realized, and a breakthrough technical framework is provided for the field of e-commerce commodity selection.

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.

A system and method are disclosed for transforming at least one foundation artificial intelligence (AI) model into one or more embedded micro-models configured for deployment in constrained or hybrid computing environments. Each micro-model is packaged within a structured container that includes inference logic, metadata, telemetry thresholds, and fallback logic. A runtime engine monitors execution conditions and adaptively switches among inference logics or activates fallback behavior based on telemetry signals such as CPU load, memory usage, latency, or confidence score. The system supports operation on OS-less or minimal-runtime platforms and enables distributed deployment across mesh networks using multiple communication protocols. Micro-models may coordinate autonomously or under supervisory guidance and are executed within multi-dimensional or non-topological configurations. The invention enables scalable, resilient AI inference in embedded systems with limited resources.

The invention provides an uncorrelated parallel machine scheduling method, system and device, a medium and a product. The method comprises the steps of obtaining a main problem and a sub-problem corresponding to a scheduling task; obtaining a binary representation model of the main question; the scheduling quantum computer analyzes the binary representation model of the main problem to obtain a plurality of optional scheduling strategies; analyzing the sub-problem based on the plurality of selectable scheduling strategies to obtain a plurality of candidate sub-problem feedback information; screening the plurality of candidate sub-problem feedback information to obtain target sub-problem feedback information, and performing a next iterative analysis process based on the target sub-problem feedback information to obtain a plurality of new selectable scheduling strategies; and obtaining a target scheduling strategy until an iteration ending condition is met. Through the quantum and classical hybrid computing system, the quality and generation efficiency of the scheduling strategy are improved.

The application discloses an optoelectronic computer based on time domain all-optical signal serial-parallel hybrid computing, which is composed of an electrical transceiving module, a light source module, a linear matrix vector computing module, a summation separation module and a nonlinear activation module.The application adopts time sequence encoding serial input, and multiple input signals are simultaneously and parallelly calculated, so that all computing steps of a full connection neural network, i.e., linear calculation, summation operation and nonlinear activation, can be completed in the optical domain, and the host end only needs to send input data and receive the final computing result.The application has the following advantages compared with other comparative applications: multiple matrix-vector multiplication operation can be completed in parallel; only one optical element is used to complete summation and nonlinear activation operation on multiple multiplication vectors, thereby reducing the hardware deployment pressure; and the bandwidth loss of a pure serial time domain computing method is relieved through serial-parallel hybrid encoding in the time domain, and the computer computing speed is improved.

The invention provides a hybrid computing architecture and method based on cloud computing and edge computing, and the method comprises the steps: a business scene analyzer determines a processing mode of a task, a resource allocation module executes resource allocation by adopting a genetic algorithm according to the processing mode, and dynamically allocates cloud computing resources and edge computing resources, the decision-making center predicts future resource requirements based on a machine learning algorithm and feeds back a prediction result to the resource allocation module, the data management unit performs block processing on business data and allocates storage positions based on hash values, the edge node processing module executes a quick response task, the cloud processing center executes a compute-intensive task, and the cloud processing center executes a cloud computing task. And finally, feeding back a processing result to the edge node for further optimization. According to the method, the advantages of cloud computing and edge computing are integrated, flexible distribution and efficient utilization of computing power are achieved, and the requirements for the computing power in different scenes are met.

The invention relates to the technical field of high-speed aircraft ground simulation and electromagnetic property calculation, and provides a high-speed aircraft electromagnetic calculation method and system based on a quantum classification algorithm. A variable component sub-classifier is used as an intelligent scheduling core to perform feature recognition and region division on a whole flow field, and a transmission matrix method and a WKB approximation method are adaptively called according to a division result to perform hybrid calculation, so that the technical problem that a traditional single calculation strategy is difficult to collaboratively optimize in precision and efficiency is solved; the method has the advantages that global computing resources can be distributed according to needs, and the overall solving speed is greatly increased while high simulation precision of the key area is guaranteed.

The application relates to the technical field of Internet of Things, and discloses a STAR-RIS assisted hybrid NOMA computing offloading method, which comprises the following steps: S1, before the start of the offloading process, a main base station collects and caches hyper-surface and auxiliary base station basic information in a network; S2, when a user needs to offload a task, the user first sends an offloading request to the main base station; S3, the main base station receives the user offloading requirement; S4, after the optimization of the offloading strategy is completed, the main base station can notify the user, the auxiliary base station and RIS to prepare for task offloading; and S5, after the hyper-surface and the auxiliary base station are configured, the main base station sends the strategy to the user. Compared with the traditional only-reflection RIS technology, the STAR-RIS technology can transmit the offloading task to both sides of the hyper-surface through reflection and refraction signals, the offloading range is expanded, and for the Internet of Things equipment, computing nodes can be selected for computing offloading in a wider communication range.

This invention discloses a collaborative cloud rendering system, including a lighting and shadow rendering module. The module's direct lighting rendering unit performs rendering through direct lighting calculations, the shadow rendering unit performs rendering through shadow calculations, the ambient occlusion rendering unit performs rendering through ambient occlusion calculations, and the indirect lighting rendering unit performs rendering through indirect lighting calculations. The indirect lighting rendering unit runs on a cloud backend device. A hybrid rendering unit performs rendering through hybrid calculations and runs on a web frontend device. Depending on the performance of the web frontend device, each unit runs on either the web frontend device or the cloud backend device. Therefore, this invention proposes four key real-time lighting and shadow rendering algorithms for collaborative real-time dynamic lighting and shadow rendering in Web3D cloud rendering, optimized through analysis of various data such as algorithm frame rate, the operating efficiency of the device running the algorithm, and the lighting and shadow rendering results.

The present invention relates to a method for allocating resources by means of a hybrid computing system (10) comprising a computer (12) and a quantum computing device (14), the method comprising: - providing a set of elements comprising the tasks to be performed by a set of resources, the performance capacity of each resource and the operating constraint on the resources for performing the tasks; - encoding, by means of the computer (12), the elements in the form of a unit disk graph; - solving the unit disk graph by means of the quantum computing device (14) in order to obtain a solution; and - converting, by means of the computer (12), the obtained solution into a starting time and a resource allocation for each task complying with the operating constraint.

A device, method, and hybrid computing architecture for a Symbolic Photonic Logic Gate (SPLG) system that performs data processing primarily in the optical domain. The system comprises a photonic substrate, including silicon-photonics, indium phosphide, or similar optically active materials, patterned with optical waveguides and interferometric structures configured to implement logic operations through controlled optical interference, phase modulation, or non-linear optical effects. The SPLG system is configured to execute Boolean and symbolic logic functions without requiring intermediate optical-to-electrical signal conversion. In operation, information is encoded into one or more optical parameters including phase, polarization, wavelength, or amplitude, and processed within a Symbolic Optical Core optimized for linear algebraic operations such as matrix multiplication and vector transformation. The architecture further comprises an electronic control layer configured to initialize, configure, and monitor the photonic logic elements while allowing the majority of computational operations to occur within the optical domain, thereby reducing electrical interconnect congestion and resistive heating relative to fully electronic processors.

The invention provides a hybrid computing task classification method and device, equipment and a medium, and the method comprises the steps: carrying out the grammar analysis of an input hybrid computing code, generating a tree structure reflecting a code structure, and extracting subtask units and unit information in the hybrid computing code based on the tree structure; extracting feature words of the unit information according to the unit information; obtaining a subtask type of a predefined hybrid computing task and a corresponding keyword; and respectively mapping the keywords and the feature words into semantic vectors, and calculating semantic similarity to determine the subtask type of the subtask unit. The tree structure is obtained through the abstract syntax tree algorithm, so that the hybrid computing code is analyzed, the function definition and the dependency relationship thereof are accurately extracted, and subjectivity and errors possibly brought by manual intervention are avoided.

The invention relates to an intelligent calculation method for receivable debts based on dynamic data aggregation. The method comprises the following steps: calling a dynamic data acquisition and integration module, obtaining various preset types of receipts in real time, establishing a data association relationship, and aggregating dispersed receipt data; calling a dynamic algorithm scheduling engine, and dynamically calling an adaptive calculation model according to the real-time state of the document; and calling a mixed data processing module, and when it is detected that the preset rule is updated, recalculating the historical data of the document to ensure the continuity and integrity of the data. According to the invention, real-time collection and processing of multi-type document data are realized, real-time calculation of receivable debts is carried out according to a document state accurate scheduling algorithm, and timeliness and accuracy of the data are ensured; hybrid calculation of historical data and optimized data is supported, data coherence and integrity are guaranteed, and powerful support is provided for long-term financial analysis; and the overall operation management efficiency can be effectively improved.

The invention discloses a dual encryption method based on MD5 and an asynchronous request, and relates to the technical field of data security, and the method comprises the step of establishing a dynamic salt value generation model, an asynchronous request processing model, a block chain storage verification model, an edge calculation encryption model and a dual inspection model. Through cross-domain technology fusion advantages, biological characteristics are used as a part of a dynamic salt value, double authentication of'behavior + physical characteristics' is realized, through an edge-cloud collaborative architecture hybrid calculation mode, data encryption processing efficiency is improved, hash security is enhanced through the dynamic salt value, efficiency and stability are improved through asynchronous transmission, and finally, the data encryption processing efficiency is improved through the edge-cloud collaborative architecture hybrid calculation mode. Through collaborative design of a dynamic salt value, asynchronous transmission, a block chain, edge calculation and dual verification, a multi-level and high-robustness data encryption transmission system is constructed.

The invention provides a quantum-classical hybrid compiling method and system, electronic equipment and a storage medium, and the method comprises the steps: receiving a source file containing a quantum operation and a classical three-state operation, converting an operation instruction in the source file into a unified intermediate representation based on a preset grammar framework, obtaining an intermediate representation file, and storing the intermediate representation file in a storage medium; performing instruction optimization on the quantum operation instruction and the classical operation instruction in the intermediate representation file to obtain an optimized instruction stream, encoding the optimized instruction stream into executable binary instructions, and applying a space-time alignment constraint on a binary instruction corresponding to the quantum operation and a binary instruction corresponding to the classical operation in the binary instructions to obtain an executable binary instruction; quantum operation and classical operation are aligned in a physical clock period, and a more efficient and reliable compiling solution is provided for a quantum-classical hybrid computing system.

Calculation control for hybrid computing of Hamiltonian eigensolutions may be provided by selecting K basis states from an ansatz space of a chemical system, wherein the ansatz space is generated by a quantum computer system and includes fewer basis states than a whole basis space for the chemical system, wherein the K basis states are selected according to a selection protocol to define a core space for the chemical system; computing, via an eigensolver provided by a classical computer system, an eigensolution for the chemical system from the core space; and outputting the eigensolution for the chemical system.

This invention relates to the field of graph computing, and particularly to a method and apparatus for using data in a hybrid graph computing mode. It mainly includes: partitioning vertices according to vertex and edge weights, assigning vertices in each partition to corresponding computing nodes; storing the partitioned vertices in each computing node, using a row-column sparse compression matrix to store the adjacency relationships between vertices and edges, and reconstructing the vertices and edges stored in each computing node according to the adjacency relationships so that the reconstructed vertices and edges can adapt to all computing modes; obtaining the current computing mode based on the number of currently active vertices and edges, and using the reconstructed vertices and edges for data processing under the current computing mode. This invention can flexibly and efficiently convert the storage representation of graph data in a hybrid graph computing mode system, meeting the computational pre-processing requirements of different graph computing modes.

The invention provides a high-energy-efficiency digital-analog hybrid CMOS Boolean satisfiability problem solver chip which comprises a solver core, an SRAM peripheral read-write drive circuit and a parallel-to-serial read-out circuit, the SRAM peripheral read-write drive circuit is connected with a processor core and used for reading and writing data, the parallel-to-serial read-out circuit is used for reading a final solution, and the solver core comprises a 218 * 50 processing unit array. The processing units in each column are interconnected through charge sharing lines so as to complete accumulation operation in a charge domain. The processing units in each row are interconnected through TCAM matching lines, and the word and sentence evaluation operation is completed. In addition, a graph coloring circuit is also integrated for grouping Boolean variables. In each iteration, the Boolean variables in the same group can be updated at the same time, so that the parallel computing capability of the solver is improved. The energy efficiency is improved by using a digital-analog hybrid calculation mode, and the degree of parallelism is improved by using a packet updating strategy.

The invention belongs to the technical field related to information analysis, and discloses a flow graph processing method and processing system based on sub-graph hybrid computation.The processing method comprises the steps that an original flow graph in a graph database is divided into a plurality of sub-graphs with the vertex total weight distributed in a balanced mode; executing a graph updating operation on the sub-graphs; matching a corresponding graph calculation mode for each updated sub-graph, matching the corresponding updated sub-graph with a full-amount calculation mode when the proportion of the vertexes influenced by the updating operation in the updated sub-graphs exceeds a preset proportion, and otherwise, matching with an incremental calculation mode; extracting a dependency relationship between the sub-graphs; performing graph calculation on each updated sub-graph in parallel by adopting the matched graph calculation mode to respectively obtain an intermediate result of each sub-graph; and transmitting and updating the intermediate result of each sub-graph based on the dependency relationship, and outputting a global processing result of the flow graph. According to the processing mode, the locality characteristic of the graph can be fully utilized, the calculation overhead is reduced, and the accuracy of a calculation result is ensured.

Network optimization for arranging computational sub-tasks in a hybrid-computing environment is provided. The method comprises receiving input of a network of nodes and edges representing computational processes and their constituent information, wherein the nodes are grouped according to whether the nodes use classical computing resources or quantum computing resources. The method generates workflow constraints, scheduling constraints and computing resource assignment constraints. The method generates an objective function. An optimization problem is solved according to the objective function and all said constraints. The solution determines a best computational objective achieved, a selected computational workflow through the nodes, compute job scheduling, and assignment of the computational processes among the classical computing resources and quantum computing resources. The computational workflow is then executed to achieve the best computational objective according to the computed job scheduling and assignment of computational processes among the classical computing resources and quantum computing resources.

To provide a hybrid computing resource optimization model.SOLUTION: A method includes receiving input of a network of nodes and edges representing a computational process and their configuration information, generating workflow constraints, scheduling constraints, computing resource allocation constraints, and an objective function, solving an optimization problem according to the generated objective function and all constraints, determining an optimal computational objective to be achieved, a selected computational workflow through the nodes, computational job scheduling, and allocation of computational processes among classical computing resources and quantum computing resources, and executing a computational workflow to achieve the optimal computational objective according to the computational job scheduling and the allocation of computational processes between classical computing resources and quantum computing resources.SELECTED DRAWING: Figure 4

A compute device includes one or more volatile memory (VM) dies and a non-volatile memory (NVM) die, stacked with the VM dies. The NVM die includes a wide-bit input / output (I / O) interface. Through-silicon vias (TSVs) are formed through the VM dies and the NVM die and interconnected through microbumps. A logic die is stacked with the VM dies and the NVM die. The wide-bit I / O interface is configured to handle data transfers over the TSVs to and from the VM dies, through the logic die, at one of a data rate of the VM dies or at a reduced rate compared to the data rate of the VM dies.