5450results about "Quantum computers" patented technology

The embodiment of the invention provides an electro-optical modulator and a light quantum computer. The electro-optical modulator comprises an electrode assembly and an optical waveguide assembly. The electrode assembly comprises a positive signal electrode, a negative signal electrode, a first grounding electrode and a second grounding electrode, and the positive signal electrode and the negative signal electrode are arranged between the first grounding electrode and the second grounding electrode. The optical waveguide assembly is configured to bear optical signals and comprises a first optical waveguide and a second optical waveguide, the first optical waveguide is located between the positive signal electrode and the negative signal electrode and used for bearing first optical signals, and the second optical waveguide is located between the first grounding electrode and the positive signal electrode or between the second grounding electrode and the negative signal electrode and used for bearing second optical signals. The carrier is used for bearing second optical signals. Constant-amplitude voltage signals with opposite phases are applied to the positive signal electrode and the negative signal electrode which are tightly coupled, so that the modulation efficiency can be improved, and the common-mode noise can be reduced.

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 invention relates to the technical field of electric power intelligent management, and discloses a cooperative scheduling method and system for a virtual power plant, and the method comprises the following steps: S1, collecting the real-time data of each distributed power supply, each load and an energy storage system in the virtual power plant, and carrying out the ultra-short-term prediction, and obtaining a prediction parameter; s2, dynamically calculating the dynamic operation boundary of the energy storage system based on the real-time state of the energy storage system; s3, on the day before the current operation day, generating a pre-scheduling plan through collaborative decision making of a multi-target fuzzy satisfaction function; s4, in the current running day, taking the pre-scheduling plan as a reference, updating boundaries and prediction parameters in a rolling manner, and generating a real-time scheduling instruction through model prediction; and S5, monitoring the deviation between the actual output of each resource and the real-time scheduling instruction in real time, and when the deviation exceeds a threshold value, starting a collaborative deviation compensation mechanism to carry out power balance. According to the invention, fine cooperative scheduling of different types of distributed resources can be realized in a complex environment with high uncertainty.

The invention relates to the technical field of combustion monitoring, and discloses a combustion instability boundary prediction system based on complex environment condition analysis, and the system comprises a multi-source sensing module, a data fusion module, an anti-interference processing module, an instability feature library, an intelligent decision engine and a digital twinborn body. A dynamic environment interference compensation mechanism is constructed, spectrum correction parameters are configured according to different environment working conditions during real-time prediction of a combustion instability boundary, the problem of spectrum distortion caused by environment disturbance on combustion dynamic parameters is eliminated in real time, spectrum interference deviation of a combustion measurement position can be automatically sensed and corrected, and real-time prediction of the combustion instability boundary is realized. The accuracy of combustion instability sensitive feature extraction is guaranteed, the instability early warning error is reduced, a flame pose real-time tracking system is deployed, fuel control parameter self-adaptive adjustment is triggered immediately when it is detected that the pose is abnormal, autonomous correction of the combustion instability feature recognition position is achieved, and it is guaranteed that the feature capture space is accurately positioned.

The invention discloses a distribution line broadband waveform sampling method, and belongs to the technical field of power system monitoring. By collecting quantum vacuum fluctuation noise, temperature, humidity and electromagnetic parameters, a quantum environment coupling entropy model is constructed, an optimal sparse base dictionary is dynamically generated, and a noise entropy break variable is calculated. And when the entropy break variable exceeds a threshold value, triggering sub-Nyquist compressed sampling, otherwise, adopting Nyquist rate sampling to realize dynamic matching of sampling resources and signal complexity. A high-frequency component of a compressed signal is reconstructed by combining an adversarial compressed sensing network with a residual network, a low-frequency component is generated based on an original signal, the distribution consistency of high and low frequency entropies is optimized by using a Wasserstein distance, and finally a three-dimensional waveform matrix containing a timestamp, a frequency bin and quantum entropy confidence is output. The problems of low sampling efficiency, poor sparse representation adaptability, slow noise response, inconsistent reconstruction and the like in a non-stationary signal and complex noise environment are solved, and the precision and energy efficiency of broadband waveform sampling are remarkably improved.

System, method, and computer program product embodiments detect synthetic media known as deepfakes based on received inferencing data that includes content such as a file or stream of audio, image, video, or textual chat data. The inferencing data further includes inferencing metadata associated with a source of the content. A data vector derived from one or more samples of the content and the inferencing metadata is transmitted to quantum computing hardware for quantum amplitude encoding of the data vector into a set of qubits, which are processed with a trained quantum support vector machine (SVM) to produce a classification of the content as synthetic or genuine. Based on a signal indicating that the content is synthetic, a notification or alert is generated and sent to a human user or an automated system warning that the content is classified as including deepfake data.

A robotic fleet resource provisioning system includes a computer-readable storage system storing a fleet resources data store and resource provisioning rules. The fleet resources data store maintains a fleet resource inventory indicating fleet resources, each with features, configuration requirements, and a status. The resource provisioning rules are accessible to an intelligence layer to ensure that provisioned resources comply with the resource provisioning rules. The system receives a request for a robotic fleet to perform a job and determine a job definition data structure. The definition data structure defines a set of tasks that are to be performed in performance of the job. The system determines a robotic fleet configuration data structure corresponding to the job based on the set of tasks and the fleet resource inventory. The system determines a respective provisioning configuration for each respective fleet resource. The system deploys the robotic fleet to perform the job.

The invention relates to the technical field of quantum computing, and discloses a quantum circuit mapping method and system based on deep reinforcement learning, according to the method, an original logic quantum circuit is analyzed and simplified into a ZX-graph, then the ZX-graph is converted into a quantum circuit dependency graph, the computing complexity is remarkably reduced, and the quantum circuit mapping efficiency is improved. A large-scale quantum circuit mapping task can be processed within reasonable time, a Markov decision process environment is constructed through a quantum circuit dependency graph and quantum chip parameters, a deep reinforcement learning agent is trained by using the Markov decision process environment, and an optimal mapping strategy optimization model is obtained. Through the optimal mapping strategy optimization model, the corresponding optimal quantum bit mapping scheme can be output to the current to-be-mapped logic line, so that the method can be adapted to quantum chips of various topological structures, and the generalization ability and adaptability of the mapping scheme are improved.

A raw material system includes a product manufacturing demand estimation system programmed to calculate an expected demand for a product at a future point in time. An environment detection system identifies at least one of an environmental condition or an environmental event. A raw material production system estimates a raw material availability at the future point in time based on the expected demand and the environmental condition / event. A raw material requirement system calculates a required raw material amount to manufacture the product at the future point in time based on the expected demand and the environmental condition / event. A raw material procurement system autonomously configures a futures contract for procurement of at least a portion of the required raw material amount in response to the required raw material amount calculation exceeding the raw material availability estimation.

The invention discloses a quantum fuzzy neural network adaptive to high-dimensional input and a classification method, and relates to the field of quantum calculation and fuzzy neural networks and the field of computer vision. The network input layer receives high-dimensional data, amplitude coding, forward and reverse enhanced chain entanglement layer, parameterized quantum transformation and fuzzy set mapping are carried out through a quantum fuzzy feature extraction module, and dynamic dimension fuzzy features are output; high-dimensional neural features are extracted through a DNN feature extraction module to adapt to quantum fuzzy feature dimensions; dynamically distributing the weights of the quantum fuzzy features and the classic neural features through an adaptive feature fusion module; and carrying out Softmax classification on the fusion features through a classifier, and outputting a category probability. According to the method, the high-dimensional data coding efficiency can be effectively improved, the complex fuzzy logic relation learning capability of the quantum part and the quantum state correlation stability are enhanced, the uncertainty of the data is represented, and accurate classification of high-dimensional uncertainty images is realized while noise interference is reduced.

The invention discloses a multi-modal large model training data acquisition method and system, and relates to the technical field of data acquisition optimization, and the method comprises the steps: building a causal graph adjacency matrix based on a cleaning alignment data set, carrying out the anti-fact intervention after recognizing a prejudice variable, and generating an anti-fact sample set; combining the anti-fact sample set and the cleaning alignment data set into an enhanced data set; performing cross-modal anti-long-tail compensation on the enhanced data set to obtain a balanced data set; based on the image data and the text data in the balance data set, depth separable convolution feature extraction and BERT semantic coding are carried out respectively, cross-modal fusion is carried out, and fusion features are output; through the steps of depth separable convolution, BERT coding, quantum latent variable evolution and cross-modal semantic verification, the generalization ability, robustness and social adaptability of the multi-modal large model in a complex and real scene are significantly improved.

The invention discloses an extended-range hybrid propulsion double-source dynamic coupling energy management method, which comprises the following steps: carrying out global physical modeling on a double-source power system and a flight scene, and establishing a double-source dynamic coupling model; designing a reinforcement learning physical constraint reward function, performing optimization training on each coefficient of the reward function by adopting a QMPSO algorithm, and outputting an optimized reward function coefficient; a qualified double-source dynamic coupling model is verified, and a power distribution coefficient is optimized; outputting the optimal power distribution coefficient of the battery and the range extender; the superiority of the dual-source power cooperative control strategy in the aspects of flight economy, operation stability and system life guarantee is verified through multi-dimensional comparative analysis of each performance index. Cooperative power distribution of the battery and the range extender is achieved through dynamic coupling modeling and reinforcement learning, the flight scene load requirement is met, the system energy efficiency is improved, and the service life of parts is prolonged.

The invention discloses a driving mechanism analysis method of reservoir group scheduling for flood non-consistent evolution, which comprises the following steps: acquiring a smooth reservoir entering flood sequence by utilizing a topologically coupled physical manifold constraint denoising model, and constructing dynamic topological characteristics representing propagation time lag and sensitivity through a hydraulic propagation mechanism-based Figure ordinary differential equation; constructing a structural causal model comprising a scheduling capability index, ectogenic hydrological driving and topological characteristics, and fitting a nonlinear dependency relationship by using a causal generalized additive model; executing anti-fact inference, and calculating a local causal driving index and a global causal cumulative effect index through a dry budget; and generating an optimal scheduling strategy for suppressing variation based on the causal index. According to the method, systematic analysis from data physical restoration to causal mechanism decoupling can be realized, the driving contribution of a scheduling behavior to flood inconsistency is accurately quantified, and decision support is provided for scientific flood control of a drainage basin.

An autonomous futures contract orchestration platform includes a set of processors programmed with a set of non-transitory computer-readable instructions. The instructions include receiving, from a data source, an indication associated with a product that relates to an entity that purchases or sells the product. The instructions include predicting a baseline cost of purchasing or selling the product at a future point in time based on the indication. The instructions include retrieving a futures cost, at a current point in time, of a futures contract for an obligation to the purchasing or selling the product for delivery or performance of the product at the future point in time. The instructions include executing a smart contract for the futures contract based on the baseline cost and the futures cost. The instructions include orchestrating the delivery or performance of the product at the future point in time.

The invention provides an integrated feature selection method and product based on quantum computing and Bayesian optimization, and relates to the technical field of data processing. According to the embodiment of the invention, an original mathematical integration model is converted into a QUBO model which can be solved by quantum calculation, and the QUBO model is decomposed into a joint optimization discretization step length optimization sub-problem and a parameterization QUBO sub-problem. In a mixed quantum classical optimization algorithm framework, a self-adaptive Q learning model is designed on the upper layer, and proper sub-problems can be dynamically selected in the search process. In the lower layer, a dropout Bayesian optimization algorithm is provided for effectively optimizing the high-dimensional discretization step length in each iteration. A CIM-based quantum computing method is adopted, and a parameterized QUBO sub-problem under the given discretization step length is efficiently solved. According to the method provided by the embodiment of the invention, the feature selection problem can be successfully and efficiently solved, and the selected features of the credit classification problem and the credit classification model for classification based on the selected features can be obtained.

An approach is disclosed for enabling contextually relevant conversational interaction. Environment data is received by an AI System which detects a plurality of physical objects in a physical environment and forms a contextual understanding of the plurality of physical objects and the physical environment and identifies a user relevant to the contextual understanding. A most relevant contextual information to the user is predicted by the AI system and transformed into a textual form. A set of intents and objectives is predicted by the AI system for user-centered interaction. The AI system and the user interact iteratively through the user-centered interaction to determine an understanding of a most relevant intent and a most relevant objective which is validated by the AI system with the user until the user agrees. The validated most relevant intent and the most relevant objective is utilized to facilitate the user-centered and contextually relevant conversational interaction.

An AI-based energy edge platform is provided herein with a wide range of features, components and capabilities for management and improvement of legacy infrastructure and coordination with distributed systems to support important use cases for a range of enterprises. The platform may incorporate emerging technologies to enable ecosystem and individual energy edge node efficiencies, agility, engagement, and profitability. Embodiments may forecast, plan for, and manage the demand and utilization of energy in greater distributed environments. Embodiments may use AI, IoT, and technologies that filter, process, and move data more effectively across communication networks. Embodiments of the platform may leverage energy market connection, communication, and transaction enablement platforms. Embodiments may employ intelligent provisioning, data aggregation, and analytics.

The present subject relates to a method for proving a statement describing a functionality of an electronic circuit, using a quantum computer and a proof assistant.The procedure comprises: encoding a current proof state into a vector of real numbers of a fixed length, where the current proof state defines a task to prove at least part of the statement; encoding the vector into a quantum state of a quantum system of the quantum computer; using the quantum state as an input quantum state by a quantum machine learning model to provide an output quantum state, the measurement of which constitutes a proof step for the defined task; measuring the output quantum state, thereby obtaining the proof step for the defined task; providing the proof step to the proof assistant; and receiving a next proof state from the proof assistant in response to the provision of the proof step.

The invention belongs to the technical field of cable processing, and relates to a preparation method and application of a flexible superconducting coaxial cable outer conductor. The preparation method comprises the following steps: mixing Nb powder and Ti powder by adopting a vibrating ball mill to prepare NbTi composite powder; a substrate of laser powder bed melting equipment is preheated, the NbTi composite powder is spread on the substrate of the laser powder bed melting equipment, a melting powder layer is scanned through the laser powder bed melting technology, a three-dimensional communicated grid structure is formed through interlayer staggered scanning, finally, the NbTi net-shaped alloy pipe is placed in a strong acid solution to be soaked for a short time, redundant metal powder is removed, and the NbTi net-shaped alloy pipe is obtained. And finally, the preparation of the flexible superconducting coaxial cable outer conductor for the quantum computer is realized.

A method, system, and computer program product for learning noise models to perform quantum error mitigation. Each target layer of a quantum circuit is divided into a set of sub-layers. Each of the sub-layers for each target layer of the quantum circuit is grouped into a reduced set of learning layers, which enables each sub-layer's noise model to be learned from fewer layers (learning layers). A learning layer refers to a layer that is used in combination with other learning layers to form the minimally complete layer set for learning all the layer components used in the quantum circuit. The noise models for each of the sub-layers are then learned on the reduced set of learning layers. Such learned noise models are combined to form a complete set of noise models for the target layers of the quantum circuit and used to perform quantum error mitigation on the quantum circuit.

According to the large power grid reactive power optimization method and device, the storage medium and the computer equipment provided by the invention, the advantages of the two algorithms are fully exerted through the hybrid chaos quantum particle swarm optimization algorithm and the dimension-by-dimension convex space search algorithm. According to the chaotic quantum particle swarm algorithm, the global search capability and the capability of jumping out of local optimum of a particle swarm are enhanced by utilizing the characteristics of quantum behaviors and chaotic mapping, and the problem of premature convergence of a traditional heuristic intelligent algorithm is avoided. And according to the dimension-by-dimension convex space search algorithm, fine search is carried out on each excellent particle in different dimensions, a local optimal solution is determined, and the search precision and efficiency are further improved. According to the design of the hybrid algorithm, special optimization is carried out aiming at the characteristics of a reactive power optimization problem model, such as variable property difference, constraint complexity and the like, and the technical defects of poor optimization effect and optimization efficiency of an optimization solution algorithm in the prior art are effectively overcome.

In some aspects, a cloud-based computer system includes: a quantum computing system comprising a quantum processing unit; a container management and execution system configured to receive a container and execute a program within the container; and a communication channel between the container management and execution system and the quantum computing system for providing program instructions to the quantum computing system. The container management and execution system and the quantum computer system may be co-located in a data center or located in different data centers. The latency of the communication channel may be selected to optimize cost for a required computer performance.

A control system for a network of quantum systems operates the network to perform quantum informatics processing. The control system includes an entanglement scheduler configured to identify operations for which entanglement of two or more of the quantum systems is desired, identify plural potential entanglement attempts for generating the entanglement; and schedule at least some of the potential entanglement attempts to be performed to generate the entanglement for at least one of the operations. The entanglement scheduler may use direct entanglement, entanglement swapping and / or quantum teleportation approaches to generate the entanglement. The entanglement scheduler may schedule probabilistic entanglement attempts based at least in part on the probability of success of the entanglement attempts. The entanglement scheduler may generate entanglement to support plural branches of a quantum informatics process and / or to respond to events occurring while the quantum informatics process is executing.

The invention discloses a multi-degree-of-freedom mechanical arm control method based on quantum particle swarm optimization of a migration strategy. The method comprises the steps that system modeling is conducted; mPC to-be-adjusted parameter definition and constraint processing are carried out; adopting, adopting, and introducing penalty terms to construct a composite fitness function; updating particle positions by adopting a basic or enhanced quantum updating mode; triggering conditions are judged in a group diversity measurement mode, and when the conditions are met, a migration strategy and dynamic subgroup division are executed; the global optimal solution and the fitness value are loaded into a real-time MPC controller, and online adjustment is carried out; a prediction equation is constructed, tracking errors and energy consumption optimization are converted into a standard quadratic programming problem, and the solving precision is dynamically adjusted in combination with Cholesky pre-decomposition, a structured sparse solver and a warm-start and early stop strategy; and a closed-loop adaptive control system is constructed. According to the method, the precision, robustness and real-time performance of trajectory tracking control of the mechanical arm can be effectively improved.

The invention provides an FPGA parallel Isin model optimization method and system based on a GHMC simulated annealing algorithm, relates to the field of power system combination optimization, and solves the technical problem that an existing FPGA-based Isin model solver adopts a traditional Monte Carlo method and is low in convergence precision and solution quality. The method comprises the following steps: receiving a question input by a user through an upper computer, and mapping the received question into a Hamiltonian parameter of an Isin model; the Hamiltonian parameter is transmitted to an FPGA through a communication interface, and a mixed GHMC sampler and a temperature scheduling module are arranged in the FPGA; the hybrid GHMC sampler carries out parallel solution on Hamiltonian parameters; and the temperature scheduling module gradually reduces the system temperature according to a preset annealing curve, and drives the Isin model to converge to a global optimal solution. The method is used in the power system combination optimization process.

The invention discloses a rolling bearing generalization fault diagnosis method based on a quantum physical information neural network, and the method comprises the following steps: S1, obtaining vibration signals and working condition parameters in the operation process of a rolling bearing, and carrying out the preprocessing and domain division to obtain a meta-training set and a meta-test set; s2, constructing a quantum-physical information neural network model which comprises a quantum path and a physical path; s3, feature fusion and classification; and S4, based on physical perception element learning training, repeating inner ring-outer ring iteration until the model is converged. Compared with the prior art, the rolling bearing fault diagnosis field generalization method based on the quantum physical information neural network has the advantage that the field offset problem existing in a traditional fault diagnosis method is solved.

System and method for hybrid analysis of quantum and classical genetic algorithms is disclosed. The method includes, receiving an input bitstring, the input bitstring being an output of a genetic optimization module, processing the input bitstring to generate quantum processed bitstrings, mutating the input bitstring, mutating the quantum processed bitstrings as a function of the input bitstring and the mutated input bitstring, performing crossover on a combination of the mutated input bitstring and the mutated quantum processed bitstrings, and selecting a set of individuals from the quantum processed bit strings and output of the crossover. The method further includes, determining, after selecting the set of individuals, if the hybrid analysis is complete or incomplete based on predetermined criteria, returning, in response to the hybrid analysis being incomplete, the set of individuals as the input bit string, else, outputting the set of individuals as a result of the hybrid analysis.

A method of estimating noise for individual gates of a quantum circuit uses averaged circuit eigenvalue sampling to determine an overall eigenvalue for the circuit. The circuit is sampled a sufficient number of times to fill a design matrix, wherein the design matrix allows for solving of a system of equations to determine eigenvalues for the individual gates using the averaged eigenvalue for the overall quantum circuit, the samples, and the sample results. Applying a logarithm to these relationships enables solving the system of equations using linear estimation or linear regression, in some embodiments.

The invention discloses a quantum bit mapping optimization method for hardware guidance and fidelity perception and a related device, and belongs to the field of quantum computing, and the method comprises the steps: defining each node in a hardware coupling graph of a target quantum processor as an initial community; evaluating all adjacent community pairs according to a reward function defined based on modularity and fidelity of candidate communities, selecting an optimal community pair for fusion, and taking the community with the largest current scale as an optimal candidate area; iteratively executing a community fusion step until a converged single community is obtained; and selecting an optimal candidate region with the physical quantum bit number greater than or equal to the logic quantum bit number of the quantum circuit to be mapped and the minimum scale, and outputting a mapping sub-graph. According to the method, through a recursive community fusion strategy, the operation range of the solver is restrained in a subspace with the scale greatly reduced and the quality better, the problem of combinatorial explosion caused by global search is fundamentally avoided, and the calculation overhead is greatly reduced.

The invention belongs to the field of quantum computing, and particularly relates to a distortion correction optimization method and device for a quantum measurement and control link, and the method comprises the steps: applying a frequency control signal for loading a target pre-distortion waveform on a to-be-corrected quantum measurement and control Z link; setting a first logic gate for quantum state operation on an XY control line of the selected quantum bit; obtaining the fidelity of the quantum state output by the selected quantum bit; in response to the fact that the fidelity does not reach the preset standard, updating the pre-distortion waveform, and returning to execute a frequency control signal for applying and loading the target pre-distortion waveform signal on the quantum measurement and control Z link to be corrected; and setting a first logic gate for quantum state operation on an XY control line of the selected quantum bit until the fidelity reaches a preset standard. According to the invention, distortion correction can be rapidly and effectively carried out.