98 results about "Quadratic unconstrained binary optimization" patented technology

The invention discloses a beam control optimization method and system for a millimeter wave network, and relates to the technical field of beam control optimization, and the method comprises the following steps: employing a DTW algorithm dynamic time warping algorithm to match multi-hop path time delay characteristics between a base station and user equipment, and screening an optimal reflection path sequence; obtaining path topology, constructing a secondary unconstrained binary optimization model, and obtaining discrete phase distribution through quantum annealing solution; calculating a quantization error of the discrete phase and performing frequency domain compensation by adopting an interpolation algorithm to obtain a phase compensation vector; loading the compensation vector to programmable metasurface hardware to form a reflection link, and constructing a phase error covariance matrix; correcting the weight parameter increment of the optimization model based on the matrix; and finally, a target function is constructed in combination with the correction weight and the compensation vector, channel capacity optimization is realized, the problems of difficult multi-hop path selection, insufficient phase error compensation and high beam optimization complexity in millimeter wave communication are solved, and the channel capacity and the communication quality are improved.

The invention relates to a satellite cabin load layout optimization method based on a quantum algorithm, and the method comprises the following steps: listing a mathematical expression form of an optimization target and a limiting condition according to a satellite cabin load layout requirement, and generating a constraint optimization problem model; wherein the optimization target and the limiting condition at least comprise a satellite quality characteristic requirement, a space geometric compatibility requirement, a thermal control requirement, an electromagnetic compatibility requirement, an installation and maintenance requirement and an installation position requirement of special instrument equipment; converting the layout constraint optimization problem mathematical model into an optimization objective function in a secondary unconstrained binary optimization form; solving the optimization objective function by using a quantum optimization algorithm to obtain an optimization vector; and calculating the load position and attitude of the satellite cabin by optimizing the vector, and carrying out inversion to obtain a final satellite cabin load layout scheme. According to the method, the non-deterministic polynomial problem of satellite cabin load layout optimization can be solved in an accelerated manner by utilizing the characteristics of superposition, entanglement, interference and the like of quantum bits, and a new technical approach is provided for improving the design efficiency of a satellite cabin load layout optimization scheme.

A system and method include reformulating an optimization program as a Quadratic Unconstrained Binary Optimization (QUBO) model and in a single iteration, solving the optimization program by inputting the QUBO model into a quantum computing solver, instructing the quantum computing solver to generate a plurality of solutions to the optimization program based on the QUBO model, receiving the plurality of solutions from the quantum computing solver, inputting each of the plurality of solutions into a nonquantum computing solver, wherein the nonquantum computing solver uses each of the plurality of solutions as a starting point to continue solving the optimization program, and outputting an optimal solution to the optimization program from the nonquantum computing solver.

The invention discloses a quantum Isin model construction method for a security constraint unit commitment optimization problem, and relates to the field of quantum computation.The quantum Isin model construction method comprises the steps that a security constraint unit commitment optimization model is constructed, and parameters of a mixed integer programming problem are obtained; using a Benders decomposition method to decompose a mixed integer programming problem into a main problem and a sub-problem; substituting the optimal binary solution to solve the sub-problem to obtain a new cut plane and expand a cut plane set; constructing a compact high-dimensional quadratic function fitting cutting plane set; solving a positive semidefinite programming problem to obtain a high-dimensional quadratic function parameter; converting a quadratic unconstrained binary optimization model constructed based on a high-dimensional quadratic function into an Isin model; solving the Isin model to obtain a quantum bit state, and solving an optimal binary solution; and substituting the optimal binary solution into the above steps for iterative solution. According to the invention, the problem of huge consumption of quantum bit resources in the prior art is solved, especially the problem of difficulty in processing NP with complex constraints and more variables is solved.

The invention relates to a power distribution network frame optimization method based on a quantum annealing algorithm. The method comprises the following steps: constructing an objective function of power distribution network frame optimization based on actual power distribution network data and setting constraints; obtaining a quantum Hamiltonian function based on the objective function and the constraint; based on the quantum Hamiltonian function, obtaining a secondary unconstrained binary optimization objective function of power distribution network frame optimization; based on the quantum Hamiltonian function or the quadratic unconstrained binary optimization objective function, solving a power distribution network frame optimization optimal solution; and generating a power distribution network frame optimization strategy based on the optimal solution. Compared with the prior art, the method has the advantages of higher calculation efficiency and precision and the like.

Optimization problems, such as QUBO problems, can be solved using quantum or classical device. When solving using a classical device, GPUs are used to solve the problem. To optimize GPU usage and explore a deeper solution space, a genetic algorithm using an island model is used to generate an initial solution comprising bundles of close vectors and their associated energies. Simulated annealing is performed and initialized by the initial solution of the genetic algorithm. The simulated annealing process is combined with a student-teacher technique, in which teacher bundles are combined to form a student bundle that is subject to the simulated annealing process. The initialization of the simulated annealing processing using the initial solution from the genetic algorithm enhances the probability of identifying a solution close to the global minimum, while the student-teacher technique provides for deeper exploration of the solution space.

The invention discloses a renewable energy source data clustering method and system based on an optical quantum computer, and the method comprises the steps: building a renewable energy source data clustering optimization problem based on a quadratic unconstrained binary optimization (QUBO) model based on a discrete optimization objective function, carrying out the matrix of the objective function, carrying out the clustering optimization of the objective function through a kernel method, and carrying out the clustering optimization of the objective function. The method comprises the following steps of: establishing a target function item, converting the target function item into a matrix form, constructing a matrix of a new clustering model, finally solving a new QUBO optimization problem by using CIM, and converting the new QUBO optimization problem into an optimal clustering result. According to the method, data similarity measurement is improved by constructing the QUBO model and combining a kernel method, and a renewable energy source data clustering problem is converted into a form suitable for CIM solution, so that the calculation bottleneck of a classical calculation method in high-dimensional data processing is avoided. According to the method, the nonlinear characteristic of the data can be kept, efficient clustering optimization calculation can be realized, and the calculation efficiency and the clustering precision are remarkably improved.

The invention discloses a precoding matrix optimization method based on a multi-user MIMO system, and the method comprises the steps: obtaining a channel matrix through a base station channel estimation module, and defining a non-convex optimization problem of multi-user precoding; converting a non-convex optimization problem into a quantum annealing solvable secondary unconstrained binary optimization model, and dynamically reflecting a channel interference relationship through quantum bit coupling strength; designing a post-processing algorithm, compensating quantum hardware noise by using gradient descent and a nonlinear function of a power amplifier, and optimizing an output pre-coding matrix; the method overcomes the limitation that a traditional method is prone to falling into local optimum in non-convex optimization, can adapt to complex interference scenes in real time, and is suitable for a current 5G environment and a future wireless communication system.

The invention discloses a path planning method based on a quadratic unconstrained binary optimization model, which belongs to the technical field of path planning, is used for unmanned system navigation, and comprises the following steps: rasterizing an environment area, determining a starting point, an ending point and the positions of barrier grids, determining a set of the grids where barriers are located, and defining grid binary variables; defining a target function, and establishing an optimization problem of the target function under the conditions of adjacent grid constraints, in-out constraints, obstacle avoidance constraints and starting point and terminal point position constraints; converting the constrained optimization problem into a quadratic unconstrained binary optimization model, solving the quadratic unconstrained binary optimization model, determining the value of each grid variable according to the value of the independent variable when the target function takes the minimum value, and finally obtaining a planned path. According to the method, multiple complex constraint conditions are directly embedded into the target function through the penalty term, the complex constraint conditions do not need to be independently processed, and the logic of the solving process is simplified.

The invention relates to a multi-machine multi-task configuration method and device, a medium and equipment, belongs to the technical field of task management optimization, and solves the problem that a large-scale multi-machine task configuration problem cannot be effectively solved in the prior art. According to the technical scheme, the method mainly comprises the steps that a task set and a machine set are obtained, and needed processing time corresponding to all tasks in the task set is obtained; constructing a task configuration binary variable to represent whether any task is configured to any machine; an optimization model is constructed according to a preset configuration target, the optimization model comprises a target function and constraint conditions, and the constraint conditions are expressed as constraint expressions through required processing time and task configuration binary variables; forming a penalty term according to the constraint formula, and adding the penalty term into the target function to form a quadratic unconstrained binary optimization model; and solving the quadratic unconstrained binary optimization model to determine the value of each task configuration binary variable, and determining a task configuration scheme according to the values of the task configuration binary variables.

The invention discloses a quantum method and device for processing database multi-query optimization and a storage medium. The quantum method comprises the steps that database multi-query optimization problem expression is converted into a secondary unconstrained binary optimization problem; running a quantum circuit on a quantum computer, and calculating an energy value corresponding to Hamiltonian according to a test state prepared in a quantum processor; summing the energy values on a classical computer, and optimizing parameters of the quantum circuit through a classical optimizer according to a summing result; and iteratively executing the steps of quantum circuit operation, energy calculation and parameter optimization until an energy value converges to a stable value, obtaining an optimal solution of a multi-query optimization problem, and solving the quadratic unconstrained binary optimization model in a quantum-classical hybrid framework by the variable component sub-feature solver algorithm. According to the method, the quantum computer is introduced into the database optimizer, the multi-query optimization problem is solved by using a variable component subscript solicitation solver algorithm, and a problem modeling strategy is improved.

The invention relates to an edge data caching method and device, a medium and equipment, belongs to the technical field of edge storage, and solves the problems that a caching scheme is difficult to determine and the scheme result effect is poor in the existing edge data caching process. According to the technical scheme, the method mainly comprises the steps of obtaining file data, server data and user data; constructing a to-be-selected cache scheme space, wherein the to-be-selected cache scheme space comprises a plurality of to-be-selected cache schemes; maximizing the delay reduction amount of the caching scheme as a target function; according to a preset condition required to be met by the caching scheme, constructing a constraint through a relationship among the first binary variable, the file data, the server data, the user data and the request delay reduction amount; forming a penalty term according to the constraint, and adding the penalty term into the target function to form a quadratic unconstrained binary optimization model; and solving the secondary unconstrained binary model to determine the value of each first binary variable, and determining the file cache position according to the values of the first binary variables.

The invention provides an environment and equipment cluster regulation and control method and system, electronic equipment and a storage medium, and the method comprises the steps: obtaining a physiological state vector based on physiological state data; performing empirical mode decomposition on the physiological state vector, and constructing a biological rhythm digital twinborn model; based on the physiological state data, the environmental parameters and the biological rhythm digital twinborn model, using a PC algorithm of a Pearl causal inference framework to discover a causal structure between the environmental parameters and the physiological state data, and constructing an environmental physiological causal relationship model; constructing a multi-objective optimization problem according to the biological rhythm digital twinborn model and the environmental physiological causal relationship model; the multi-objective optimization problem is converted into a quadratic unconstrained binary optimization function, and a Pareto optimal solution set is obtained for the quadratic unconstrained binary optimization function according to a quantum optimization algorithm; and regulating and controlling the environment and equipment cluster based on the Pareto optimal solution set and the biological rhythm digital twinborn model.

This invention relates to the field of quantum computer technology, specifically to a quantum state transmission method, device, and quantum computer. This application transforms the quantum circuit cutting problem into solving a quadratic unconstrained binary optimization model, finding a way to minimize the number of global gates required for transmission and the transmission cost. Based on minimizing the number of global gates required for transmission and the transmission cost, the quantum circuit is cut, and quantum state transmission is performed based on selected qubits as transmission bits. This reduces the frequency and communication cost of quantum communication within distributed quantum circuits and improves the computational efficiency of distributed quantum circuits.

One example method includes receiving a plurality of Quadratic Unconstrained Binary Optimization (QUBO) instances, the plurality of QUBO instances including at least first controllable features and their corresponding feature values and a first uncontrollable structure feature that defines a problem type of each of the plurality of QUBO instances. The first controllable features and the first uncontrollable structure feature of the received plurality of QUBO instances are used to train a QUBO generation machine-learning (ML) model to generate a QUBO instance. A QUBO feature set that includes second controllable features and their corresponding feature values and a second uncontrollable structure feature that defines a problem type of a new QUBO instance is received. The trained QUBO generation ML model generates the new QUBO instance that includes the second one or more controllable features and their corresponding feature values and the second uncontrollable structure feature.

The application is a QUBO problem solving method based on analog computing circuit, belonging to the field of analog computing and integrated circuit.The method combines the mathematical mapping of QUBO problem with the physical evolution process of analog computing circuit, maps the quadratic term matrix in QUBO problem into the product of resistance value in programmable resistance memory array and output current of current source module, maps the linear coefficient vector into threshold voltage of each row comparator, and takes the output voltage of each row comparator on feedback loop as the output of corresponding logic of state variable to be solved in QUBO problem, and relies on the natural evolution process of voltage in the circuit to realize the solution of QUBO problem in one step.The application does not depend on external digital circuit to provide clock, but updates in continuous time, has the advantages of fast speed, less peripheral circuit and low power consumption, and is suitable for the application scene of QUBO problem which is sensitive to power consumption and requires high solving speed.

The invention relates to a sparse principal component analysis method, a quantum computing device and a medium, belongs to the technical field of data dimension reduction, and solves the problem that the sparse principal component analysis method is low in efficiency. According to the technical scheme, the method mainly comprises the steps that a target data matrix is obtained, and a covariance matrix is determined based on the target data matrix; setting a to-be-solved principal component vector, and determining an original objective function and a vector constraint condition; expressing each component of the to-be-solved principal component vector through a binary system, and defining a plurality of second binary variables to respectively express whether each component of the to-be-solved principal component vector is zero or not; determining constraint conditions of the first binary variable and the second binary variable according to the vector constraint conditions and the definition of the second binary variable; forming a secondary unconstrained binary optimization model; and solving the quadratic unconstrained binary optimization model to determine the value of each first binary variable.

The invention discloses an electric power spot minimum electricity purchase cost function fitting method adaptive to quantum computing, and belongs to the crossing field of electric power system optimization and quantum computing, and the method comprises the steps: building a security constraint unit commitment optimization model based on electric power system parameters; based on the security constraint unit commitment optimization model, constructing a training sample set of a unit startup state and the minimum power purchase cost; based on the training sample set, fitting an explicit function relationship between the minimum power purchase cost and the unit starting state by adopting a kernel function enhanced elastic network regression algorithm; and converting the explicit function relationship into a quadratic unconstrained binary optimization model adaptive to quantum calculation, and solving the quadratic unconstrained binary optimization model to realize power spot minimum power purchase cost function fitting adaptive to quantum calculation. According to the method, the calculation efficiency bottleneck in clearing of the electric power spot market and the objective function dominant expression problem in quantum calculation adaptation of an existing method are solved.

A method is disclosed for optimizing Quadratic Unconstrained Binary Optimization (QUBO) instances for efficient execution on a quantum computer. Initially, a first machine learning (ML) model receives a QUBO instance along with scaling and scheduling parameters. The first ML model transforms the QUBO instance into a scaled version and adjusts the scheduling parameters accordingly. Subsequently, a second ML model compares the scaled QUBO instance and parameters with those of a known efficiently executable QUBO instance on a quantum computer. Based on this comparison, the second ML model assigns a score to the scaled QUBO instance, indicating its efficiency for execution on the quantum computer. This method enables the optimization of QUBO instances for enhanced quantum computing performance.

The application discloses a kind of multilayer feedforward neural network training methods for Ising machine, the method includes supervision learning task construction, problem form conversion, network parameter solution;Wherein supervision learning task construction is the modeling of quadratic constrained binary optimization problem on training data set and quantization neural network parameter;Wherein problem form conversion is the quadratic unconstrained binary optimization problem of quadratic constrained binary optimization problem constructed into;Wherein network parameter solution is the optimal solution of quadratic unconstrained binary optimization problem on Ising machine, and optimal solution is decoded to obtain optimal quantization neural network parameter, and the multilayer feedforward neural network of training is obtained.The application realizes the multilayer feedforward neural network of training on Ising machine, and provides the alternative method of traditional back propagation method as a kind of non-gradient training method.

The invention relates to a feature selection method and device based on a quantum algorithm, and the method comprises the steps: setting a feature set as F = {f1, f2,..., fn}, setting a feature subset S, enabling the feature subset S to meet # imgabs0 #, calculating first mutual information between a feature fi and a target variable y, and obtaining the importance Ii of the feature fi; second mutual information between the features fi and fj is calculated, and the redundancy Rij between the feature pairs (fi, fj) is obtained; constructing an objective function h (z) of a quadratic unconstrained binary optimization problem according to the importance Ii and the redundancy Rij; solving a quadratic unconstrained binary optimization problem by adopting a quantum approximate optimization algorithm, outputting a binary vector z, and decoding an output result to obtain a feature subset S; by introducing quantum calculation, a plurality of states are simultaneously represented in a quantum space, and a large number of possible feature combinations are processed, so that the calculation time of feature selection is greatly shortened, and the global optimization capability is enhanced.

The present invention discloses a beam steering optimization method and system for millimeter wave networks, relating to the technical field of beam steering optimization, and comprising the following steps: using a DTW algorithm and a dynamic time warping algorithm to match the multi-hop path delay characteristics between a base station and a user device, and screening the optimal reflection path sequence; obtaining the path topology, constructing a quadratic unconstrained binary optimization model and solving it through quantum annealing to obtain a discrete phase distribution; calculating the quantization error of the discrete phase and using an interpolation algorithm to compensate in the frequency domain to obtain a phase compensation vector; loading the compensation vector into programmable metasurface hardware to form a reflection link, and constructing a phase error covariance matrix; incrementally correcting the weight parameters of the optimization model based on the matrix; finally, combining the corrected weights and the compensation vector to construct an objective function to achieve channel capacity optimization, thereby solving the problems of difficult multi-hop path selection, insufficient phase error compensation, and high complexity of beam optimization in millimeter wave communications, and improving channel capacity and communication quality.

One example method includes receiving a Quadratic Unconstrained Binary Optimization (QUBO) problem that comprises a matrix that includes various cells having data. It is then determined that one or more of cells is missing data or has corrupted data. A machine learning (ML) model performs a denoising process that removes random noise from the one or more cells having the missing data or corrupted data. This results in data being imputed to the one or more cells having the missing data or the corrupted data. The imputed data approximates the missing data or approximates an expected value of the corrupted data before the corrupted data was corrupted.

This cost function generation device comprises: a parameter information acquisition unit that acquires parameter information including N (an integer of 1 or more) as a parameter; and a function generation unit that generates, on the basis of the parameter information, a cost function used for calculating a generator start / stop schedule of a plurality of generators over a plurality of consecutive time frames by quadratic unconstrained binary optimization and including a plurality of binary variables. The cost function includes a power generation amount for each time frame and for each generator. The plurality of binary variables include N first binary variables for each time frame and for each generator. The power generation amount is expressed using any of 2 to the Nth power patterns of the N first binary variables.

This invention provides a large-scale single-machine job scheduling method and system based on quantum computing, belonging to the field of single-machine job scheduling technology. The method includes: determining an integer programming model for single-machine job scheduling; converting the integer programming model into a quadratic unconstrained binary optimization model; designing a fast decomposition and mapping mechanism for the large-scale scheduling problem to decompose the problem; using quantum computing equipment to solve the quadratic unconstrained binary optimization models corresponding to all decomposed sub-problems to obtain optimized solutions for each sub-problem; designing an optimized solution correction and decoding mechanism to decode the optimized solutions for each sub-problem; and integrating and combining the scheduling schemes of all decoded sub-problems to form an optimized scheduling scheme for the large-scale single-machine job scheduling problem. This effectively solves the contradiction between response speed and optimization effect in large-scale scheduling problems, balancing optimization accuracy and response speed, and ensuring efficient, reliable, and stable operation of single-machine jobs.

A method for determining a pulse sequence for a quantum processor (2) is specified for solving a Quadratic Unconstrained Binary Optimization, QUBO, problem, comprising: - providing an initial coupling matrix characteristic of a coupling of at least some qubits of the quantum processor (2) and an initial cost matrix characteristic of the QUBO problem, - determining a rearranged cost matrix by rearranging at least some elements of the initial cost matrix dependent on a distance to the initial coupling matrix, - determining an adjusted coupling matrix by adjusting at least some elements of the initial coupling matrix dependent on a further distance of the initial coupling matrix to the rearranged cost matrix, - determining several sub-coupling matrices dependent on the adjusted coupling matrix, and - determining the pulse sequence dependent on the sub-coupling matrices. Further, a device (6), a computer program and a computer-readable storage medium are specified.

The application discloses a power spot market minimum power purchase cost function fitting method suitable for quantum calculation, belongs to the cross field of power system optimization and quantum calculation, and comprises the following steps: constructing a security constrained unit commitment optimization model based on power system parameters; constructing a training sample set of unit start state and minimum power purchase cost based on the security constrained unit commitment optimization model; fitting an explicit function relationship between the minimum power purchase cost and the unit start state by adopting a kernel function enhanced elastic net regression algorithm based on the training sample set; converting the explicit function relationship into a quadratic unconstrained binary optimization model suitable for quantum calculation, and solving the quadratic unconstrained binary optimization model, so that the power spot market minimum power purchase cost function fitting suitable for quantum calculation is realized. The application solves the calculation efficiency bottleneck of the existing method in the power spot market clearing and the difficulty in explicit expression of a target function in quantum calculation adaptation.

The application discloses a kind of data integration management methods and systems for automobile digital twin factory, it is related to data integration technical field, including, through industrial internet of things equipment and edge computing node, collect full-element real-time data stream and carry out pre-processing, utilize knowledge graph to construct semantic mapping rule, output multimodal data set;Build multiscale digital twin model, generate plant state matrix, the multiscale digital twin model includes geometric layer, physical layer and metabolic layer;Constitute quadratic unconstrained binary optimization model, obtain scheduling scheme by quantum annealing algorithm solution;Drive metabolic layer to carry out energy demand prediction, trigger dynamic adjustment based on model predictive control, output real-time regulation instruction;Through digital thread synchronization real-time regulation instruction to physical equipment, refresh multiscale digital twin model state.The application realizes the accurate mapping of digital twin model by fusing geometric space calibration, physical mechanism simulation and dynamic metabolic analysis.

A method for driving a quantum computational network for determining an extremal value of a cost function for solutions of a quadratic unconstrained binary optimization problem includes initializing qubits, sequentially applying layers of quantum gates to the qubits, determining an output state of the quantum computational network for obtaining a solution associated with the set of variational parameters {right arrow over (θ)}, and determining an output state for shifted variational parameters {right arrow over (θ)}* to evaluate a partial derivative with respect to the subset of the variational parameters {right arrow over (θ)} for determining a gradient of the cost function based on the output state for the shifted variational parameters {right arrow over (θ)}*, and by updating the variational parameters {right arrow over (θ)} based on an update function of a moving average over the gradient of the cost function and of a moving average over the squared gradient of the cost function.

A cost function generation device includes: a parameter information acquisition unit that acquires parameter information including N (an integer equal to or more than 1) as a parameter; and a function generation unit that generates, based on the parameter information, a cost function including plural binary variables for calculating a unit commitment schedule for plural power generating units over plural consecutive time frames by quadratic unconstrained binary optimization, wherein the cost function includes the amount of power generation in each of the time frames and each of the power generating units, the plural binary variables include N first binary variables in each of the time frames and each of the power generating units, and the amount of power generation is expressed using any one of 2N patterns with the N first binary variables.