58 results about "Quadratic unconstrained binary optimization" patented technology

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 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 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 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.

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.

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 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.

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 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.

The invention discloses a model training method, a computer, a storage medium and a program product. The method comprises the steps of obtaining a model training sample; constructing an optimization objective function of the filtering parameters a and b of the sample, and converting the optimization objective function into a standard form of a quadratic unconstrained binary optimization model of the filtering parameters a and b; calculating the standard form of the QUBO model by adopting a quantum optimization algorithm in each iterative operation of model training to obtain the optimal values of the filtering parameters a and b of each iterative operation; and performing sample filtering of training on the sample by using the optimal value to obtain a filtered sample, and performing iterative operation of model training by using the filtered sample.

The embodiment of the present disclosure relates to the technical field of distributed beamforming, and provides a distributed beamforming method and device, the method comprising: constructing an antenna array model satisfying a specific condition according to the positions and excitation phases of each antenna unit in the antenna array; constructing an objective function according to an optimization target; performing discrete processing on the objective function to obtain an objective function satisfying a quadratic unconstrained binary optimization problem form; using a coherent Ising machine and a simulated annealing algorithm respectively to solve the objective function satisfying the quadratic unconstrained binary optimization problem form to obtain corresponding coherent Ising machine solving results and simulated annealing solving results; comparing the coherent Ising machine solving results and the simulated annealing solving results to select the most suitable solving result; and converting the selected most suitable solving result into a phase parameter of the antenna array and optimizing the antenna array according to the phase parameter. The embodiment of the present disclosure can greatly improve the optimization effect and improve the optimization efficiency.

The application discloses an automatic theorem proving method based on neural network guidance and quantum optimization, comprising the following steps: extracting the logical structure features of a propositional logic task, and predicting the applicability weight of natural deduction rules by using a preconfigured neural network; performing a forward reasoning iteration process, dynamically identifying potential intermediate conclusions to determine a variable space, and constructing a quadratic unconstrained binary optimization (QUBO) model according to the variable space; in the construction process, the applicability weight is used to adjust the penalty coefficient of the rule constraint term, and the energy topography of the solution space is reshaped; according to the problem size, the computing resources are adaptively scheduled, the model is mapped to a coherent Ising machine or a classical simulation backend for solving, and the proof path is reconstructed through double verification. The application effectively solves the problems that the traditional method is blind in rule selection and the static optimization model is difficult to handle dynamic multi-step reasoning, and the efficiency and scalability of the proof are improved.

This invention discloses a method for constructing a quantum Ising model for safety-constrained unit combinatorial optimization problems, relating to the field of quantum computing. The method includes: constructing a safety-constrained unit combinatorial optimization model and obtaining parameters for a mixed-integer programming problem; using the Benders decomposition method to decompose the mixed-integer programming problem into a main problem and subproblems; substituting the optimal binary solution into the subproblems to obtain new cutting planes and expanding the set of cutting planes; constructing a compact high-dimensional quadratic function to fit the set of cutting planes; solving a semi-definite programming problem to obtain the parameters of the high-dimensional quadratic function; transforming the quadratic unconstrained binary optimization model constructed based on the high-dimensional quadratic function into an Ising model; solving the Ising model to obtain the qubit states and obtaining the optimal binary solution; and substituting the optimal binary solution into the above steps for iterative solving. This invention solves the problem of huge qubit resource consumption in existing technologies, especially in dealing with NP-hard problems with complex constraints and many variables.

The invention discloses a high-dimensional feature screening method and device based on quantum annealing, relates to the technical field of quantum computing, and realizes accurate and efficient feature screening in a high-dimensional financial data scene. The method comprises the following steps: acquiring data of a target financial variable and data of a to-be-preliminarily screened characteristic factor; constructing an initial total prediction linear term based on a linear term of the secondary unconstrained binary optimization model, and obtaining a selected to-be-primarily-screened characteristic factor as a to-be-finely-screened characteristic factor; based on a linear term and a quadratic term of the quadratic unconstrained binary optimization model, constructing a total prediction linear term and a redundant quadratic term, and obtaining a selected to-be-finely screened characteristic factor as a to-be-optimized characteristic factor; the permutation importance of each to-be-optimized feature factor is calculated, a target feature factor is screened from the to-be-optimized feature factors according to the permutation importance, and the target feature factor is used for predicting a target financial variable.

The application discloses a kind of based on machine learning's advertisement budget allocation method and system, it is related to advertisement putting technical field, including, based on multi-channel historical delivery data and real-time market characteristics, through heterogeneous ensemble machine learning model dynamically predicts the expected value and uncertainty measure of each advertisement channel under different budget level, generates probabilistic effect prediction matrix;With probabilistic effect prediction matrix and total budget constraint as input, construct quadratic unconstrained binary optimization model;After reconstruction, the quadratic unconstrained binary optimization model is submitted to quantum annealing processor to start a new round of iteration optimization;When adaptive convergence judgment agent determines to meet dynamic convergence condition, extract the highest ranking budget allocation scheme from the updated elite solution pool to execute delivery.The application realizes the accurate allocation and efficient solution of advertisement budget by fusing multi-source data and intelligent optimization algorithm, significantly improves the delivery benefit and reduces the calculation cost.

The embodiment of the invention relates to the technical field of distributed beam forming, and provides a distributed beam forming method and device, and the method comprises the steps: constructing an antenna array model meeting a specific condition according to the position and excitation phase of each antenna unit in an antenna array; constructing a target function according to the optimization target; discretizing the target function to obtain a target function satisfying a quadratic unconstrained binary optimization problem form; respectively using a coherent Isin machine and a simulated annealing algorithm to solve the objective function satisfying the secondary unconstrained binary optimization problem form to obtain a corresponding coherent Isin machine solving result and a corresponding simulated annealing solving result; comparing the solution result of the coherent Isin machine with the solution result of the simulated annealing, and selecting the most suitable solution result; and converting the selected most appropriate solution result into a phase parameter of the antenna array, and optimizing the antenna array according to the phase parameter. According to the embodiment of the invention, the optimization effect can be greatly improved, and the optimization efficiency is improved.

A cognitive radio device may include a radio frequency (RF) detector operable over an RF spectrum, an RF transmitter having a selectable hopping frequency window within the RF spectrum, and a controller. The controller may be configured to cooperate with the RF detector and RF transmitter to detect a jammer signal affecting a current hopping frequency window, determine different Quadratic Unconstrained Binary Optimization (QUBO) inputs based upon the detected jammer signal, process the QUBO inputs with a QUBO objective function to determine a new hopping frequency window, and operate the RF transmitter at the new hopping frequency window.

Decomposing problems such as quadratic unconstrained binary optimization problems is disclosed. A problem is decomposed into subproblems and the subproblems are classified into a first set of subproblems for classical annealers and a second set of subproblems for quantum annealers. The sets are processed to move subproblems from the second set to the first set. This is achieved by exchanging qubits among the subproblems and then reclassifying the modified subproblems. Moving subproblems from the second set to the first set reduces the need for quantum annealers. The subproblems in the two sets are executed and a solution to the original problem is determined from the solutions to the subproblems.

This invention discloses a qubit mapping method, apparatus, storage medium, and electronic device, applied in the field of quantum computing technology. The method includes: constructing a quadratic unconstrained binary optimization model for qubit mapping based on the coupling relationship between logical qubits and the topological relationship between physical qubits, where logical qubits are qubits in a quantum circuit and physical qubits are qubits in a quantum chip, with the quantum circuit running on the quantum chip; solving for the qubit mapping scheme that minimizes the quadratic unconstrained binary optimization model; and mapping logical qubits to physical qubits based on this mapping scheme. By using the quadratic unconstrained binary optimization model, the optimal mapping method for each qubit is obtained in a shorter time, effectively reducing the number of SWAP gates required for qubit mapping, reducing circuit depth, and improving execution speed and overall fidelity.

The application relates to the field of machine translation, in particular to a semantic compression method based on quantum computing. The method comprises the following steps: reconstructing a compression problem of a machine translation model into a quadratic unconstrained binary optimization (QUBO) problem, including: constructing an objective function of a machine translation task; mapping a pruning mask vector P and a quantization parameter vector Q into quantum bits; optimizing and solving the QUBO problem based on a quantum annealing device to obtain a compressed machine translation model; and performing semantic retention degree evaluation and optimization on the compressed machine translation model, so that the compression rate and BLEU value of the compressed machine translation model meet target compression rate and target BLEU value. The application can improve the efficiency and semantic retention degree of machine translation.

The invention relates to the technical field of quantum computing, and provides an investment portfolio optimization method and device adaptive to a neutral atom quantum computer, and the method comprises the steps: carrying out the quantum coding of an investment portfolio optimization problem, and constructing a secondary unconstrained binary optimization model; mapping the secondary unconstrained binary optimization model to a two-dimensional atom array of a neutral atom quantum computer, and constructing a native optimization Hamiltonian; based on the native optimized Hamiltonian, constructing a variable component sub-circuit adaptive to a neutral atom quantum computer system structure, and executing the variable component sub-circuit on a neutral atom quantum computer to obtain a quantum measurement result; and executing a quantum-classical hybrid optimization process based on the quantum measurement result to obtain optimization parameters for investment portfolio configuration. According to the method, a large amount of controlled NOT gate and switching operation is avoided, and the circuit complexity is reduced, so that the calculation efficiency, the adaptability and the engineering practicability of quantum calculation in an investment portfolio optimization problem are improved.

A cognitive radio device may include a radio frequency (RF) detector operable over an RF spectrum, an RF jammer having a selectable jamming frequency window within the RF spectrum, and a controller. The controller may be configured to cooperate with the RF detector and RF jammer to detect an RF transmission, determine different Quadratic Unconstrained Binary Optimization (QUBO) inputs based upon the detected RF transmission, process the QUBO inputs with a QUBO objective function to determine a new jamming frequency window, and operate the RF jammer at the new jamming frequency window.

The present disclosure relates to a method of driving a quantum computing network for determining an extremum of a cost function of a solution to a quadratic unconstrained binary optimization problem, the method comprising: initializing qubits; sequentially applying quantum gate layers to the qubits, wherein each layer comprises a multi-qubit quantum gate acting on a plurality of qubits and a plurality of variational quantum gates having two eigenvalues and a variable action on the qubits, wherein the variable actions of the variational quantum gates of the quantum gate layers form a set of variational parameters; and determining an output state of the quantum computing network to obtain a solution associated with the set of variational parameters, wherein each binary variable of the quadratic unconstrained binary optimization problem is associated with a computational ground state of the register qubits and the solution is obtained by evaluating probabilities of measuring the computational ground states corresponding to the binary variables, and wherein the solution is iteratively improved by determining an output state of altered variational parameters to evaluate partial derivatives with respect to a subset of the variational parameters, thereby determining a gradient of the cost function based on the output state of the altered variational parameters; and updating the variational parameters based on an update function of a moving average over the gradient of the cost function and a moving average over the square gradient of the cost function.

The invention provides an analog calculation circuit for solving a quadratic unconstrained binary optimization (QUBO) problem, and belongs to the technical field of integrated circuits. A weight matrix and a bias vector in a QUBO problem are respectively mapped into a conductance value of a 1T1R array variable resistive memory and a voltage gain coefficient of a voltage gain module, and a feedback signal output by a threshold function module is connected to a grid electrode of a 1T1R array transistor to form a closed-loop feedback structure. The grid voltage is used as a QUBO problem solving state variable result, natural evolution minimization of an energy function is achieved, and solving of the QUBO problem is completed in one step. According to the structure of feeding back the signal to the grid electrode, the isolation of the signal is enhanced, and the power consumption of the circuit is reduced; the circuit is a continuous-time analog calculation circuit, no digital circuit participates in the circuit, the calculation cost is reduced, the calculation time is shortened, and the system power consumption and the chip area are reduced. The circuit has an important value and a wide development prospect in combination optimization application based on a Hopfield network and an Ising model.

A cognitive radio device may include a radio frequency (RF) detector operable over an RF spectrum, an RF transmitter having a selectable hopping frequency decoy window within the RF spectrum, and a controller. The controller may be configured to cooperate with the RF detector and RF transmitter to detect a jammer signal affecting a current hopping frequency decoy window, determine different Quadratic Unconstrained Binary Optimization (QUBO) inputs based upon the detected jammer signal, process the QUBO inputs with a QUBO objective function to determine a new hopping frequency decoy window, and operate the RF decoy transmitter at the new hopping frequency decoy window.