23 results about "Ising model" patented technology

The quantum Monte Carlo calculation method includes: a step of preparing a predetermined number of spin arrangements for an Isin model; setting the initial state of the minimum energy state and the initial value of the minimum energy related to the Isinus model; and a Monte Carlo calculation step of repeatedly executing a Monte Carlo calculation process for searching for the minimum energy state of the Isinus model until an end condition is satisfied, the Monte Carlo calculation step including: for each of a predetermined number of spin arrangements, calculating the minimum energy state of the Isinus model; generating a new state candidate in which the spin state of the spin array to be processed changes; and for each of the predetermined number of spin arrangements, performing update processing of the state of the spin arrangement to be processed and the state of the minimum energy on the basis of the energy related to the state candidate, the energy related to the spin arrangement to be processed, and the minimum energy.

A combination of fastening points is obtained by repeating following steps as an optimum value when an evaluation method of a physical quantity acting on a substrate is specified. The steps include: obtaining the physical quantity according to the combination of fastening points; generating a regression model expressing one fastening point candidate by a binary variable; converting the regression model into an Ising model based on the evaluation method; and determining the combination of fastening points by selecting one of fastening points from the fastening point candidate positions using an Ising machine so as to minimize an evaluation value of the Ising model.

An aspect of the invention includes an optimization unit configured to perform optimization, by stochastic calculation, on a Hamiltonian of a quantum annealing model, which is an Ising model representing the optimization target in a state in which a transverse magnetic field is applied, in which the optimization unit performs the optimization by executing a series of formulas satisfying a state update condition, which is a condition related to an update of a spin state, until a predetermined end condition is satisfied, and the state update condition includes a first condition representing the spin state, a second condition representing a relationship between the spin states obtained at different times, a third condition representing a relationship between adjacent trotter layers, and a fourth condition including an amount representing an intensity of an interaction between the same or different spins existing in the same trotter layer, an amount representing a magnitude of energy of a spin itself, and an amount representing an intensity of an interaction between spins belonging to different trotter layers.

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.

A computer-readable recording medium stores therein an information processing program for causing a computer to execute a process, the process includes: obtaining a decomposition number of a Trotter decomposition approximating a quantum unitary corresponding to an Ising model; and setting a quantum circuit expressing a formula of the Trotter decomposition by one or more first partial circuits and a second partial circuit, the one or more first partial circuits being of a first count one less than the obtained decomposition number and each including an Rz gate that represents a rotation action about a Z-axis on a qubit, the second partial circuit being free of the Rz gate and coupled to a rear of the one or more first partial circuits.

A non-transitory computer-readable storage medium storing a solution search program that causes at least one computer to execute a process, the process includes reducing, when searching for a ground state of the Ising model that represents a problem by obtaining a state change of the Ising model, a strength of a magnetic field applied to the Ising model according to a first annealing schedule from an initial state of the Ising model; and reducing, after a quantum phase transition occurs in the Ising model, the strength of the magnetic field according to a second annealing schedule.

The application discloses a kind of sparse perception, hierarchical topology driven Ising annealing SAT solver, it is related to Ising model technology, the present application is proposed to solve the problem in prior art, such as redundancy phenomenon.First, SAT problem is mapped to Ising model using mixed topology graph including sparse connection graph and lattice graph, then using simulated annealing algorithm of hierarchical topology structure is solved, after problem solving successfully, spin is reflected to the variable assignment of SAT problem expression;Sparse perception coding mechanism is used to represent model coefficient;The sparse perception coding mechanism is composed of sparse format storage mechanism and weight perception mechanism.Spin type includes original spin and auxiliary spin;The original spin is shot on reconfigurable sparse connection graph, and the auxiliary spin is mapped to lattice graph.The advantage is that mapping effect is good, resource occupation is less, and solving efficiency is high.

An original problem, which may be represented as a string, for execution in a quantum computing system is classically compressed using classical computing systems. Unique variable names and unique coefficients stored in tables constructed from the original problem are associated with unique identifiers. The problem is then translated to a translated form, such as a translated string, using the tables. The translated string may also be generated after quantizing values in the at least one of the tables. The translated string, whether or not quantized, may be entropy encoded. The final result is a compressed version or string of the original string that can be transmitted and stored in a compressed form. The compressed problem may also be decompressed and submitted for execution in a quantum computing system.

To easily solve a combination optimization problem.SOLUTION: The information processing apparatus 100 calculates a first solution 101 of the combinatorial optimization problem based on the Ising model 110. The information processing apparatus 100 determines a value of the parameter 120 of the first quantum circuit 111 such that a probability that a quantum state of the first quantum circuit 111 becomes the calculated first solution 101 is maximized. At this time, the information processing device 100 determines the value of the parameter 120 by, for example, repeating processing of updating the value of the parameter 120 of the first quantum circuit 111 by QPU using the second quantum circuit 112 by QAE. The information processing apparatus 100 calculates the second solution 102 of the combinatorial optimization problem based on the first quantum circuit 111 in which the determined value of the parameter 120 is set.SELECTED DRAWING: Figure 1

This invention relates to a method for generating conditional data and a hybrid computer system. In this hybrid computer system, a digital computer transforms the sampling problem of the variables to be sampled in a conditional data generation model of an energy network type, which can be mapped to a quantum computer, into a problem of solving the Ising model. The quantum computer then solves the Ising model to achieve rapid sampling. This invention solves the technical problem of low sampling efficiency in existing deep learning-based conditional data generation models, thereby improving the sampling efficiency of conditional data generation models.

The invention discloses a ferromagnetic material Isin model parameter inversion method and system based on hysteresis characteristics, and the method comprises the steps: obtaining an initial magnetization curve and a saturation hysteresis loop of a ferromagnetic material, and determining an experiment excitation magnetic field sequence; constructing a three-dimensional Isin model; inputting an excitation magnetic field sequence, calculating and generating a simulated initial magnetization curve and a saturated hysteresis loop by using a three-dimensional Isin model, and constructing a training data set containing a large number of sample pairs; constructing a parameter inversion neural network model, and performing supervised learning training on the neural network model by using the training data set; and inputting the experimental hysteresis characteristics into the trained parameter inversion neural network model, and outputting an optimal model parameter combination matched with the experimental hysteresis characteristics of the ferromagnetic material. According to the method, physical constraint is carried out on the Isin model by utilizing the hysteresis characteristic of a macroscopic experiment, inversion of relevant parameters of a microscopic magnetic domain structure in the model is realized, and a modeling basis is provided for further carrying out magnetic Barkhausen noise signal simulation and magnetic characteristic prediction.

The objective is to achieve at least one of the following: higher accuracy or higher speed compared to conventional calculations using the Ising model. [Solution] The processing system comprises a plurality of array modules, each having a spin block that stores a spin value for each spin in a plurality of spins, an interaction block that stores partial interaction values ​​obtained by dividing the interaction value between each spin in the plurality of spins, and a calculation block that outputs a calculation result calculated based on the spin value and the partial interaction value; and a control module that controls the state of the spins in the plurality of spins based on an integrated result obtained by integrating the calculation results output from the plurality of array modules.

A method for solving continuous-variable quantum quadratic optimization problems is disclosed. Entangled qubits encoded with an Ising model problem are coupled to a heat reservoir. The Ising model problem expresses a continuous-variable quantum quadratic optimization problem. The entangled qubits are evolved by thermal annealing, resulting in optimized solutions to the continuous-variable quantum quadratic optimization problem. A temperature of the heat reservoir is varied on a schedule such that each evolution of the entangled qubits occurs while the heat reservoir is at a desired temperature.

The present application relates to an apparatus and a method for optimization. An optimization apparatus solves the ground state of an Ising model by running a simulation of state changes of the Ising model that occur when a magnetic field applied to the Ising model representing a target problem is decreased. In doing so, the optimization apparatus adds values corresponding to noise to some coefficients used in the simulation. Then, the optimization apparatus performs a first process of real-time propagation that decreases the strength of the magnetic field with the time progression in the simulation and a second process that decreases the energy of the Ising model based on an imaginary-time propagation method.

An information processing device for assisting creation of an Ising model, includes an input reception unit for receiving an input of an objective function that formulates characteristics of a composite material according to a formulation and a condition of constraint on the formulation, and a data creation unit for creating the Ising model for causing an annealing type optimization machine to solve an optimal solution of the formulation that satisfies a constraint condition expression and optimizes the characteristics, wherein the condition of constraint on the formulation includes a condition related to a mix number of substances included in a substance group and to be mixed in the composite material, for each substance group composing the composite material, and a condition related to an amount of substance to be mixed in the composite material, for each substance included in the substance group, and the constraint condition expression is formulated so as not to be calculated as the optimal solution of the formulation in a case where at least one of the condition regarding the mix number and the condition related to the amount of substance is not satisfied.

The application discloses an optical Ising machine based on Cholesky decomposition. The application encodes the interaction matrix of the Ising model into an optical modulation system after Cholesky decomposition, inputs an optical signal carrying the spin group state information of the Ising model, and outputs an optical intensity signal encoded with the Hamiltonian information of the Ising model, so that the high-speed calculation of an arbitrary Ising model Hamiltonian is realized. The application has the advantages of large scale, wide application range, high speed and the like.

This calculation model is a calculation model that is appliable to an Ising model or QUBO, in which a plurality of choices in a combinatorial optimization problem are assigned to any of possible values of one or more binary variables, and one of the binary variables is fixed on the basis of constraints imposed on the combinatorial optimization problem.

An information processing device for assisting in the creation of an Ising model for causing an annealing-type optimization machine to solve the problem of searching for an optimal solution for a composite material having a compounding constraint condition, the information processing device characterized by comprising: an input reception unit that receives an input of the compounding constraint condition and an objective function in which the characteristics of the composite material have been formulated according to the compounding; and a data creation unit that creates an Ising model that causes the annealing-type optimization machine to solve the optimal solution for a compounding that satisfies a constraint conditional expression in which the compounding constraint condition is formulated and in which the characteristics are optimized, wherein with regard to substances to be mixed into the composite material, an amount ri of a substance i is represented by bits, and whether the substance i is included in the composite material is represented by a bit, the constraint conditional expression includes a condition satisfying expressions (1) and (2), and the constraint conditional expression is formulated so as not to be calculated as an optimal solution of the compounding if at least one of expressions (1) and (2) is not satisfied.

The application discloses an optical Ising model calculation method and system based on wavelength division multiplexing. The application can load any Ising model, greatly increases the adaptability of the system, has the advantages of wide application range, fast information transmission, simple structure, low cost, fast calculation and the like. The application takes a coherent multi-wavelength light source as a signal input, splits the light field by using a light splitting device, then encodes the spin, interaction and magnetic field parameters on the light signals of different wavelengths by using an optical modulator, performs optical Fourier transform by using a lens system, makes the light fields of the same wavelength interfere, and combines the light fields of different wavelengths, finally measures the total light intensity after combination by using a detector, so that the Hamiltonian of the Ising model of optical calculation of any interaction is realized. The method provided by the application has important application prospects in solving optimization problems and Ising model phase transition research and the like, and is convenient to integrate in optical chips and the like specific applications.

The invention relates to a simulated annealing acceleration method and related equipment, and the method comprises the steps: receiving simulated annealing parameters needed for executing an annealing task for a plurality of annealing examples of an Isin model problem, and storing the vector information of main solution vectors of the plurality of annealing examples in a first storage module, the vector information comprises an M-bit spinning state value and record information; in the process of executing the annealing task on the plurality of annealing examples based on the simulated annealing parameters, reading and adjusting the vector information in the first storage module; and after the annealing tasks of the plurality of annealing instances are executed, reading vector information corresponding to the plurality of annealing instances from the first storage module, reconstructing candidate solution vectors based on the M-bit state values and the recorded information, and then taking the candidate solution vector with the lowest energy value as a global optimal solution. The problem that long and uncertain memory access delay is caused by frequent and high-bandwidth global solution vector synchronization operation of a traditional simulated annealing acceleration scheme is solved.

The invention relates to the technical field of data processing, in particular to an optimization algorithm based on mixing of a binary particle swarm optimization algorithm and a simulated annealing algorithm, which is characterized by comprising the following steps of: combining binary particle swarm optimization (BPSO) and simulated annealing (SA) mechanisms to realize the balance of global exploration and local development through a staged updating strategy; the method is suitable for the Ising Model and quadratic unconstrained binary optimization (QUBO) problem. According to the method, BPSO and SA are combined, and global search and local random hopping capabilities are achieved; providing a unified optimization framework for the Isin model and the QUBO problem; the annealing temperature and the PSO inertia weight are dynamically adjusted, and self-adaptive search is achieved; and parallel realization is supported, and the method can be expanded to a hardware acceleration platform (such as an FPGA Isin machine).

The present application relates to the technical field of intelligent manufacturing, and more particularly to a plasticizing order matching method, which comprises the following steps: first, encoding the order and production line data events, generating a shape vector and an energy flow vector through a neural network; constructing a coupling matrix in a hyperbolic space and matching once using an Ising model; if the output gradient is over the threshold, refining through neural morphology inhibition and calculating the local entropy gradient; feeding the refined results and production line feedback into a causal generation graph to determine conflicts, and if necessary, modulating a solver to generate a correction matching; finally, writing the matching vector and conflict markers into a blockchain, and each node updates the model through federated learning, and drives the curvature and threshold adaptively through the shape entropy. The method realizes real-time production scheduling with low energy consumption, less line change and sustainable learning.