36 results about "Ising model" patented technology

A calculation device of the present disclosure is characterized by implementing an item, which expresses a magnetic field, at each site by adding, to the conventional coherent Ising machine, a function for simulating the magnetic field item. Specifically, light having the amplitude of a predetermined sign is input in the conventional coherent Ising machine by dislocating an operation point of the push-pull-type optical modulator. When the operation point is dislocated to a + direction, DOPO (Degenerate Optical Parametric Oscillator) having the same wavelength as that of DOPO of an optical resonator and having a fixed initial phase difference from pump light into which the light is injected considerably tends to oscillate at 0 phase, and becomes reversed when the operation point is dislocated to a minus direction.

A method implements seismic inversion using quantum computing. The method includes encoding a first objective function and trace values as a first Ising model Hamiltonian, annealing the first Ising model Hamiltonian to obtain a first result, and decoding the first result to output reflectivity values. The method further includes encoding a second objective function and the reflectivity values as a second Ising model Hamiltonian, annealing the second Ising model Hamiltonian to obtain a second result, and decoding the second result to output acoustic impedance values.

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.

The present invention discloses a complex system fault location method based on the Ising model, comprising: corresponding each sensor in the complex system to a node in the Ising model, using spin variables to represent the sensor state; determining the interaction parameters in the Ising model based on the correlation between different sensors and the changing trend of sensor readings; defining an energy function, which includes interaction terms, external field terms, and terms related to sensor readings; defining a loss function; randomly initializing parameters; minimizing the loss function using an optimization algorithm and updating the parameters; iteratively updating the node state using the Monte Carlo method, updating the node spin state with a preset probability based on the current energy function and temperature parameters; and determining the sensor fault state based on the final node spin state. The present invention can achieve rapid fault location in complex systems.

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.

The invention discloses an Isin annealing SAT solver based on sparse sensing and hierarchical topology driving, relates to an Isin model technology, and provides the scheme for solving the problems of redundancy and the like in the prior art. The method comprises the following steps: firstly, mapping an SAT problem to an Isinus model by using a mixed topological graph comprising a sparse connection graph and a lattice graph, then solving by using a simulated annealing algorithm of a layered topological structure, and after the problem is successfully solved, reflecting a spin reflection to a variable assignment of an SAT problem expression; a sparse perceptual coding mechanism is adopted to represent a model coefficient; the sparse perception coding mechanism is composed of a sparse format storage mechanism and a weight perception mechanism. The spinning type comprises original spinning and auxiliary spinning; the original spins are emitted to a reconfigurable sparse connection graph, and the auxiliary spins are mapped to a lattice graph. The method has the advantages of good mapping effect, less resource occupation and high solving efficiency.

This information processing device comprises a control unit that executes i-th spin calculation processing, which is processing for calculating the energy of a spin Si (i is an integer equal to or greater than 0 and less than I) in a model to be analyzed, which is a classical spin Ising model comprising I spins. In the i-th spin calculation processing, the energy of an interaction between the spin Si and each spin of an i-th calculation connection number is calculated, wherein the i-th calculation connection number is an integer multiple of a predetermined value N (N is an integer equal to or greater than 2) and is a minimum number equal to or greater than the number of i-th actual spins that are spins having a non-zero interaction with the spin Si in the model to be analyzed.

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 provides a de novo drug design method and device based on a light quantum computer. The method comprises the following steps: acquiring a ligand receptor complex structure needing to be optimized; extracting a ligand molecule and an acceptor structure from the ligand-acceptor complex structure, marking an optimizable part in the ligand molecule, and presetting a fragment library for replacement; determining an attribute needing to be optimized according to the marked optimizable part; according to the to-be-optimized attribute and the fragment library, calculating to obtain a corresponding monomial coefficient and a corresponding quadratic coefficient, and according to the calculated monomial coefficient and the calculated quadratic coefficient, constructing a corresponding mathematical model; and converting the constructed mathematical model into a corresponding Isin model, inputting the Isin model into a light quantum computer for solving, and calculating to obtain an optimized structure and ligand information. According to the invention, rapid and accurate de novo drug design can be realized.

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.

The invention provides a beam forming method and device, equipment, a medium and a product, and the method comprises the steps: obtaining target information, obtaining an Isin model needed by a quantum approximation optimization algorithm, and enabling the ground state of Hamiltonian in the Isin model to be the optimal solution of a total cost function; a variation parameter is initialized; sending Hamiltonian and variational parameters in the Isin model to a quantum processing unit; and the processing unit executes an iteration step: receiving the bit string of the final quantum state determined by the quantum processing unit to obtain a new variational parameter, and sending the new variational parameter to the quantum processing unit again to execute the iteration step until the new variational parameter converges or the iteration step reaches the maximum number of iterations, thereby obtaining a user set of a final selection service. And a final beam forming matrix is determined. According to the method, the combination optimization part is solved through quantum calculation, high-precision numerical calculation is completed through classical calculation, and therefore a solution with global optimality and numerical accuracy is output.

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.

A code conversion method includes a conversion step of converting a first calculation model applicable to an Ising model or a QUBO in which options are represented by a first representation method into a second calculation model applicable to an Ising model or a QUBO in which options are represented by a second representation method, in which the conversion step includes solving a simultaneous equation in which each component of an energy calculated on the basis of the first calculation model matches each component of an energy calculated on the basis of the second calculation model, and obtaining a submatrix of the Hamiltonian used in computation of the second calculation model.

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.

The invention relates to the technical field of intelligent manufacturing, in particular to a plasticizing order matching method, which comprises the following steps of: encoding an order and a production line data event, and generating a form vector and an energy vector through a neural network; constructing a coupling matrix in the hyperbolic space and performing primary matching by using an Isin model; if the output gradient exceeds the threshold, refining is carried out through neuromorphic suppression, and a local entropy gradient is calculated; feeding a refining result and production line feedback into a causal generation graph to judge conflicts, and modulating a solver to generate deviation correction matching if necessary; and finally, the matching vector and the conflict mark are written into the block chain, and each node automatically updates the model through federal learning and drives curvature and threshold value self-adaption through morphological entropy. According to the method, real-time production scheduling with low energy consumption, less line change and sustainable learning is realized.

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.

Searching for a ground state of an Ising model is performed so that a combinatorial optimization problem is efficiently solved. An information processing apparatus stores an energy function setting an interaction between an i-th spin of a first spin group and a j-th spin of a second spin group so that the i-th spin of the first spin group and the j-th spin of the second spin group have the same value in a ground state in an interaction relationship of an Ising model represented as a complete bipartite graph connecting N spins of the first spin group and of the second spin group, and searches for the ground state of the Ising model based on the energy function and information unique to the spins. The search for the ground state of the Ising model is performed by applying an algorithm of simulated annealing method to the above-mentioned energy function.

The invention discloses a method for updating process objects in an engineering design system, and relates to the technical field of engineering design systems.The method comprises the steps that a to-be-updated process object set and a dependency graph are obtained; mapping the to-be-updated process object set and the dependency graph into Hamiltonian of an Isin model, solving a voltage pulse signal corresponding to the lowest energy state through a quantum tunneling effect, and generating a deadlock-free updating sequence; the non-deadlock update sequence is converted into a biomarker instruction, interference fringes are generated through excitation of a micro-fluidic chip, and a time-varying optical signal is generated; an optical sensor array sensing area is divided based on interference fringes, time-varying optical signals are sensed through optical sensors, abnormal events are detected, fault points are identified, and fault coordinates and fault types are recorded; according to the method, the to-be-updated process object set and the dependency graph are mapped into the Hamiltonian quantity of the Isin model, so that the problem of updating sequence failure caused by static modeling is solved.

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 invention provides an EDA layout optimization method and device, equipment and a storage medium, and belongs to the technical field of electronic design automation. The method comprises the following steps: describing a topological relation of a plurality of devices by adopting a sequence pair representation method, mapping a layout problem of the devices to an Isin model through three-dimensional spinning, and constructing a coupling coefficient and external field intensity of a total Hamiltonian; solving the Isin model according to the coupling coefficient and the external field intensity to determine an initial spin configuration; according to the initial spin configuration, identifying an overload device and an overload position, and according to priority ranking of the device, carrying out iteration processing of spin overturning to repair configuration conflicts, and obtaining a legal spin configuration; determining an intermediate layout according to the legal spin configuration; and performing post-processing on the intermediate layout through a force-oriented optimization algorithm to obtain an optimized layout. According to the method, the EDA layout quality and layout reliability can be improved while the design period is shortened.

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.