Fujitsu Corporation

University of Toronto

Fujitsu Limied

Hirotaka Tamura

Junji Ogawa

Kohtaroh Gotoh

Hisakatsu Yamaguchi

Kouichi Kanda

Masaya Kibune

Shigetoshi Wakayama

Satoshi Matsubara

Sanroku Tsukamoto

Tsz-Shing Cheung

Yoichi Koyanagi

Yoshiyasu Doi

Ali Sheikholeslami

Hisakatsu Araki

Takayuki Shibasaki

Miyoshi Saito

Yasumoto Tomita

Hideki Takauchi

Richard L Schober, Jr

Weixin Gai

Raghu Sastry

Takaya Chiba

Yuji Takahashi

Nikola Nedovic

Yasuo Hidaka

William Warren Walker

Jason Helge Anderson

Yasuhiro Watanabe

Hiroaki Fujimoto

Kosuke Suzuki

Joshua Liang

Safeen Huda

Akira Yoshida

Makiko Konoshima

Masaki Aoki

Shigeto Suzuki

Takashi Eshita

Hisanori Fujisawa

Takumi Danjo

Aki Dote

Nestor Tzartzanis

Yuuki Ogata

Hideki Ishida

Hidetoshi Matsumura

David Thach

Jumpei Koyama

Yanfei Chen

Daisuke Kushibe

Takushi Hashida

Fang Yin

Kohtaro Gotoh

Tadahisa Matsumoto

Takuya Saze

Mohammad Bagherbeik

Wahid Rahman

Mehrdad Malekmohammadi

Keivan Dabiri

A data processing device including: a memory storing a plurality of values at a first time point, which include: values of state variables at the first time point and included in an evaluation function of a combinatorial optimization problem with a constraint condition; values of first local fields at the first time point and used to specify a change amount of values of the evaluation function by a change in the values of the state variables; and values of second local fields at the first time point and used to specify an amount to be added to the values of the evaluation function when the constraint condition is not satisfied; and a control circuit configured to return the plurality of state variables, the first local fields, and the second local fields to the values at the first time point when the determination circuit has determined not to accept the change.

Data processing device, computer-readable recording medium storing data processing program, and data processing method

A method includes: accessing first storage configured to store a first weight coefficient group which is at least some of a plurality of weight coefficients indicating a magnitude of interaction between a plurality of state variables in an evaluation function representing energy of an Ising model; accessing a plurality of second storages each of the plurality of second storage being configured to store a second weight coefficient group related to a state variable having a value of 1 in any of a plurality of state variable groups respectively including the plurality of state variables among the plurality of weight coefficients; outputting, for each of the plurality of state variable groups, a search result obtained by performing searching processing configured to perform processing of searching for an optimum solution by repeatedly performing a first update process with a first constraint or a second update process with a second constraint.

Optimization apparatus and optimization method

According to an aspect of an embodiment, operations may include obtaining a first fixed temperature and a second fixed temperature of a replica exchange Markov Chain Monte Carlo (MCMC) process used to solve an optimization problem associated with a system, and obtaining a plurality of replicas of the system. The operations may also include obtaining a target swap acceptance probability with respect to swapping, during the replica exchange MCMC process, between replicas that correspond to adjacently ordered temperatures of a set of temperatures between the first fixed temperature and the second fixed temperature. The operations may include determining a respective average swap acceptance probability with respect to one or more respective adjacent pairs of temperatures. Further, the operations may include adjusting one or more of the variable temperatures based on a relationship between the target swap acceptance probability and each of one or more of the respective swap acceptance probabilities.

Automatic adjustment of replica exchange

An optimization method executed by a computer upon attempting to solve a ground state of an Ising model by simulating a state change of the Ising model when a magnetic field applied to the Ising model is reduced, the Ising model representing a problem to be solved, the method including: executing a first process, the first process being a real time propagation in which an intensity of the magnetic field is reduced with progress of time in simulation; and in response to the progress of time in the real time propagation of the first process, executing a second process, the second process including reducing energy of the Ising model based on an imaginary time propagation method.

Optimization apparatus, optimization method, and non-transitory computer-readable storage medium for storing optimization program

An optimization apparatus calculates a first portion, among energy change caused by change in value of a neuron of a neuron group, caused by influence of another neuron of the neuron group, determines whether to allow updating the value, based on a sum of the first and second portions of the energy change, and repeats a process of updating or maintaining the value according to the determination. An arithmetic processing apparatus calculates the second portion caused by influence of a neuron not belonging to the neuron group and an initial value of the sum. A control apparatus transmits data for calculating the second portion and the initial value to the arithmetic processing apparatus, and the initial value and data for calculating the first portion to the optimization apparatus, and receives the initial value from the arithmetic processing apparatus, and a value of the neuron group from the optimization apparatus.

Optimization system, optimization apparatus, and optimization system control method for solving optimization problems by a stochastic search

A calculation circuit calculates, among a plurality of neurons whose states are represented by variables each having m values (m is a positive integer of 3 or greater), two energy changes caused by a state change of a second neuron by 2(n is an integer of 0 or greater) in positive and negative directions, based on a state change direction of a first neuron whose state has been updated and a weighting coefficient indicating magnitude of an interaction between the first and second neurons. A state transition determination circuit determines, based on magnitude relationships among a thermal excitation energy and the two energy changes, whether to allow updates of the state of the second neuron that cause the two energy change, outputs the determination results, and limits updates by which the state of the second neuron exceeds an upper limit value or falls below a lower limit value.

Optimization apparatus and optimization apparatus control method

According to an aspect of an embodiment, operations may include obtaining a first matrix associated with an optimization problem associated with a system and obtaining a second matrix associated with the optimization problem. The operations may include obtaining a local field matrix that indicates interactions between the variables of the system as influenced by their respective weights. The operations may include updating the local field matrix. Updating the local field matrix may include performing arithmetic operations with respect to a first portion of the first matrix and a second portion of the second matrix that correspond to a third portion of the local field matrix that corresponds to the one or more variables. The operations may include updating an energy value of the system based on the updated local field matrix and determining a solution to the optimization problem based on the energy value.

System local field matrix updates

A transition control unit detects, when stochastically determining based on a temperature, energy changes, and a random number whether to accept any of a plurality of state transitions according to a relative relationship between the energy changes and thermal excitation energy, a minimum value among the energy changes. The transition control unit then subtracts, when the minimum value is positive, an offset obtained by multiplying the minimum value by a value M that is greater than 0 and less than or equal to 1 from each of the energy changes corresponding to the plurality of state transitions.

Optimization apparatus and control method thereof

An optimization method includes holding combining destination information indicating a combining destination neuron to be combined with a target neuron which is one of a plurality of neurons corresponding to a plurality of spins of an Ising model obtained by converting an optimization problem, the target neuron being different in a plurality of neuron circuits; holding a weighting coefficient indicating a strength of combining between the target neuron and the combining destination neuron, and outputting the weighting coefficient corresponding to the combining destination information; permitting an update of a value of the target neuron by using the weighting coefficient output and the value of the update target neuron, and outputting a determination result indicating whether or not the value of the target neuron is permitted to be updated; and determining the update target neuron based on the plurality of determination results respectively output and outputting the update target information.

In an optimization apparatus, calculation circuits individually calculate a change in energy based on first local field values each associated with one of n bits (n is an integer greater than or equal to 2) amongst a plurality of bits corresponding to spins in an Ising model, values of the n bits, and one or more weight values representing strength of interaction among the n bits. The change in energy is caused by flips of the n bits. An update bit selection circuit selects the n bits for which value update is accepted, based on the magnitude relationship between thermal excitation energy and each of the calculated changes in energy. An update circuit flips the n bits, and also updates, based on the flips of the n bits, second local field values including the first local field values and each associated with the plurality of individual bits.

Optimization apparatus and method for controlling thereof

An optimization device includes: k first calculation circuits, N&#x2212;k second calculation circuits, a selection circuit, an identification information calculation circuit and an update circuit. The first calculation circuit calculates a first energy change of an Ising model due to a change of a value of one of k first bits having values of 1 and a change of a value of a second bit having a value of 0 selected based on a generated first random number. The second calculation circuit calculates a second energy change of the Ising model due to a change of a value of one of (N&#x2212;k) third bits having the values of 0 and a change of a value of a fourth bit having a value of 1 selected based on a generated second random number.

Optimization device and control method of optimization device

Storage devices each hold corresponding one of n weight coefficient groups obtained by dividing weight coefficients such that each group includes weight coefficients about at least two bits. Bit value calculation circuits each output a result (flag information) by determining whether to accept updating about each of the bits based on the weight coefficient group, a value of an updated bit, identification information, and thermal excitation energy and an updated value of an accepted bit whose uprate has been accepted. First selection circuits each select an accepted bit based on the flag information and output a state signal including the flag information, the updated value, and identification information associated with the accepted bit. A second selection circuit determines the updated bit based on the flag information in the state signal and supplies the value of the updated bit and the identification information to each of optimization apparatuses.

Information processing system, information processing apparatus, and information processing method

An information processing device includes: a memory configured to hold values of state variables included in an evaluation function presenting energy and a weight value for each set of the state variables; and a processor coupled to the memory and configured to: calculate an energy change value when each of the values of the state variables is set as a next change candidate based on the values of the state variables and the weight value; calculate a total energy change value by adding a penalty value according to an excess amount violating an inequality constraint, to each of the energy change values calculated for the state variables, the excess amount being calculated based on a coupling coefficient and a threshold value; and change any value of the state variables in the memory based on a set temperature value, a random number value, and the total energy change values.

Semiconductor integrated circuit

An information processing device includes: a memory; and a processor coupled to the memory and configured to calculate, for a plurality of bits corresponding to a plurality of spins included in an Ising model obtained by converting a problem to be calculated, in a case where the plurality of bits is divided into a plurality of groups, on the basis of a first local field value for a first bit having a value of 1 and a second local field value for a second bit having a value of 0 among a plurality of bits included in each of the plurality of groups, a first energy change of the Ising model due to a change of the value of the first bit from 1 to 0 and a change of the value of the second bit from 0 to 1.

Optimization device and method for controlling optimization device

An information processing apparatus includes: a memory; and a processor coupled to the memory and configured to: hold values of a plurality of state variables included in an evaluation function representing energy, and outputs, every certain number of trials, the values of the plurality of state variables; compute, when a state transition occurs in response to changing of one of the values of the plurality of state variables, an energy change value for each state transition based on a weight value selected based on an update index value; and determine a first offset value based on a plurality of the energy change values such that at least one of the state transitions is allowed, outputs a plurality of first evaluation values obtained by adding the first offset value to the plurality of energy change values, and outputs, every certain number of trials, the first offset value.

Information processing apparatus and method for controlling sampling apparatus

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Yokohama, Japan

Hirotaka Tamura