Application of Ising Machines and a Software Development for Ising Machines

Tanahashi, Kôtarô; Takayanagi, Shinichi; Motohashi, Tomomitsu; Tanaka, Shu

doi:10.7566/jpsj.88.061010

Cited by 107 publications

(60 citation statements)

References 25 publications

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“…Annealing machines (or Ising machines) have been developed to solve combinatorial optimization problems as non-von Neumann computers. Annealing machines search [16], [19]. The D-Wave quantum annealing (QA) machine [1], [2], [3] has superconducting quantum interference device (SQUID) and works at a low temperature of milli-Kelvin order.…”

Section: Annealing Machinementioning

confidence: 99%

“…However, the Ising model or quadratic unconstrained binary optimization (QUBO) model formulations targeted for practical combinatorial optimization problems have been proposed [15], [16], [17], [18], [19]. Especially, PyQUBO is a software package that supports these formulations [19]. Also, the method to determine the hyperparameters used in the formulations has been proposed [20].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

How to Reduce the Bit-Width of an Ising Model by Adding Auxiliary Spins

Oku

Tawada

Tanaka

et al. 2022

IEEE Trans. Comput.

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Section: Annealing Machinementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

How to Reduce the Bit-Width of an Ising Model by Adding Auxiliary Spins

Oku

Tawada

Tanaka

et al. 2022

IEEE Trans. Comput.

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“…3.1). Previously, various combinatorial optimization problems have been mapped to Ising models and solutions have been evaluated via Ising machines [13]- [15].…”

Section: Ising Machinementioning

confidence: 99%

Solving Constrained Slot Placement Problems Using an Ising Machine and Its Evaluations

Kanamaru

Kawamura

Tanaka

et al. 2021

IEICE Trans. Inf. & Syst.

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Ising machines have attracted attention, which is expected to obtain better solutions of various combinatorial optimization problems at high speed by mapping the problems to natural phenomena. A slotplacement problem is one of the combinatorial optimization problems, regarded as a quadratic assignment problem, which relates to the optimal logic-block placement in a digital circuit as well as optimal delivery planning. Here, we propose a mapping to the Ising model for solving a slotplacement problem with additional constraints, called a constrained slotplacement problem, where several item pairs must be placed within a given distance. Since the behavior of Ising machines is stochastic and we map the problem to the Ising model which uses the penalty method, the obtained solution does not always satisfy the slot-placement constraint, which is different from the conventional methods such as the conventional simulated annealing. To resolve the problem, we propose an interpretation method in which a feasible solution is generated by post-processing procedures. We measured the execution time of an Ising machine and compared the execution time of the simulated annealing in which solutions with almost the same accuracy are obtained. As a result, we found that the Ising machine is faster than the simulated annealing that we implemented.

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“…Algorithm 1 takes the number of colors and h (small constant) and returns the multiplicatives α and β. Algorithm 2 compiles the model based on the Hamiltonian H for the classical solver (PyQUBO (Tanahashi et al 2019)). Algorithm 3 prepares the graph coloring Hamiltonian built from symbolic computing (SymPy) to a quantum approach.…”

Section: Polynomial Reductionmentioning

confidence: 99%