Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

International Conference on Consumer Electronics

Mapping Constrained Slot-Placement Problems to Ising Models and its Evaluations by an Ising Machine

Sho Kanamaru, Kazushi Kawamura, Shu Tanaka, Yoshinori Tomita, Hidetoshi Matsuoka, Kaoru Kawamura, Nozomu Togawai

Published Sep 8, 2019• 11 citations
Publisher

Abstract

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 slot-placement 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 slot-placement problem with additional constraints, called a constrained slot-placement problem, where several item pairs must be placed within a given distance. Since the behavior of Ising machines is stochastic, 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. Using an Ising machine computer, feasible solutions could be obtained up to 50 times faster than the conventional simulated annealing without degrading accuracy for constrained slot-placement problems with 6 × 6 slots and 27 items at the maximum.

DOI
10.1109/icce-berlin47944.2019.8966207

Related papers

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

Sho Kanamaru, Kazushi Kawamura, Shu Tanaka +2 more

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 slot-placement 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 slot-placement problem with additional constraints, called a constrained slot-placement 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.

IEICE Transactions on Information and Systems•Jan 31, 2021•7 citations

Efficient Ising Model Mapping to Solving Slot Placement Problem

Sho Kanamaru, Daisuke Oku, Masashi Tawada +5 more

Ising model-based computation has attracted attention as it can obtain better solutions of various combinatorial optimization problems at high speed by mapping the problems to natural phenomena. A slot placement problem is one of the combinatorial optimization problems which plays an important role in the optimal logic-block placement as well as optimal delivery decisions but it is known as an NP-hard problem. Solving it efficiently is one of the largest challenges. In this paper, we propose an efficient Ising model mapping to solve the slot placement problem and an interpretation method for the obtained Ising solutions to satisfy the slot-placement constraints. First, we propose slot-placement constraint terms that minimize the energy function or Hamiltonian of the Ising model when satisfying the slot-placement constraints. Secondly, we propose an objective function term in the energy function that minimizes the total weighted wiring length between items to be placed to the slots. In Ising model-based computations, the final spin states cannot necessarily satisfy the slot-placement constraints, which gives one of the largest differences from the conventional computations. Hence we newly propose an interpretation method for the obtained Ising solutions to satisfy the slot-placement constraints. On a fully-connected annealing machine, we could obtain feasible solutions with almost the same accuracy as the simulated annealing for slot placement problem with two orders of magnitude faster.

International Conference on Consumer Electronics•Jan 1, 2019•15 citations

Toward Practical Benchmarks of Ising Machines: A Case Study on the Quadratic Knapsack Problem

Kentaro Ohno, Tatsuhiko Shirai, Nozomu Togawa

Combinatorial optimization has wide applications from industry to natural science. Ising machines bring an emerging computing paradigm for efficiently solving a combinatorial optimization problem by searching a ground state of a given Ising model. Current cutting-edge Ising machines achieve fast sampling of near-optimal solutions of the max-cut problem. However, for problems with additional constraint conditions, their advantages have been hardly shown due to difficulties in handling the constraints. In this work, we focus on benchmarks of Ising machines on the quadratic knapsack problem (QKP). To bring out their practical performance, we propose fast two-stage post-processing for Ising machines, which makes handling the constraint easier. Simulation based on simulated annealing shows that the proposed method substantially improves the solving performance of Ising machines and the improvement is robust to a choice of encoding of the constraint condition. Through evaluation using an Ising machine called Amplify Annealing Engine, the proposed method is shown to dramatically improve its solving performance on the QKP. These results are a crucial step toward showing advantages of Ising machines on practical problems involving various constraint conditions.

arXiv:2403.19175•IEEE Access•Jan 1, 2024•9 citations

Visiting-Route Recommendation in Amusement Parks and its Evaluations by an Ising Machine

Yosuke Mukasa, Tomoya Wakaizumi, Shu Tanaka +1 more

In an amusement park, an attraction-visiting route considering the waiting time and traveling time improves visitors' satisfaction and experience. We focus on Ising machines to solve the problem, which are recently expected to solve combinatorial optimization problems at high speed by mapping the problems to Ising models or quadratic unconstrained binary optimization (QUBO) models. We propose a mapping of the visiting-route recommendation problem in amusement parks to a QUBO model for solving it using Ising machines. By using an actual Ising machine, we could obtain feasible solutions 15 times faster with almost the same accuracy as the simulated annealing method for the visiting-route recommendation problem.

International Conference on Consumer Electronics•Jan 10, 2021•3 citations

Mapping Induced Subgraph Isomorphism Problems to Ising Models and Its Evaluations by an Ising Machine

Natsuhito Yoshimura, Masashi Tawada, Shu Tanaka +4 more

Ising machines have attracted attention as they are expected to solve combinatorial optimization problems at high speed with Ising models corresponding to those problems. An induced subgraph isomorphism problem is one of the decision problems, which determines whether a specific graph structure is included in a whole graph or not. The problem can be represented by equality constraints in the words of combinatorial optimization problem. By using the penalty functions corresponding to the equality constraints, we can utilize an Ising machine to the induced subgraph isomorphism problem. The induced subgraph isomorphism problem can be seen in many practical problems, for example, finding out a particular malicious circuit in a device or particular network structure of chemical bonds in a compound. However, due to the limitation of the number of spin variables in the current Ising machines, reducing the number of spin variables is a major concern. Here, we propose an efficient Ising model mapping method to solve the induced subgraph isomorphism problem by Ising machines. Our proposed method theoretically solves the induced subgraph isomorphism problem. Furthermore, the number of spin variables in the Ising model generated by our proposed method is theoretically smaller than that of the conventional method. Experimental results demonstrate that our proposed method can successfully solve the induced subgraph isomorphism problem by using the Ising-model based simulated annealing and a real Ising machine.

IEICE Transactions on Information and Systems•Mar 31, 2021•2 citations

An Ising Machine-Based Solver for Visiting-Route Recommendation Problems in Amusement Parks

Yosuke Mukasa, Tomoya Wakaizumi, Shu Tanaka +1 more

In an amusement park, an attraction-visiting route considering the waiting time and traveling time improves visitors' satisfaction and experience. We focus on Ising machines to solve the problem, which are recently expected to solve combinatorial optimization problems at high speed by mapping the problems to Ising models or quadratic unconstrained binary optimization (QUBO) models. We propose a mapping of the visiting-route recommendation problem in amusement parks to a QUBO model for solving it using Ising machines. By using an actual Ising machine, we could obtain feasible solutions one order of magnitude faster with almost the same accuracy as the simulated annealing method for the visiting-route recommendation problem.

IEICE Transactions on Information and Systems•Sep 30, 2021•10 citations
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 slot-placement 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 slot-placement problem with additional constraints, called a constrained slot-placement problem, where several item pairs must be placed within a given distance. Since the behavior of Ising machines is stochastic, 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. Using an Ising machine computer, feasible solutions could be obtained up to 50 times faster than the conventional simulated annealing without degrading accuracy for constrained slot-placement problems with 6 × 6 slots and 27 items at the maximum.
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 slot-placement 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 slot-placement problem with additional constraints, called a constrained slot-placement 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.
Ising model-based computation has attracted attention as it can obtain better solutions of various combinatorial optimization problems at high speed by mapping the problems to natural phenomena. A slot placement problem is one of the combinatorial optimization problems which plays an important role in the optimal logic-block placement as well as optimal delivery decisions but it is known as an NP-hard problem. Solving it efficiently is one of the largest challenges. In this paper, we propose an efficient Ising model mapping to solve the slot placement problem and an interpretation method for the obtained Ising solutions to satisfy the slot-placement constraints. First, we propose slot-placement constraint terms that minimize the energy function or Hamiltonian of the Ising model when satisfying the slot-placement constraints. Secondly, we propose an objective function term in the energy function that minimizes the total weighted wiring length between items to be placed to the slots. In Ising model-based computations, the final spin states cannot necessarily satisfy the slot-placement constraints, which gives one of the largest differences from the conventional computations. Hence we newly propose an interpretation method for the obtained Ising solutions to satisfy the slot-placement constraints. On a fully-connected annealing machine, we could obtain feasible solutions with almost the same accuracy as the simulated annealing for slot placement problem with two orders of magnitude faster.
Combinatorial optimization has wide applications from industry to natural science. Ising machines bring an emerging computing paradigm for efficiently solving a combinatorial optimization problem by searching a ground state of a given Ising model. Current cutting-edge Ising machines achieve fast sampling of near-optimal solutions of the max-cut problem. However, for problems with additional constraint conditions, their advantages have been hardly shown due to difficulties in handling the constraints. In this work, we focus on benchmarks of Ising machines on the quadratic knapsack problem (QKP). To bring out their practical performance, we propose fast two-stage post-processing for Ising machines, which makes handling the constraint easier. Simulation based on simulated annealing shows that the proposed method substantially improves the solving performance of Ising machines and the improvement is robust to a choice of encoding of the constraint condition. Through evaluation using an Ising machine called Amplify Annealing Engine, the proposed method is shown to dramatically improve its solving performance on the QKP. These results are a crucial step toward showing advantages of Ising machines on practical problems involving various constraint conditions.
In an amusement park, an attraction-visiting route considering the waiting time and traveling time improves visitors' satisfaction and experience. We focus on Ising machines to solve the problem, which are recently expected to solve combinatorial optimization problems at high speed by mapping the problems to Ising models or quadratic unconstrained binary optimization (QUBO) models. We propose a mapping of the visiting-route recommendation problem in amusement parks to a QUBO model for solving it using Ising machines. By using an actual Ising machine, we could obtain feasible solutions 15 times faster with almost the same accuracy as the simulated annealing method for the visiting-route recommendation problem.
Ising machines have attracted attention as they are expected to solve combinatorial optimization problems at high speed with Ising models corresponding to those problems. An induced subgraph isomorphism problem is one of the decision problems, which determines whether a specific graph structure is included in a whole graph or not. The problem can be represented by equality constraints in the words of combinatorial optimization problem. By using the penalty functions corresponding to the equality constraints, we can utilize an Ising machine to the induced subgraph isomorphism problem. The induced subgraph isomorphism problem can be seen in many practical problems, for example, finding out a particular malicious circuit in a device or particular network structure of chemical bonds in a compound. However, due to the limitation of the number of spin variables in the current Ising machines, reducing the number of spin variables is a major concern. Here, we propose an efficient Ising model mapping method to solve the induced subgraph isomorphism problem by Ising machines. Our proposed method theoretically solves the induced subgraph isomorphism problem. Furthermore, the number of spin variables in the Ising model generated by our proposed method is theoretically smaller than that of the conventional method. Experimental results demonstrate that our proposed method can successfully solve the induced subgraph isomorphism problem by using the Ising-model based simulated annealing and a real Ising machine.
In an amusement park, an attraction-visiting route considering the waiting time and traveling time improves visitors' satisfaction and experience. We focus on Ising machines to solve the problem, which are recently expected to solve combinatorial optimization problems at high speed by mapping the problems to Ising models or quadratic unconstrained binary optimization (QUBO) models. We propose a mapping of the visiting-route recommendation problem in amusement parks to a QUBO model for solving it using Ising machines. By using an actual Ising machine, we could obtain feasible solutions one order of magnitude faster with almost the same accuracy as the simulated annealing method for the visiting-route recommendation problem.