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QUBO formulation of l1-norm for Ising-type computers

Tomohiro Yokota, Makiko Konoshima, Hirotaka Takamura +1 more

IEICE Technical Report; IEICE Tech. Rep.Feb 26, 20200 citations

A formulation of mask optimization into QUBO model for Ising machines

Yukihide Kohira, Haruki Nakayama, Naoki Nonaka +3 more

Recently, Ising machines, which solve Quadratic Unconstrained Binary Optimization (QUBO) problems in a short computation time, have attracted attention. In this paper, we propose a mask optimization method using an Ising machine to obtain a mask with high fidelity to target patterns and high tolerance to process variation. In the proposed method, a mask pattern is improved by repeatedly solving QUBO problems by the Ising machine. In experiments, we applied the proposed method using the Ising machine to various patterns and evaluated it in comparison with existing methods in fidelity to target patterns, tolerance to process variation, and execution time.Recently, Ising machines, which solve Quadratic Unconstrained Binary Optimization (QUBO) problems in a short computation time, have attracted attention. In this paper, we propose a mask optimization method using an Ising machine to obtain a mask with high fidelity to target patterns and high tolerance to process variation. In the proposed method, a mask pattern is improved by repeatedly solving QUBO problems by the Ising machine. In experiments, we applied the proposed method using the Ising machine to various patterns and evaluated it in comparison with existing methods in fidelity to target patterns, tolerance to process variation, and execution time.

SPIE Photomask Technology ConferenceNov 21, 20230 citations

AutoQUBO v2: Towards Efficient and Effective QUBO Formulations for Ising Machines

Justin Pauckert, Mayowa Ayodele, Marcos Diez García +2 more

The QUBO framework provides a way to model, in principle, any combinatorial optimization problem and enables the use of Ising machines to solve it. Ising machines are devices designed to quickly find good solutions to QUBO problems. In previous work, Auto-QUBO was designed to automatically generate QUBO formulations from a high-level problem description. We address two shortcomings of this method. It only works on a per-instance basis, making repeated formulations for similar problems inefficient, and relies on the user to specify penalty weights. This work introduces symbolic sampling, which provides QUBO formulations for entire problem classes. We demonstrate the speedup that can be achieved with this approach using instances of the maximum clique problem. Additionally, we use proven methods to compute valid penalty weights automatically to simplify the translation process. By providing a user-friendly way to generate QUBO formulations in an efficient manner, both in terms of time and problem difficulty, we enable more people to use Ising machines for combinatorial optimization.The QUBO framework provides a way to model, in principle, any combinatorial optimization problem and enables the use of Ising machines to solve it. Ising machines are devices designed to quickly find good solutions to QUBO problems. In previous work, Auto-QUBO was designed to automatically generate QUBO formulations from a high-level problem description. We address two shortcomings of this method. It only works on a per-instance basis, making repeated formulations for similar problems inefficient, and relies on the user to specify penalty weights. This work introduces symbolic sampling, which provides QUBO formulations for entire problem classes. We demonstrate the speedup that can be achieved with this approach using instances of the maximum clique problem. Additionally, we use proven methods to compute valid penalty weights automatically to simplify the translation process. By providing a user-friendly way to generate QUBO formulations in an efficient manner, both in terms of time and problem difficulty, we enable more people to use Ising machines for combinatorial optimization.

Genetic and Evolutionary Computation Conference Companion (GECCO Companion)Jul 15, 202314 citations

Probabilistic algorithms for Ising machines from optimization to generative AI

Corentin Delacour, Xiuqi Zhang, Abdelrahman S. Abdelrahman +4 more

arXiv:2610.03972Nature Reviews Electrical EngineeringOct 15, 20260 citations

Combinatorial optimization with coherent Ising machine

Takahiro Inagaki

The Japan Society of Applied PhysicsJul 10, 20180 citations

Compressed sensing based on QUBO formulation

Kazue Kudo

Abstract Ising machines efficiently solve the combinatorial optimization problems described by the Ising model or the quadratic unconstrained binary optimization (QUBO) formulation. A hybrid method based on the QUBO formulation for compressed sensing is proposed. The proposed method comprises alternative steps of discrete and continuous optimization. In the discrete optimization step, the objective function is described by the QUBO formulation. Successful examples obtained via the proposed method are demonstrated. The performance of the proposed method depends highly on the initial conditions.Abstract Ising machines efficiently solve the combinatorial optimization problems described by the Ising model or the quadratic unconstrained binary optimization (QUBO) formulation. A hybrid method based on the QUBO formulation for compressed sensing is proposed. The proposed method comprises alternative steps of discrete and continuous optimization. In the discrete optimization step, the objective function is described by the QUBO formulation. Successful examples obtained via the proposed method are demonstrated. The performance of the proposed method depends highly on the initial conditions.

Journal of Physics Conference SeriesMar 1, 20221 citation

A formulation of mask optimization into QUBO model for Ising machines

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