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Efficient correlation-based discretization of continuous variables for annealing machines

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Yuki Furue, Makiko Konoshima, Hirotaka Tamura, Jun Ohkubo

Quantum Physics · Submitted 18 Jan 2023

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim We numerically show that the proposed method reduces the number of necessary binary variables in the QUBO formulation without a significant loss in prediction accuracy.

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arXiv ID2301.07244 (version 1)
Pith IDDO255KQZ, Pith's permanent record of this paper and its review
CitationHirotaka Tamura, Jun Ohkubo, Makiko Konoshima, Yuki Furue. "Efficient correlation-based discretization of continuous variables for annealing machines." arXiv:2301.07244 (2023). https://pith.science/paper/DO255KQZ
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What the abstract itself states, as probabilities: claims first 0.05 · states limitation 0.22 · human participants 0.03 · controlled comparison 0.39 · randomized assignment 0.01 · states quantitative result 0.08 · compares with prior results 0.85 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

Annealing machines specialized for combinatorial optimization problems have been developed, and some companies offer services to use those machines. Such specialized machines can only handle binary variables, and their input format is the quadratic unconstrained binary optimization (QUBO) formulation. Therefore, discretization is necessary to solve problems with continuous variables. However, there is a severe constraint on the number of binary variables with such machines. Although the simple binary expansion in the previous research requires many binary variables, we need to reduce the number of such variables in the QUBO formulation due to the constraint. We propose a discretization method that involves using correlations of continuous variables. We numerically show that the proposed method reduces the number of necessary binary variables in the QUBO formulation without a significant loss in prediction accuracy.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Annealing machines specialized for combinatorial optimization problems have been developed, and some companies offer services to use those machines. background 1.00 0.04 0.02 asserted 1.00
s1 Such specialized machines can only handle binary variables, and their input format is the quadratic unconstrained binary optimization (QUBO) formulation. background 0.99 0.05 0.02 asserted 1.00
s2 Therefore, discretization is necessary to solve problems with continuous variables. background 0.97 0.05 0.03 asserted 1.00
s3 However, there is a severe constraint on the number of binary variables with such machines. background 0.79 0.06 0.02 asserted 1.00
s4 Although the simple binary expansion in the previous research requires many binary variables, we need to reduce the number of such variables in the QUBO formulation due to the constraint. background 0.62 0.10 0.06 asserted 1.00
s5 We propose a discretization method that involves using correlations of continuous variables. contribution 0.50 0.83 0.12 asserted 0.97
s6 We numerically show that the proposed method reduces the number of necessary binary variables in the QUBO formulation without a significant loss in prediction accuracy. result 1.00 0.96 0.90 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from simulation or computation 1.00.

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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

Annealing machines specialized for combinatorial optimization problems have been developed, and some companies offer services to use those machines. Such specialized machines can only handle binary variables, and their input format is the quadratic unconstrained binary optimization (QUBO) formulation. Therefore, discretization is necessary to solve problems with continuous variables. However, there is a severe constraint on the number of binary variables with such machines. Although the simple binary expansion in the previous research requires many binary variables, we need to reduce the number of such variables in the QUBO formulation due to the constraint. We propose a discretization method that involves using correlations of continuous variables. We numerically show that the proposed method reduces the number of necessary binary variables in the QUBO formulation without a significant loss in prediction accuracy.

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