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On the application of matrix congruence to QUBO formulations for systems of linear equations

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Sun Woo Park, Hyunju Lee, Byung Chun Kim, Youngho Woo, Kyungtaek Jun

Quantum Physics · Submitted 1 Nov 2021

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

The authors' main claim We further exhibit computational merits of the proposed QUBO models, which can outperform classical algorithms such as QR and SVD decomposition.

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arXiv ID2111.00747 (version 1)
Pith IDO7XGED3T, Pith's permanent record of this paper and its review
CitationByung Chun Kim, Hyunju Lee, Kyungtaek Jun, Sun Woo Park, Youngho Woo. "On the application of matrix congruence to QUBO formulations for systems of linear equations." arXiv:2111.00747 (2021). https://pith.science/paper/O7XGED3T
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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 3,549 input tokens.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.05 · human participants 0.02 · controlled comparison 0.38 · randomized assignment 0.01 · states quantitative result 0.04 · compares with prior results 0.89 · mentions code or data availability 0.02.

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

Recent studies on quantum computing algorithms focus on excavating features of quantum computers which have potential for contributing to computational model enhancements. Among various approaches, quantum annealing methods effectively parallelize quadratic unconstrained binary optimization (QUBO) formulations of systems of linear equations. In this paper, we simplify these formulations by exploiting congruence of real symmetric matrices to diagonal matrices. We further exhibit computational merits of the proposed QUBO models, which can outperform classical algorithms such as QR and SVD decomposition.

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 Recent studies on quantum computing algorithms focus on excavating features of quantum computers which have potential for contributing to computational model enhancements. background 1.00 0.04 0.02 asserted 0.62
s1 Among various approaches, quantum annealing methods effectively parallelize quadratic unconstrained binary optimization (QUBO) formulations of systems of linear equations. background 1.00 0.07 0.05 asserted 1.00
s2 In this paper, we simplify these formulations by exploiting congruence of real symmetric matrices to diagonal matrices. method 0.71 0.94 0.16 asserted 0.99
s3 We further exhibit computational merits of the proposed QUBO models, which can outperform classical algorithms such as QR and SVD decomposition. result 0.98 0.86 0.71 asserted 0.98

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from simulation or computation 0.60.

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  • full text
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  • references and citing sentences
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  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

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

Recent studies on quantum computing algorithms focus on excavating features of quantum computers which have potential for contributing to computational model enhancements. Among various approaches, quantum annealing methods effectively parallelize quadratic unconstrained binary optimization (QUBO) formulations of systems of linear equations. In this paper, we simplify these formulations by exploiting congruence of real symmetric matrices to diagonal matrices. We further exhibit computational merits of the proposed QUBO models, which can outperform classical algorithms such as QR and SVD decomposition.

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Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

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