Pith. sign in

REVIEW 5 major objections 5 minor 1 cited by

Demonstration of a Compatibility-Based Childcare Support Service using Quantum Annealing

T0 review · 5 major / 5 minor · reviewed 2026-08-04 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read This paper shows that matching parents to experienced senior supporters, with compatibility scores and workload limits, can be solved as a QUBO, and that quantum annealing keeps solution quality and diversity where simulated annealing degra

desk verdict Honest, well-benchmarked QUBO application paper whose central QA-advantage claim rests on two instance sizes the authors themselves say may be coincidence—worth a referee but with claims to temper. read the letter →

arxiv 2509.08520 v1 pith:6Y2NWGDD submitted 2025-09-10 quant-ph

Yuuma Matsumoto , Taisei Takabayashi , Rima Sato , Rumiko Honda , Masayuki Ohzeki This is my paper · ORCID
classification quant-ph
keywords quantumannealingQUBObipartitematchingcompatibilityscoringsimulatedsolutiondiversitychildcaresupportintergenerationalexchange
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Isolated parenting in Japan is the problem: existing childcare support leans on physical help, leaving psychological strain and isolation unaddressed. The authors propose a service that pairs parents with experienced senior supporters, scoring each possible pair by questionnaire-derived compatibility and treating the assignment as a quadratic unconstrained binary optimization (QUBO) problem with workload and schedule constraints. Solving random benchmark instances, they find that quantum annealing keeps relative error low and samples a more diverse set of near-optimal matchings as the problem grows, while simulated annealing's quality and diversity degrade. They also propose a compressed "top-2" formulation that cuts the number of decision variables to the number of users, and a field experiment in Sendai with 14 parents and 14 supporters recovered all eight optimal matchings after pre-filtering, showing that multiple high-quality options can be offered to schedulers.

What carries the argument

The load-bearing mechanism is the QUBO formulation: binary variables mark each user-supporter pair, the compatibility score enters the linear objective, and the two hard requirements—each user matched once, each supporter given exactly C_j users—are encoded as squared penalty terms whose strengths are tuned. Because these penalties create barriers that hurt annealing, the paper exploits a second mechanism, the approximate top-2 formulation: each user is restricted to their first- and second-highest-compatibility supporters, so a single binary variable per user replaces n^2 variables and the user-side constraint is satisfied by construction. On the quantum-annealing side, the solution is obta

What would settle it

Run the same n=14 and n=15 benchmarks with compatibility scores drawn from other distributions, such as skewed or clustered score patterns, or from a second real city's questionnaire data. If simulated annealing matches quantum annealing's relative error and diversity on those instances, the reported advantage is an artifact of the one fitted distribution. A second check is to re-embed the same QUBO with different chain strengths; if the advantage disappears, it is a hardware artifact rather than a property of quantum annealing.

Watch

Extended reading notes

Core claim

The paper's central claim is that user-supporter matching for a psychosocial childcare service can be encoded as a QUBO and solved well enough to be operationally useful, with quantum annealing providing an advantage as instances grow. On random instances with n users and n supporters, using scores drawn from a bell-curve distribution fitted to the field data, simulated annealing's relative error grows with n, while quantum annealing stayed near zero at n=14 and n=15 and, with greedy post-processing, returned a broader and more distinct set of near-optimal matchings as measured by a diversity metric. The authors also establish a compressed formulation in which each user picks between their t

Load-bearing premise

The whole quantum-annealing advantage is measured on random instances generated from one bell-curve score distribution fitted to a single small city dataset; if real compatibility scores look different, the advantage may not survive.

Editorial extensions

If this is right

  • A service operator can offer schedulers several near-optimal matchings instead of a single computed optimum, which matters when compatibility scores are subjective and visit schedules must be arranged flexibly.
  • The top-2 compression reduces qubit and variable counts enough to tackle larger matching instances; its gap to the exact optimum shrinks as the number of users per supporter grows.
  • Pre-filtering infeasible pairs before optimization does not necessarily destroy the possibility of a perfect matching; in the field data, roughly 62% of pairs could be removed while all eight optima remained reachable.
  • If the observed quantum-annealing advantage holds beyond the tested random instances, quantum annealing becomes a practical sampler for diverse high-quality solutions in service-matching operations, not just an exact-optimum finder.
  • Solution diversity can be used as a design criterion for real operations, giving decision-makers alternatives that are both high-scoring and structurally different.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The authors flag that the strong quantum-annealing result at n=14 and n=15 may be a coincidence; read that way, the scaling advantage is a testable hypothesis, not an established law.
  • The top-2 compression is really a member of a top-k family; increasing k trades a linear growth in variables for a tighter approximation, and the expected candidate-surplus argument gives a quantitative guide to the trade.
  • If a service values flexibility as much as score, the QUBO could reward diversity directly—for example by penalizing structurally repeated matchings—instead of only measuring diversity after sampling.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 5 minor

Summary. The paper proposes a QUBO-based matching framework for pairing parents with experienced senior supporters, using compatibility scores derived from a 10-item questionnaire. Two formulations are presented: a naive edge-based QUBO (Eq. 7) and a compressed 'top-2' approximation (Eq. 11) in which each user is restricted to their two highest-compatibility supporters. The authors benchmark D-Wave quantum annealing (QA) against simulated annealing (SA) on random instances, using Gurobi as an exact reference. They report that QA achieves lower relative error than SA at larger problem sizes (n=14, 15) and higher solution diversity at n=15, while SA is better on small instances. They further test the approximate formulation using SA on larger synthetic instances, and describe a field experiment in Sendai with 14 users and 14 supporters; after pre-filtering, the exact formulation was solved with SA and produced eight optimal matchings.

Significance. If the reported QA advantage is robust, the paper would contribute a practically motivated application of quantum annealing to a social-service matching problem, with a particularly useful emphasis on solution diversity rather than only the single optimum. The manuscript has clear strengths: the benchmark against the exact solver Gurobi is appropriate; the solver settings (num_reads, num_sweeps, annealing_time, chain_strength) are reported; the authors honestly state that the n=14/15 QA advantage is unexplained and may be coincidental. However, the central claims are currently supported by limited evidence: the QA advantage rests on two problem sizes from a single synthetic distribution with no statistical testing, and the real-world demonstration uses SA rather than QA. The approximate formulation's stated 'approximation bound' is never actually stated or proved. These gaps prevent the paper from being acceptable as a demonstration of a quantum-annealing advantage.

major comments (5)
  1. [Results, Figure 5 and accompanying paragraph] The abstract's central claim that 'QA achieved higher solution quality and diversity, particularly for larger problem instances' rests almost entirely on the n=14 and n=15 columns of Figure 5. The text itself concedes: 'The reason why QA exhibited particularly strong performance at n=14 and n=15 remains unclear... this behavior might just be a coincidence and not something intrinsic to the algorithm.' No statistical significance test, effect size, or per-instance paired comparison is reported; the mean and standard error over 100 instances can be dominated by a few outliers or by sampling noise. Please report per-instance paired differences (e.g., Wilcoxon signed-rank tests), show error bars or distributions for every n, and test on additional problem families (different n ranges, score distributions, and filtering densities) before claiming a size-dependent QA advantage.
  2. [Results, Figure 7] The diversity advantage is presented as a key practical benefit, but Figure 7 is the only evidence and it shows results for a single problem size (n=15), a single synthetic score distribution, and no measure of variability across the 100 instances. Diversity also depends on the arbitrary thresholds α and R, and the maximum-independent-set computation adds further methodological choices that are not described in detail. Please compute diversity per instance across the benchmark, report paired comparisons and confidence intervals, and provide sensitivity analysis with respect to α and R. Without this, the conclusion that QA 'sampled a broader set of near-optimal matchings than SA' is not supported.
  3. [Results, Figure 9/10 paragraph] The manuscript states: 'The approximation bound of the formulation is indicated, and the results demonstrate that its approximation accuracy improves as the number of users N increases.' However, no approximation bound is ever stated or proved anywhere in the paper. The top-2 truncation is introduced heuristically (Eq. 8-11), and its accuracy is only demonstrated empirically on Gaussian-distributed instances using SA. This is a load-bearing gap for the claimed contribution of the compressed formulation: either derive a worst-case or expected approximation ratio for Eq. (11) relative to Eq. (4)-(6), or explicitly remove the assertion that a bound is indicated and reframe the result as purely empirical.
  4. [Results, field experiment (final paragraphs of Results)] The proof-of-concept field experiment in Sendai does not use quantum annealing. After pre-filtering leaves 74 feasible pairs, the text states: 'we used the exact formulation (7) and performed optimization using SA.' Thus the real-world demonstration validates the QUBO/matching framework and the value of multiple optimal matchings, but it does not demonstrate the QA component. The title and abstract ('Demonstration of a Compatibility-Based Childcare Support Service using Quantum Annealing') overstate the role of QA in the field study. Either run the field instance on the QA hardware (n=14, 196 QUBO variables, which appears feasible from Figure 4) or revise the title and claims to distinguish the QUBO demonstration from the QA benchmark.
  5. [Methods, synthetic benchmark generation] All synthetic instances are generated from a Gaussian distribution with mean 12.3 and variance 2.80, estimated from a single 14x14 Sendai dataset (Figure 12). The actual compatibility scores are bounded integers in [6,21], while the Gaussian model is continuous and unbounded, potentially producing unrealistic instances outside this range. Because both the quality results (Figure 5/6) and the diversity results (Figure 7) are obtained exclusively on this distribution, the external validity of the central claim is limited. Please include robustness checks using the actual score matrix, bootstrap resampling of the real scores, and other score distributions (e.g., uniform, truncated Gaussian, or distributions from other cities).
minor comments (5)
  1. [Figure 5 and Figure 6 captions] The text says the plotted values are means with standard error, but the figures do not appear to show error bars. Please clarify whether error bars are omitted for visual clarity and, if so, state this explicitly.
  2. [Equation (7) and Figure 5] Equation (7) is a penalized QUBO, not the constrained matching problem itself; for finite λ1 and λ2 its ground state may violate constraints. The caption of Figure 5 refers to 'the best feasible solution,' but the feasibility-checking procedure is not described. Please state how feasibility was verified when extracting solutions from SA/QA/Gurobi.
  3. [Equations (8)-(11)] The decision variable is redefined from x_e in Eq. (3) to x_i in Eq. (8), and the notation M_i^(1)/M_i^(2) is introduced without explicit definitions. Please add a small table or explicit definitions of these quantities.
  4. [Parameter tuning, paragraph on λ] The penalty coefficients in the approximate formulation (11) are denoted λ, while Eq. (7) uses λ1 and λ2. The text says the same tuning procedure was applied, but it is not explicit which λ value was used for Eq. (11). Please state this clearly.
  5. [Figure 10 caption] The caption of Figure 10 states the relative error is with respect to the naive formulation optimum, but the text in the paragraph does not emphasize that the approximate-formulation optimal line is also computed relative to that same optimum. Repeating the definition would improve readability.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; the derivation chain is self-contained and the admitted n=14/15 uncertainty is a robustness limitation, not circular reasoning.

full rationale

The paper's derivation chain does not reduce to its inputs. Compatibility scores are defined by a fixed closed-form rule (Eqs. 1-2), not fitted to the matching outcomes being evaluated. The QUBO objective (Eq. 7) is a standard penalty encoding of a bipartite matching problem, and solver quality is measured against an independent exact optimum from Gurobi, not against any parameter fitted from the solver results. The approximate top-2 formulation (Eqs. 8-11) is explicitly derived in the paper and is subsequently benchmarked against the naive formulation in Figures 9-10; the citations to refs. 25/26 for the underlying idea are contextual and not the only support for the formulation. The random benchmark distribution (mean 12.3, variance 2.80) is fitted to one real data set, but this is an input distribution, not a fitted parameter used to produce the QA-vs-SA conclusion. The paper explicitly acknowledges that the QA advantage at n=14 and n=15 'might just be a coincidence and not something intrinsic to the algorithm' (Results, Figure 5 discussion); that is a limitation on external validity, not a circular step. No load-bearing self-citation or construction-equals-prediction pattern is present.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The central claim rests on penalty tuning, a Gaussian fit to one dataset, a hand-chosen top-2 truncation, and the domain assumption that questionnaire item agreements capture psychological compatibility. No new physical entities or forces are introduced.

free parameters (5)
  • Penalty coefficients lambda1, lambda2, lambda = not stated; chosen by SA search on representative instances
    Control strictness of constraints in eqs (7) and (11); tuned per problem, not derived.
  • chain_strength = not stated; selected by experiments
    D-Wave chain strength tuned on several instances to balance chain breaks and coefficient scaling.
  • Gaussian mean and variance for synthetic scores = mean=12.3, variance=2.80
    Fitted to the Sendai score histogram and then used to generate random benchmark instances.
  • Top-2 candidate truncation = 2
    The approximate formulation keeps only each user's two highest-scoring supporters; this hand-chosen truncation defines the approximation and is not justified by a proven bound.
  • Diversity thresholds alpha and R = alpha in [0,0.25], R=0.1,0.5
    Analysis parameters chosen to define near-optimal range and distinctness; not fitted but user-selected.
assumptions (4)
  • standard math The penalty method converts constrained maximization into unconstrained QUBO with sufficiently large lambdas.
    Used in eq (7) and eq (11); standard but relies on lambda being large enough.
  • domain assumption Compatibility can be measured as the sum of 10 four-level item agreement scores, with exact matches scoring 3.
    This operationalizes psychological compatibility as a linear sum of self-reported item agreements; no external validation is provided.
  • ad hoc to paper Low-compatibility pairs can be discarded without hurting solution quality, formalized as the top-2 truncation.
    Motivated by a heuristic argument in 'Parameter tuning and solver settings' and by prior work, but no formal approximation guarantee is derived.
  • ad hoc to paper Synthetic test scores follow a Gaussian distribution.
    Used to generate random instances; fitted from one small field dataset.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Demonstration of a Compatibility-Based Childcare Support Service using Quantum Annealing." pith.science (2026). https://pith.science/paper/6Y2NWGDD

@misc{pith2026250908520,
  author       = {Pith},
  title        = {Pith review of: Demonstration of a Compatibility-Based Childcare Support Service using Quantum Annealing},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6Y2NWGDD}},
  note         = {Machine review of arXiv:2509.08520}
}
read the original abstract

In contemporary Japan, isolated parenting has become a serious social issue, increasing psychological stress on parents and potentially affecting children's development. Existing childcare support services tend to focus on physical assistance, while psychological support and community connections remain insufficient. To address this gap, we developed a service that connects parents with senior community members who have parenting experience, aiming to provide psychological support and foster intergenerational exchange. Achieving high-quality matching requires considering pair compatibility, balancing supporter workload, and handling scheduling constraints, which can be formulated as a combinatorial optimization problem. We designed a matching framework using the Quadratic Unconstrained Binary Optimization (QUBO) formulation and evaluated quantum annealing (QA) against simulated annealing (SA). QA achieved higher solution quality and diversity, particularly for larger problem instances. Furthermore, a proof-of-concept field experiment conducted in Sendai City, Japan, demonstrated that the framework can generate multiple high-quality matching candidates, enabling flexible scheduling in real-world operations.

Figures

Figures reproduced from arXiv: 2509.08520 by the authors.

Figure 1
Figure 1. Schematic diagram of survey items. This diagram illustrates example items related to personality. Respondents are asked to choose the option that best reflects their tendency from four available choices. 2/17 [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Visualization of the overall compatibility score Mi j calculated using random data of 14 users and five supporters. The combination of users and supporters corresponds to each cell in the matrix, and the number inside represents the compatibility score Mi j [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. The masked version of [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (11 more)
Figure 4
Figure 4. Figure 4: Comparison of the number of qubits required for embedding concerning problem size n. The horizontal axis shows the number of users and supporters n, and the vertical axis shows the number of qubits required for embedding. 4 6 8 10 12 14 n 0.00 0.01 0.02 0.03 0.04 0.05 …
Figure 5
Figure 5. Figure 5: Comparison of the relative errors from the optimal solutions obtained using the exact solver Gurobi Optimizer (version 12.0.2). The horizontal axis represents the number of users and supporters n, and the vertical axis shows the relative error from the solutions obtain…
Figure 6
Figure 6. Figure 6: Histograms of the energy distributions of the solutions obtained by SA and QA. The horizontal axis represents the relative error, and the vertical axis indicates the probability of occurrence of samples corresponding to each relative error. For QA, the results with and…
Figure 7
Figure 7. Figure 7: Each solver obtained a comparison of the solution diversity (size of the maximum independent set). The horizontal axis α represents the allowable error from the optimal cost, and the vertical axis indicates the diversity of the solution set within that range. The solid…
Figure 8
Figure 8. Figure 8: Comparison of computation times for each solver. The horizontal axis represents the problem size n, and the vertical axis shows the computation time. QA (QPU access) and SA (sampling) represent the time required to execute the predefined 1000 samples for a single probl…
Figure 9
Figure 9. Figure 9: The relative error of the two formulations in SA depends on the number of users, with the number of supporters fixed at M = 10. Here, the relative error is defined as the deviation from the optimal solution of the naive formulation (7). 11/17 [PITH_FULL_IMAGE:figures/…
Figure 10
Figure 10. Figure 10: The relative error of the two formulations in SA depends on the number of users, with the number of supporters fixed at M = 4. Here, the relative error is defined as the deviation from the optimal solution of the naive formulation (7). The results for the larger-scale…
Figure 11
Figure 11. Figure 11: Compatibility scores between pairs are calculated based on the actual questionnaire results. There are 14 users and 14 supporters, and a compatibility score is assigned to each pair. The questionnaire consists of 10 items, and since the maximum item score m µ i j is t…
Figure 12
Figure 12. Figure 12: Histogram visualizing the distribution of compatibility scores. There are few extremely compatible or incompatible pairs, and the distribution shows a gradual peak around 11 and 12. 13/17 [PITH_FULL_IMAGE:figures/full_fig_p013_12.png]
Figure 13
Figure 13. Figure 13: The compatibility score matrix is after masking the infeasible pairs for matching due to the pre-processing filter. Approximately 38 % of the pairs remain feasible for matching. However, a one-to-one matching can still be achieved. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Use…
Figure 14
Figure 14. Figure 14: One of the optimal matchings obtained from sampling with SA. The pairs enclosed in red boxes represent the matched pairs, with exactly one user assigned to each supporter. Among the 1000 samples, all eight optimal matching solutions were successfully obtained. 14/17 …

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Mixed-Binary Quadratic Programming via QUBO Sampling without Continuous-Variable Binarization

    quant-ph 2026-07 unverdicted novelty 6.0 of 10

    For separable mixed-binary quadratic programs, the continuous variables are integrated out analytically at fixed Lagrange multipliers, yielding a QUBO sampling formulation that avoids binarization and outperforms pena...

Reference graph

Works this paper leans on

25 extracted references · 3 canonical work pages · cited by 1 Pith paper

  1. [1]

    Current situation of children and childrearing and the voices and attitudes of youth and childrearing stakeholders

    Cabinet Secretariat, Office for the Establishment of the Children and Families Agency. Current situation of children and childrearing and the voices and attitudes of youth and childrearing stakeholders. https://www.cas.go.jp/jp/seisaku/kodomo_ seisaku_kyouka/dai1/siryou5.pdf (2023). Accessed: July 23, 2025

    2023

  2. [2]

    & Ihara, Y

    Morita, M., Saito, A., Nozaki, M. & Ihara, Y . Childcare support and child social development in japan: investigating the mediating role of parental psychological condition and parenting style.Philos. Transactions Royal Soc. B: Biol. Sci.376, 20200025, DOI: 10.1098/rstb.2020.0025 (2021)

    arXiv 2020

  3. [3]

    Glover, F., Kochenberger, G., Hennig, R. & Du, Y . Quantum bridge analytics i: a tutorial on formulating and using qubo models.Annals Oper. Res.314, 141–183, DOI: 10.1007/s10479-022-04634-2 (2022)

    doi:10.1007/s10479-022-04634-2 2022

  4. [4]

    & Nishimori, H

    Kadowaki, T. & Nishimori, H. Quantum annealing in the transverse ising model.Phys. Rev. E58, 5355–5363, DOI: 10.1103/PhysRevE.58.5355 (1998)

    doi:10.1103/physreve.58.5355 1998

  5. [5]

    Ising formulations of many np problems.Front

    Lucas, A. Ising formulations of many np problems.Front. Phys.V olume 2 - 2014, DOI: 10.3389/fphy.2014.00005 (2014). 6.Neukart, F.et al.Traffic flow optimization using a quantum annealer (2017). 1708.01625

    arXiv 2014

  6. [7]

    & Yoshida, H

    Inoue, D., Okada, A., Matsumori, T., Aihara, K. & Yoshida, H. Traffic signal optimization on a square lattice with quantum annealing.Sci. Reports11, 3303, DOI: 10.1038/s41598-021-82740-0 (2021)

    doi:10.1038/s41598-021-82740-0 2021

  7. [8]

    & Tanaka, K

    Shikanai, R., Ohzeki, M. & Tanaka, K. Quadratic unconstrained binary formulation for traffic signal optimization on real-world maps.J. Phys. Soc. Jpn.94, 024001, DOI: 10.7566/JPSJ.94.024001 (2025)

    work page doi:10.7566/jpsj.94.024001 2025

  8. [9]

    Ohzeki, M., Miki, A., Miyama, M. J. & Terabe, M. Control of automated guided vehicles without collision by quantum annealer and digital devices.Front. Comput. Sci.V olume 1 - 2019, DOI: 10.3389/fcomp.2019.00009 (2019)

    arXiv 2019

Show all 25 references
  1. [10]

    & Tanaka, K

    Haba, R., Ohzeki, M. & Tanaka, K. Travel time optimization on multi-agv routing by reverse annealing.Sci. Reports12, 17753, DOI: 10.1038/s41598-022-22704-0 (2022). 15/17

    2022 doi

  2. [11]

    Rosenberg, G.et al.Solving the optimal trading trajectory problem using a quantum annealer.IEEE J. Sel. Top. Signal Process.10, 1053–1060, DOI: 10.1109/JSTSP.2016.2574703 (2016)

    2016

  3. [12]

    & Kondratyev, A

    Venturelli, D. & Kondratyev, A. Reverse quantum annealing approach to portfolio optimization problems.Quantum Mach. Intell.1, 17–30, DOI: 10.1007/s42484-019-00001-w (2019)

    2019 doi

  4. [14]

    H., Andriyash, E., Rolfe, J., Kulchytskyy, B

    Amin, M. H., Andriyash, E., Rolfe, J., Kulchytskyy, B. & Melko, R. Quantum boltzmann machine.Phys. Rev. X8, 021050, DOI: 10.1103/PhysRevX.8.021050 (2018)

    2018 doi

  5. [15]

    V ., Alexandrov, B

    O’Malley, D., Vesselinov, V . V ., Alexandrov, B. S. & Alexandrov, L. B. Nonnegative/binary matrix factorization with a d-wave quantum annealer.PLoS ONE13, e0206653, DOI: 10.1371/journal.pone.0206653 (2018)

    2018 doi

  6. [16]

    & Tanaka, K

    Sato, T., Ohzeki, M. & Tanaka, K. Assessment of image generation by quantum annealer.Sci. Reports11, 13523, DOI: 10.1038/s41598-021-92295-9 (2021)

    2021 doi

  7. [17]

    & Tanaka, K

    Urushibata, M., Ohzeki, M. & Tanaka, K. Comparing the effects of boltzmann machines as associative memory in generative adversarial networks between classical and quantum samplings.J. Phys. Soc. Jpn.91, DOI: 10.7566/jpsj.91.074008 (2022). 18.Hasegawa, Y ., Oshiyama, H. & Ohzek...

    2022 arXiv

  8. [19]

    & Ohzeki, M

    Goto, T. & Ohzeki, M. Online calibration scheme for training restricted boltzmann machines with quantum annealing.J. Phys. Soc. Jpn.94, DOI: 10.7566/jpsj.94.034002 (2025)

    2025 doi

  9. [20]

    & Ohzeki, M

    Takabayashi, T., Goto, T. & Ohzeki, M. Subgradient method using quantum annealing for inequality-constrained binary optimization problems.J. Phys. Soc. Jpn.94, 054003, DOI: 10.7566/JPSJ.94.054003 (2025). https://doi.org/10.7566/JPSJ. 94.054003

    2025 doi

  10. [21]

    Yonaga, K., Miyama, M. J. & Ohzeki, M. Solving inequality-constrained binary optimization problems on quantum annealer (2020). 2012.06119

    2020 arXiv

  11. [22]

    Yonaga, K.et al.Quantum optimization with lagrangian decomposition for multiple-process scheduling in steel manufac- turing.ISIJ Int.62, 1874–1880, DOI: 10.2355/isijinternational.ISIJINT-2022-019 (2022)

    2022 doi

  12. [23]

    Breaking limitation of quantum annealer in solving optimization problems under constraints.Sci

    Ohzeki, M. Breaking limitation of quantum annealer in solving optimization problems under constraints.Sci. Reports10, 3126, DOI: 10.1038/s41598-020-60022-5 (2020)

    2020 doi

  13. [24]

    & Ohzeki, M

    Hirama, S. & Ohzeki, M. Efficient algorithm for binary quadratic problem by column generation and quantum annealing. J. Phys. Soc. Jpn.92, 113002, DOI: 10.7566/JPSJ.92.113002 (2023). https://doi.org/10.7566/JPSJ.92.113002. 25.Ohzeki, M. Presentation at qubits 2023 (2023). In p...

    2023 doi

  14. [26]

    & Ohzeki, M

    Takabayashi, T., Sudo, S., Aoki, T., Seo, S. & Ohzeki, M. Optimization of connection patterns between mobile phones and base stations using quantum annealing.Sci. Reports15, DOI: 10.1038/s41598-025-09230-5 (2025)

    2025 doi

  15. [27]

    Kirkpatrick, S., Gelatt, C. D. & Vecchi, M. P. Optimization by simulated annealing.Science220, 671–680, DOI: 10.1126/science.220.4598.671 (1983). https://www.science.org/doi/pdf/10.1126/science.220.4598.671

    1983 doi

  16. [28]

    Openjij: Framework for the ising model and qubo

    OpenJij. Openjij: Framework for the ising model and qubo. https://github.com/Jij-Inc/OpenJij (2023). Accessed: 2025-07-22. 29.Cai, J., Macready, W. G. & Roy, A. A practical heuristic for finding graph minors (2014). 1406.2741. 30.D-Wave Systems. Programming the d-wave qpu: Set...

    2023 arXiv

  17. [32]

    & Amin, M

    Zucca, A., Sadeghi, H., Mohseni, M. & Amin, M. H. Diversity metric for evaluation of quantum annealing (2021). 2110.10196. Acknowledgments This study was financially supported by programs for bridging the gap between R&D and IDeal society (Society 5.0) and Generating Economic ...

    2021 arXiv

Pith tools

Reviewed August 4, 2026 · model on record in the stance chip above.