Venue: ICLR 2023 Conference

Reviews:

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: The paper proposes applying association rule mining for reducing the problem size of QUBO. The mining is performed for solution samples, and the authors proposes an efficient approximate association rule mining specifically for QUBO. Empirical results show that the proposed procedure can substantially reduce the problem size and find good solutions compared with an existing method.
strength_and_weaknesses: Strength:
- The basic idea, combining a quantum annealer (QA) and association rule mining, is interesting and seems novel.
- The empirical results suggest superiority of the proposed method.

Weaknesses:
- As the authors mentioned, determining the number of variables that maximizes performance is difficult.
- No theoretical guarantee or justification for the approximation quality (e.g., risk to find wrong rule) is provided.
- The paper is sometimes difficult to follow for those who are not familiar with a QA. 
clarity,_quality,_novelty_and_reproducibility: Clarity: OK, but sometimes a bit difficult to follow for those who are not familiar with a QA. 

Quality: Fine, though the proposed method is a simple heuristics.

Novelty: Combination of annealer and rule mining would be novel

Reproducibility: OK.

summary_of_the_review: The paper seems to try an interesting approach combining QA and rule mining. Although the proposed method is a simple heuristics that do not have a theoretical support, the empirical results suggest the practical usefulness of the proposed framework. However, to be honest, I am not familiar with the topic and my review contains educated guess.
review_title: null

final_score: rating: 3: reject, not good enough
review_content: summary_of_the_paper: The paper proposes to use heuristic method to discover association rules between variables in an optimization problem.

strength_and_weaknesses: + An interesting idea compared to the state of the art works
- Poor results
clarity,_quality,_novelty_and_reproducibility: Some concepts introduced in the paper should be clarified. It seem an incremental work where many concepts could better described and the results should be improved adopting different strategies.
summary_of_the_review: The introduction of the problem is unclear. For instance in the introduction in not clear the the meaning of samples in an optimization problem. This should be clarified.

The are same part of the paper that could be better explained. For instance how the rules have been adopted to reduce the size of the dataset is not reported. 

The idea could be interesting but the experimental evaluation is poor and the validity of the proposed approach is non confirmed by the reported results.
review_title: null

Meta-review: metareview:_summary,_strengths_and_weaknesses: We apologize for failing to find a third reviewer despite many requests and reminders.

As the area chair, I am familiar with quantum annealing and with mining association rules. I agree with the overall sentiment of the two reviewers that this paper is interesting. It should be published, but not at ICLR now. 

In their responses, the authors provided clarifications but did not show that the reviews are incorrect. To help the authors resubmit elsewhere, here are a few questions or suggestions.

1. Clarify whether you used actual quantum hardware from D-Wave and/or Fujitsu. If yes, show that your method enables finding optimal solutions better or faster using the hardware. None of the current figures show this.

2. The FARM method does not have guarantees, and the rules discovered may be false in the solution to the annealing problem. So the contribution of the paper is not solid and strong enough for ICLR.

3. Be more incisive when discussing previous research. This area chair has been a professor and FAANG director in ML for over 30 years. I am not aware of a single real-world useful application of association rules, for market basket analysis or for anything else.

Also, fix the formatting of citations, which has words run together with missing spaces
justification_for_why_not_higher_score: Results are preliminary.
justification_for_why_not_lower_score: The idea is worth exploring.
Decision: Reject