{"id":"a86c3b03-b665-447c-9c8b-ef9c12dc8f29","arxiv_id":"2506.18260","paper_version":1,"verdict":"REJECT","confidence":"MODERATE","novelty_score":4.0,"correctness_risk":"high","formal_verification":"none","parameter_count":0,"one_line_summary":"The paper sketches an LLM-based multi-agent framework that generated quantum variants of MLP, forward-forward, and backpropagation, but with no reproducible evidence that the search works.","lead":"One author proposes a multi-agent AI system that uses large language models to rewrite classical machine learning algorithms into quantum versions. This proof of concept reports near-random classification accuracies, includes no released code, and does not demonstrate the claimed iterative search.","discovery_kind":"new_application","skeptic_critique":{"model":"deepseek-v4-flash","headline":"No evidence that the claimed agentic evolution loop ran: code, logs, and search trajectories are absent, and Table 1's near-random accuracies do not distinguish successful translation from chance.","rationale":"The paper proposes an interesting adaptation of FunSearch to QML, with a plausible agent decomposition and a clear goal: translate classical ML algorithms into quantum counterparts. I do not see fraud or a necessarily false architecture; the problem is evidentiary. For the central claim that the system iteratively searches and optimizes QML programs, the body offers only three cropped snippets and a table of near-random accuracies. No repository, execution logs, API calls, or search trajectories are included, so an independent reviewer cannot distinguish a working agentic loop from a small number of one-shot LLM generations curated by the author. The paper itself concedes short training (Section 2.4). On a 10-class task, 15.17% versus 15.55% baseline is not evidence of 'similar performance' without error bars. Thus the weakest link is not a mathematical error but a missing empirical foundation. A concrete release-and-reproduction test would settle it; until then, REJECT is the right gatekeeping decision. This aligns with the reader's rationale, though I place less weight on whether the snippets are correct in isolation and more on whether any iterative search actually occurred.","tokens_in":5428,"tokens_out":4343,"duration_ms":42396,"concrete_test":"Obtain the exact source code and agent logs from the authors, place them in a fresh environment with the same Pennylane version, and execute the QFF pipeline end-to-end; verify that the code runs, that Figure 6 matches the actual generated artifact, and that Table 1's QFF accuracy is reproduced within sampling error. Absent release or successful reproduction, the proof-of-concept is unsubstantiated.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim (abstract; Section 2) is that a multi-agent system with Theorist, Coder, and Validator roles runs an iterative evolution loop that generates and refines quantum implementations of classical ML algorithms. For this claim to hold, we need both (1) the displayed snippets in Figures 4a, 6, and 8 to be faithful, runnable quantum code actually produced by the loop, and (2) some observable evidence that iteration and optimization occurred. The paper provides neither. The snippets are cropped and unlabeled, with no accompanying repository, tests, or execution logs. Table 1 reports no error bars, and all accuracies (9.40-15.55%) are near the 10% random-chance level for 10-class digit classification, so 'QFF similar to baseline' is weak evidence. Section 2.4 concedes models were not trained with massive steps, and no search trace, version history, or ablation shows Validator-driven refinement. The abstract's 'iteratively generates and refines' is therefore unsupported by the body; every displayed result is consistent with one-shot LLM generation followed by manual curation.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The manuscript proposes an LLM-based multi-agent system, inspired by FunSearch, that takes a classical machine learning algorithm name, generates an initial quantum implementation, and then runs an iterative evolution loop with Theorist, Coder, and Validator roles to refine the implementation. As a proof of concept, it reports quantum versions of the multi-layer perceptron, the forward-forward algorithm, and backpropagation, with accuracy results in Table 1 compared against a human-crafted quantum neural network baseline. The paper concludes that the framework can serve as a 'for-loop' over classical ML concepts to produce quantum counterparts.","tokens_in":5621,"tokens_out":3227,"duration_ms":34413,"significance":"If substantiated, the framework would be a genuinely useful tool for automating the translation of classical ML algorithms into quantum implementations, and the choice of three well-known algorithms is a sensible starting point. The authors honestly acknowledge in Section 2.4 that training was not extensive and that the work is a proof of concept, which is a strength in framing. However, the paper is an extended abstract rather than a complete research report: there is no reproducible code, no execution logs, no search trajectories, and no statistical analysis. The only quantitative evidence is a four-row accuracy table with values near the random-chance floor for a 10-class task, so the central demonstration does not currently support the abstract's claim of iterative generation and refinement. The conceptual idea has potential, but the evidence presented is anecdotal and not yet at the standard of a research paper.","major_comments":[{"comment":"The central claim that the agentic loop generates runnable quantum implementations is unsupported because the displayed snippets are cropped, unlabeled, and not accompanied by the full source, tests, execution logs, or a repository link; without these, a reader cannot verify that the positive and negative passes in the quantum forward-forward implementation or the parameter-shift gradient in the quantum backpropagation implementation behave as described.","section":"Section 2, Figures 4a, 6, and 8"},{"comment":"The only quantitative evidence is a four-row accuracy table with no error bars, seeds, number of runs, hyperparameters, or statistical tests; since a 10-class random classifier attains 10% and every reported value (9.40% to 15.55%) is near that floor, the statement that QFF (15.17%) is 'similar' to the baseline QNN (15.55%) is not supported, and in fact the baseline itself is also near chance.","section":"Table 1 and Section 2.4"},{"comment":"The abstract's claim that the system 'iteratively generates and refines' is not evidenced: no search trajectory, version history, validator feedback, or ablation is shown, so every displayed result is consistent with one-shot LLM generation followed by manual selection, and the evolution loop is asserted rather than demonstrated.","section":"Section 2 and Figure 2"},{"comment":"The paper's own caveat that models were not trained with massive training steps, combined with the absence of a labeled convergence curve, means the statement that the quantum MLP is 'learning during the training steps' is not verifiable; a proof of concept needs at least one training curve with clearly labeled axes, loss values, and training configuration.","section":"Section 2.4 and Figure 4b"}],"minor_comments":[{"comment":"The title contains a typo: 'For-L oop' should be 'For-Loop'.","section":"Title"},{"comment":"The formalization A = sum_ij alpha_ij L_j and A(CML) = argmin_pi Q^pi_ML introduces symbols alpha_ij, L_j, and pi without definitions, and the equation is not connected to the experiments or to the agent architecture described earlier.","section":"Section 3, equation"},{"comment":"The caption says 'adapted form Guo et al., 2024' and should be 'adapted from Guo et al., 2024', and several figure captions refer to 'snippets' that are too small or too cropped to read in the PDF.","section":"Figure 1 caption"},{"comment":"Table 1 should specify the dataset size, preprocessing steps, number of evaluation runs, and the exact evaluation protocol so that the reported accuracies can be interpreted.","section":"Table 1"}],"recommendation":"reject","confidential_remarks":"This manuscript reads as an extended abstract or primer rather than a self-contained research paper. The decisive gap is reproducibility: there is no public repository, no agent transcripts, no execution logs, and no statistical evidence that the reported results exceed chance. If the authors can later provide a full implementation with benchmark scripts and search traces, a resubmission to a more appropriate venue could be considered."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Colleague,\n\nThis is a short primer that applies an LLM multi-agent system (Theorist/Coder/Validator) to translate classical ML algorithms into quantum implementations. The genuinely new part is narrow: taking the FunSearch/AI-Scientist template and pointing it at QML, with three example targets (MLP, forward-forward, backprop). That specific combination is not in the prior literature, and the application is sensible. The paper is also honest about its own status: it calls itself a proof of concept and says in Section 2.4 that models were not trained with massive steps.\n\nThe good parts: a reasonable survey of LLM-agent and QAS work, and a clear statement of the intended search loop—generate classical code, then evolve it into a quantum counterpart. If the loop worked as described, it would be useful.\n\nThe soft spots are large. There is no evidence that iteration actually happened. The figures show cropped snippets with no repository, tests, execution logs, or search trajectory. Table 1 reports accuracies near the 10% random baseline for 10-class digits (QMLP 9.40, QFF 15.17, QBP 12.37, baseline 15.55) with no error bars, seeds, or statistical tests. QFF matching baseline is weak evidence. The abstract claims “iteratively generates and refines,” but everything shown is consistent with one-shot generation and manual curation. The Section 3 formulation (A = sum alpha_ij L_j) is notation, not a mechanism.\n\nTo the paper’s credit, the body does not overclaim; the limitations are right there in Section 2.4. The mismatch is between the abstract’s confident language and the actual evidence.\n\nWho is this for? Someone scouting directions in LLM-for-QML might find it a useful pointer, but a serious reader needs code and a real evaluation. I would not cite it in the next year, and I would not bring it to reading group. Recommendation: reject as a research preprint. If the authors release the full pipeline, logs, and benchmark results with error bars, it could become a decent workshop paper, but this version does not merit referee time.","headline":"A thin but honest proof-of-concept that never shows the claimed iteration loop in action; the new application is real but the evidence is not.","tokens_in":6146,"tokens_out":3413,"would_cite":false,"duration_ms":32876,"reading_group":"no","serious_thinker":"yes","would_accept_peer_review":false},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"AI agent loop turns classical ML algorithms into quantum code","keywords":["LLM-based multi-agent systems","quantum machine learning","forward-forward algorithm","backpropagation","quantum circuit","program search","agentic framework","proof of concept"],"falsifier":"Attempt to execute the snippets exactly as printed: if they contain undefined variables, missing imports, or no complete training loop, the proof of concept cannot be reproduced. Alternatively, re-run the 10-class digit classification with the described agentic pipeline and check whether the generated quantum forward-forward reliably reaches accuracy near 15.17% against the same baseline; failure to reproduce Table 1 would settle the claim.","tokens_in":5180,"feed_emoji":"⚛️","tokens_out":6302,"duration_ms":56706,"temperature":0.7,"pith_summary":"The paper argues that a multi-agent system built on large language models can take the name and a sketch of a classical machine learning algorithm and evolve it into a quantum implementation through an iterative loop of code generation, validation, and reward-driven optimization. It presents this as a proof of concept, reporting quantum versions of the multi-layer perceptron, the forward-forward algorithm, and backpropagation. The result that makes the claim worth caring about is that the generated quantum forward-forward algorithm reaches test accuracy close to a human-crafted quantum neural network baseline on a 10-class digit task, while the other two generated algorithms also learn but lag behind. If the conversion loop is reliable, it would give researchers an automated pipeline from the large legacy of classical machine learning concepts to candidate quantum algorithms without hand-designing circuits.","feed_headline":"AI agent loop turns classical ML algorithms into quantum code","feed_subtitle":"In a proof of concept, the generated quantum forward-forward model matched a human-written QNN baseline.","key_machinery":"The carrying mechanism is the agentic evolution loop, structured as a for-loop over classical machine learning concepts. A set of agents with distinct roles (a theorist/algorithm generator, a coder, a validator, and an investor/reward-function generator) generate, evaluate, and refine code; in the abstraction equation $A = \\sum_{ij}\\alpha_{ij} L_j$, the system $A$ is a composition of language-model lobes $L_j$ with interaction weights $\\alpha_{ij}$, and applying it to a classical algorithm $C_{ML}$ is posed as $\\mathrm{argmin}_\\pi Q^\\pi_{ML}$, a search for an optimized quantum algorithm. The loop is what distinguishes the approach from one-shot prompting: each iteration feeds feedback from validation and reward signals back into the agents, so the quantum program evolves. The search occurs in program-code space rather than directly in gate sets of parameterized quantum circuits, which the paper argues allows more expressive resulting architectures.","core_discovery":"The central claim is that a large-language-model multi-agent for-loop can perform cross-domain transformation: given a classical algorithm name, the system first generates program code of the classical algorithm as an initial condition, then runs an evolution loop of searching and optimization until it obtains a quantum counterpart. Concretely, the system produced three such counterparts: a quantum MLP, a quantum forward-forward layer in which the positive and negative passes are quantized with quantum circuits, and a quantum backpropagation in which error backpropagation is replaced by the parameter-shift rule for computing gradients. The paper's headline empirical evidence is a table comparing average test accuracy: the human-crafted baseline QNN scores 15.55%, the generated quantum forward-forward scores 15.17%, quantum backprop scores 12.37%, and quantum MLP scores 9.40%. The author reads the 15.17% versus 15.55% result as showing that the agentically generated algorithm performs similarly to the human baseline, while acknowledging that training steps were limited because the work is a proof of concept.","pith_inferences":["A stronger test than the reported accuracy table would be to compare learning curves and run the same generated code in a real quantum or high-fidelity simulator backend, since the reported accuracies are close to random chance for a 10-class task (10%).","The framework's real target may not be matching human baselines on toy tasks but discovering quantum implementations whose structure differs qualitatively from human-designed circuits; the snippets could be systematically inspected for such structural novelty.","One could test the loop's reliability by feeding it multiple classical algorithms with known correct quantum analogues and checking that generated programs are functionally equivalent, not merely similar in accuracy.","If the generated code is verified and released, the approach becomes a reproducible benchmark for agentic quantum machine learning discovery; until then, the strongest available evidence is the figure snippets, not an executable artifact."],"forward_implications":["If the loop works as claimed, any named classical deep-learning algorithm becomes a candidate input to an automated search for its quantum analogue, without manual circuit design.","The quantum forward-forward result suggests that at least one generated algorithm can be competitive with a human-crafted quantum neural network on the same task.","Because the search space is program code rather than fixed gate sets, the framework can propose architectures that a gate-level quantum architecture search might miss.","The same for-loop could screen many classical machine learning concepts, making the bottleneck the conceptual translation rather than per-algorithm human effort.","With planning mechanisms and Monte-Carlo tree search added, the framework would move from translating one algorithm to actively choosing which classical concepts to translate."],"supporting_citations":[{"why":"Is the program-search pattern the paper adapts: an LLM iteratively generates and refines code, here for quantum translations.","marker":"(Romera-Paredes et al., 2024)"},{"why":"Defines the forward-forward algorithm that the system is given as a classical concept and quantizes into positive and negative quantum passes.","marker":"(Hinton, 2022)"},{"why":"Defines backpropagation, the classical algorithm whose gradient pass the quantum version replaces with the parameter-shift rule.","marker":"(Rumelhart et al., 1986)"},{"why":"Represents quantum architecture search over parameterized circuit gate sets, the paradigm the paper contrasts with searching in program-code space.","marker":"(Du et al., 2022)"},{"why":"Provides the agentic optimize-on-a-code-template loop that the paper extends by starting from a classical algorithm and transforming across knowledge domains.","marker":"(Lu et al., 2024)"}],"fun_headline_variants":["Agentic loop turns classical ML into quantum algorithms","Quantum forward-forward matches human QNN via agentic search","Multi-agent LLM search discovers quantum ML counterparts","Automated QML: agents match manual design in proof of concept"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The whole proof of concept rests on the assumption that the code snippets shown in Figures 4a, 6, and 8 are correct, complete, runnable quantum implementations of MLP, forward-forward, and backpropagation, and that Table 1 reports actual runs of that code; the paper provides no full source, execution logs, or test harness.","fun_headline_variants_meta":{"raw":{"variants":["Agentic loop turns classical ML into quantum algorithms","Quantum forward-forward matches human QNN via agentic search","Multi-agent LLM search discovers quantum ML counterparts","Automated QML: agents match manual design in proof of concept"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000275,"raw_usage":{"total_tokens":1606,"prompt_tokens":873,"completion_tokens":733,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":489,"completion_tokens_details":{"reasoning_tokens":668}},"tokens_in":489,"tokens_out":733,"duration_ms":7587,"temperature":1.0,"reasoning_tokens":668,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-15T18:52:34.474664+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Attempt to execute the snippets exactly as printed: if they contain undefined variables, missing imports, or no complete training loop, the proof of concept cannot be reproduced. Alternatively, re-run the 10-class digit classification with the described agentic pipeline and check whether the generated quantum forward-forward reliably reaches accuracy near 15.17% against the same baseline; failure to reproduce Table 1 would settle the claim.","supporting_citations":[],"review_version":1}