arxiv: 2604.08570 · v2 · submitted 2026-03-25 · 💻 cs.LG · cs.AI· cs.PL· cs.SE· quant-ph

Recognition: no theorem link

QuanBench+: A Unified Multi-Framework Benchmark for LLM-Based Quantum Code Generation

Ali Slim, Ammar Mohanna, Bernard Ghanem, Hasan Abed Al Kader Hammoud, Haydar Hamieh, Jawad Kotaich, Mahdi Chehimi, Yehya Ghosn

Authors on Pith no claims yet

Pith reviewed 2026-05-14 23:55 UTC · model grok-4.3

classification 💻 cs.LG cs.AIcs.PLcs.SEquant-ph

keywords LLMquantum code generationbenchmarkQiskitPennyLaneCirqcode generationfeedback repair

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The pith

A new benchmark finds LLMs generate quantum code with up to 59.5% success in Qiskit but lower in other frameworks.

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

QuanBench+ introduces a unified set of 42 tasks to benchmark LLMs on quantum code generation in Qiskit, PennyLane, and Cirq. The evaluation uses executable functional tests, Pass@1 and Pass@5 metrics, and KL-divergence for acceptance of probabilistic outputs. One-shot results show the best models reaching 59.5% in Qiskit, 54.8% in Cirq, and 42.9% in PennyLane. With feedback-based repair after errors, scores improve to 83.3%, 76.2%, and 66.7%. This establishes that while progress is evident, reliable cross-framework quantum code generation is not yet achieved and relies heavily on framework-specific knowledge.

Core claim

The paper establishes QuanBench+ as a benchmark that aligns 42 tasks across three major quantum frameworks to measure LLM performance in generating executable quantum code, reporting that top one-shot accuracies range from 42.9% to 59.5% and improve markedly with repair feedback, thereby showing that multi-framework reliability remains an open challenge.

What carries the argument

The QuanBench+ benchmark, which provides 42 aligned tasks covering quantum algorithms, gate decomposition, and state preparation, evaluated via functional tests and KL-divergence across Qiskit, PennyLane, and Cirq.

If this is right

Models exhibit different strengths depending on the quantum framework used.
Feedback repair is an effective way to boost performance on quantum coding tasks.
Current LLMs still require framework-specific knowledge for high accuracy in quantum code generation.
Pass rates can be measured consistently using executable tests to allow fair comparison.
The benchmark highlights the need for better generalization in quantum-aware LLMs.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Developers of LLMs for quantum computing should prioritize training on multiple frameworks to improve cross-compatibility.
Future work could test whether larger models close the performance gaps between frameworks.
Integrating the benchmark with automated verification tools might further improve repair success.
The dependency on framework knowledge suggests that abstract quantum circuit representations could help LLMs.

Load-bearing premise

The 42 aligned tasks successfully isolate quantum reasoning ability from framework familiarity, and executable tests plus KL-divergence fully capture correctness for quantum code.

What would settle it

Observing that performance scores become similar across frameworks when models are tested without prior exposure to framework documentation or examples.

Figures

Figures reproduced from arXiv: 2604.08570 by Ali Slim, Ammar Mohanna, Bernard Ghanem, Hasan Abed Al Kader Hammoud, Haydar Hamieh, Jawad Kotaich, Mahdi Chehimi, Yehya Ghosn.

**Figure 1.** Figure 1: The benchmark holds task intent and execution conditions fixed across frameworks. Our workflow standardizes prompts, grading, and runtime settings before comparing models on Qiskit, PennyLane, and Cirq. 4.2 TASK SET AND CATEGORIES QuanBench+ is derived from the original QuanBench task set Guo et al. (2025). We retain tasks that admit clear numerical or functional correctness criteria and adapt them to Qisk… view at source ↗

**Figure 2.** Figure 2: provides the main one-shot ranking, while Appendix [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗

**Figure 3.** Figure 3: Feedback repair lifts accuracy across all three frameworks. The gains are broad rather than model-specific, but no framework becomes fully reliable after repair. We evaluate 42 tasks spanning quantum algorithms, state preparation, and gate decomposition; the task-count breakdown appears in Appendix D. 7 DISCUSSION The main result is not simply that newer models score higher; it is that difficulty remains s… view at source ↗

**Figure 4.** Figure 4: The null KL distribution supports the global acceptance threshold. The pooled canonical-repeat ECDF places the 99.7th percentile at 0.048, motivating the paper-wide threshold τ = 0.05. D TASK CATEGORIES AND EXAMPLES [PITH_FULL_IMAGE:figures/full_fig_p012_4.png] view at source ↗

**Figure 5.** Figure 5: Multiple samples recover additional Qiskit solutions. The gap between Pass@1 and Pass@5 identifies tasks where one-shot decoding leaves recoverable performance on the table. 13 [PITH_FULL_IMAGE:figures/full_fig_p013_5.png] view at source ↗

**Figure 6.** Figure 6: Cirq also benefits meaningfully from multi-sample generation. The gains are especially visible among the middle of the model ranking [PITH_FULL_IMAGE:figures/full_fig_p014_6.png] view at source ↗

**Figure 7.** Figure 7: PennyLane retains large recoverable gaps for weaker models. Multi-sample decoding helps, but it does not close the framework-level difficulty gap. 14 [PITH_FULL_IMAGE:figures/full_fig_p014_7.png] view at source ↗

**Figure 8.** Figure 8: One-shot success in Qiskit is concentrated in a broad but incomplete task band. Each row corresponds to a model and each column to a task [PITH_FULL_IMAGE:figures/full_fig_p015_8.png] view at source ↗

**Figure 9.** Figure 9: PennyLane exposes a noticeably sparser one-shot success map. Each row corresponds to a model and each column to a task. 15 [PITH_FULL_IMAGE:figures/full_fig_p015_9.png] view at source ↗

**Figure 10.** Figure 10: Cirq sits between Qiskit and PennyLane in first-attempt density. The overall pattern is stronger than PennyLane but less complete than Qiskit. PASS@5 HEATMAPS What to look for: Compared with the Pass@1 maps, these heatmaps reveal how much additional coverage appears once models are allowed multiple tries. New dark regions indicate tasks where the capability exists but is unstable under one-shot decoding … view at source ↗

**Figure 11.** Figure 11: Pass@5 broadens Qiskit coverage substantially. Multi-sample decoding turns many partial one-shot failures into recoverable successes. 16 [PITH_FULL_IMAGE:figures/full_fig_p016_11.png] view at source ↗

**Figure 12.** Figure 12: Pass@5 helps in PennyLane, but hard tasks remain visibly persistent. Multi-sample decoding broadens coverage without removing the framework gap [PITH_FULL_IMAGE:figures/full_fig_p017_12.png] view at source ↗

**Figure 13.** Figure 13: Cirq gains a wider solvable region under Pass@5. The additional coverage confirms that many one-shot failures are unstable rather than absolute. 17 [PITH_FULL_IMAGE:figures/full_fig_p017_13.png] view at source ↗

**Figure 14.** Figure 14: Prefill helps most when PennyLane boilerplate is easy to miss. The ranking changes confirm that setup friction still matters for several mid-tier models. 18 [PITH_FULL_IMAGE:figures/full_fig_p018_14.png] view at source ↗

**Figure 15.** Figure 15: Cirq also shows meaningful sensitivity to prompt scaffolding. Prefill changes both average accuracy and several mid-tier rankings [PITH_FULL_IMAGE:figures/full_fig_p019_15.png] view at source ↗

**Figure 16.** Figure 16: Qiskit benefits from prefill, but less uniformly than weaker frameworks. The effect is real, though not consistent across the full model range. H ERROR DISTRIBUTIONS This section examines what goes wrong when first-attempt solutions fail. The goal is to separate semantic mistakes from implementation and framework-use errors. Observation [PITH_FULL_IMAGE:figures/full_fig_p019_16.png] view at source ↗

**Figure 17.** Figure 17: Most first-attempt failures are semantic, not syntactic. Wrong answers and logic errors dominate the Pass@1 error budget across frameworks. they are secondary, including missing methods/gates (11.8%), shape mismatches (8.0%), syntax errors (4.7%), and qubit specification errors (3.9%). This split helps explain why feedback can recover many first-attempt failures without eliminating the deeper reasoning ga… view at source ↗

**Figure 18.** Figure 18: Feedback densifies the Qiskit success map. Stronger models in particular convert many previously sparse regions into solved tasks [PITH_FULL_IMAGE:figures/full_fig_p021_18.png] view at source ↗

**Figure 19.** Figure 19: Feedback improves PennyLane coverage, but the map remains visibly harder. The gains are substantial without fully closing the framework gap. 21 [PITH_FULL_IMAGE:figures/full_fig_p021_19.png] view at source ↗

**Figure 20.** Figure 20: Feedback broadens Cirq success across much of the ranking. The densification is clear, especially among stronger and mid-tier models. Observations: (i) Performance increases monotonically with additional feedback attempts, which confirms that iterative repair generally improves functional correctness. (ii) Most gains arrive early (attempts 1→2), followed by diminishing returns after roughly three attempts… view at source ↗

**Figure 21.** Figure 21: Most Qiskit feedback gains arrive early. The curves rise quickly in the first repair rounds and then flatten. 22 [PITH_FULL_IMAGE:figures/full_fig_p022_21.png] view at source ↗

**Figure 22.** Figure 22: PennyLane improves steadily, but not indefinitely, with additional repair attempts. Most of the lift still arrives in the early rounds [PITH_FULL_IMAGE:figures/full_fig_p023_22.png] view at source ↗

**Figure 23.** Figure 23: Cirq follows the same early-gain, late-plateau pattern. Additional repair attempts help most in the first few rounds. 23 [PITH_FULL_IMAGE:figures/full_fig_p023_23.png] view at source ↗

**Figure 24.** Figure 24: Feedback compresses the spread between models, but does not erase it. Aggregate success rates after up to 5 repair attempts across all frameworks [PITH_FULL_IMAGE:figures/full_fig_p024_24.png] view at source ↗

**Figure 25.** Figure 25: After repair, the remaining failures are mostly semantic. Residual post-feedback errors become more concentrated in deeper reasoning mistakes. Observations: After the feedback loop, [PITH_FULL_IMAGE:figures/full_fig_p024_25.png] view at source ↗

read the original abstract

Large Language Models (LLMs) are increasingly used for code generation, yet quantum code generation is still evaluated mostly within single frameworks, making it difficult to separate quantum reasoning from framework familiarity. We introduce QuanBench+, a unified benchmark spanning Qiskit, PennyLane, and Cirq, with 42 aligned tasks covering quantum algorithms, gate decomposition, and state preparation. We evaluate models with executable functional tests, report Pass@1 and Pass@5, and use KL-divergence-based acceptance for probabilistic outputs. We additionally study Pass@1 after feedback-based repair, where a model may revise code after a runtime error or wrong answer. Across frameworks, the strongest one-shot scores reach 59.5% in Qiskit, 54.8% in Cirq, and 42.9% in PennyLane; with feedback-based repair, the best scores rise to 83.3%, 76.2%, and 66.7%, respectively. These results show clear progress, but also that reliable multi-framework quantum code generation remains unsolved and still depends strongly on framework-specific knowledge.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

QuanBench+ gives the first aligned multi-framework benchmark for quantum code gen with usable numbers, but the claim that gaps prove framework-specific knowledge dependence rests on unshown task equivalence.

read the letter

QuanBench+ lines up 42 tasks across Qiskit, PennyLane, and Cirq so the same quantum problems can be tested in each framework. That is the concrete advance: prior work stayed inside one framework, so this is the first place to see direct comparisons on aligned problems using executable tests and KL-divergence acceptance. The reported Pass@1 numbers (59.5 % Qiskit, 54.8 % Cirq, 42.9 % PennyLane) and the lift after runtime repair (up to 83.3 %, 76.2 %, 66.7 %) are the usable outputs. The paper also shows that repair helps across the board, which is a practical observation for anyone building these systems. Credit for shipping a benchmark that people can actually run and extend. The central claim that performance still depends strongly on framework-specific knowledge follows from the ordering, but that interpretation needs the tasks to hold quantum content fixed while only changing syntax. No details appear on how task difficulty was matched for API verbosity, gate-set differences, or how often each framework's idioms show up in pre-training data. Without those controls the ordering could track corpus imbalance instead. The KL-divergence and runtime feedback steps also tie correctness measurement to framework execution, which adds another layer that is not purely quantum. If the full text has extra validation steps for alignment I missed them in the available description. This is for groups working on LLM code generation for quantum software or anyone who needs a starting point for multi-framework evaluation. A reader who wants standardized tasks and baseline numbers will get value even if they later tighten the controls. It is coherent on its own terms and deserves a serious referee to check the task construction and any hidden equivalence checks.

Referee Report

3 major / 1 minor

Summary. The manuscript introduces QuanBench+, a unified benchmark for LLM-based quantum code generation spanning Qiskit, PennyLane, and Cirq with 42 aligned tasks covering quantum algorithms, gate decomposition, and state preparation. It evaluates models using executable functional tests, reports Pass@1 and Pass@5 scores, applies KL-divergence-based acceptance for probabilistic outputs, and studies Pass@1 after feedback-based repair. The strongest one-shot scores are 59.5% (Qiskit), 54.8% (Cirq), and 42.9% (PennyLane), rising to 83.3%, 76.2%, and 66.7% after repair. The authors conclude that reliable multi-framework quantum code generation remains unsolved and depends strongly on framework-specific knowledge.

Significance. If the 42 aligned tasks successfully isolate quantum reasoning from framework familiarity, this work supplies a valuable standardized tool for evaluating LLMs in quantum programming. The multi-framework design, executable functional tests, and inclusion of a feedback-based repair loop are concrete strengths that could inform future model development. The reported performance gaps and repair gains highlight persistent challenges in cross-framework generalization.

major comments (3)

[Abstract] Abstract and benchmark description: The central claim that performance gaps demonstrate dependence on framework-specific knowledge assumes the 42 aligned tasks hold quantum content fixed while varying only syntax. No evidence is provided that task difficulty was matched for API verbosity, gate-set expressiveness, or frequency of each framework's idioms in pre-training corpora. Without such controls, the observed ordering (Qiskit 59.5% > Cirq 54.8% > PennyLane 42.9%) could track corpus imbalance rather than isolated quantum reasoning.
[Abstract] Abstract and evaluation section: Concrete Pass@1 and repair scores are stated, yet no details are supplied on task construction, model selection, statistical tests, or data exclusion rules. This absence makes the central performance claims difficult to verify or reproduce from the available text.
[Evaluation Methodology] KL-divergence and repair loop: The KL-divergence acceptance criterion together with framework-specific runtime feedback in the repair loop couples the correctness metric to framework details, which risks confounding the intended isolation of quantum reasoning ability from framework familiarity.

minor comments (1)

[Results] Results tables or figures would benefit from explicit confidence intervals or p-values when comparing Pass@1 scores across frameworks to support the reported ordering.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below and have revised the manuscript to improve methodological transparency, discuss potential confounds, and clarify the scope of our claims.

read point-by-point responses

Referee: [Abstract] The central claim that performance gaps demonstrate dependence on framework-specific knowledge assumes the 42 aligned tasks hold quantum content fixed while varying only syntax. No evidence is provided that task difficulty was matched for API verbosity, gate-set expressiveness, or frequency of each framework's idioms in pre-training corpora. The observed ordering could track corpus imbalance rather than isolated quantum reasoning.

Authors: We agree that explicit controls for verbosity, expressiveness, and corpus frequency would strengthen the isolation claim. Tasks were constructed by mapping equivalent quantum functionality (e.g., the same algorithm or state preparation expressed via each framework's native APIs and gate sets), but we did not quantify pre-training frequency or verbosity metrics. In the revision we added a dedicated limitations subsection with examples of alignment, corpus-bias discussion, and an acknowledgment that the Qiskit > Cirq > PennyLane ordering may partly reflect training-data imbalance. We retain the claim that framework-specific knowledge contributes because the gap persists across multiple models and task categories. revision: partial
Referee: [Abstract] Concrete Pass@1 and repair scores are stated, yet no details are supplied on task construction, model selection, statistical tests, or data exclusion rules. This absence makes the central performance claims difficult to verify or reproduce from the available text.

Authors: The full manuscript already contains these details in Section 3 (task construction via aligned quantum primitives) and Section 4 (model selection, Pass@k definition, and execution environment). To address the referee's concern we have expanded the abstract with a one-sentence methodological summary, added explicit statistical significance tests (paired t-tests on Pass@1 across models), and inserted a data-exclusion protocol (e.g., discarding tasks with non-deterministic outputs beyond KL tolerance) into the evaluation section. revision: yes
Referee: [Evaluation Methodology] KL-divergence and repair loop: The KL-divergence acceptance criterion together with framework-specific runtime feedback in the repair loop couples the correctness metric to framework details, which risks confounding the intended isolation of quantum reasoning ability from framework familiarity.

Authors: We acknowledge the coupling. KL-divergence is used only for probabilistic outputs to provide a syntax-agnostic acceptance threshold; the repair loop necessarily uses framework-specific runtime errors because the benchmark evaluates executable code generation. The manuscript's goal is practical multi-framework performance rather than pure reasoning isolation. In revision we clarified this scope, added an ablation that reports one-shot Pass@1 without the repair loop, and noted that any future benchmark seeking stricter isolation would need synthetic framework-agnostic interfaces. revision: partial

Circularity Check

0 steps flagged

No circularity: benchmark applies new tasks and standard metrics to existing models

full rationale

The paper introduces 42 aligned tasks across Qiskit, PennyLane, and Cirq, then directly measures Pass@1, Pass@5, and KL-divergence on off-the-shelf LLMs using executable functional tests. No equations, fitted parameters, or predictions are defined in terms of the reported outcomes. No self-citations are invoked as load-bearing uniqueness theorems, and no ansatz or renaming reduces the evaluation results to inputs by construction. The central claims are empirical measurements, not derivations that collapse to the benchmark definition itself.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The abstract relies on standard LLM code-generation evaluation practices; no free parameters, invented entities, or non-standard axioms are stated.

axioms (1)

domain assumption Executable functional tests and Pass@k metrics measure quantum code correctness
Common assumption in code-generation benchmarks but not proven to separate quantum reasoning from framework knowledge.

pith-pipeline@v0.9.0 · 5536 in / 1206 out tokens · 44612 ms · 2026-05-14T23:55:47.549478+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

4 extracted references · 4 canonical work pages

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