MQSS-Selector: RL-Guided Pass Selection for an MLIR Compilation Pipeline
Andre Youssefi, Ercüment Kaya, Minh Chung, Jorge Echavarria, Laura B. Schulz, Martin Schulz
quant-ph · cs.DC · cs.LG · cs.PL
Quick verdict
Promising RL pass-selection for MLIR quantum compilation (DAEP works; device ML solid), but unified scheduler unimplemented and baselines missing.
Summary
The paper proposes MQSS-Selector, a learning-based component for the Munich Quantum Software Stack addressing two coupled decisions in NISQ program execution: choosing a quantum device and ordering compilation passes. The authors formalize the joint problem via a Partition-style reduction (Theorem 1) and build two components. A supervised device-selection module (MLP, random forest, SVM, KNN predicting Hellinger fidelity) is trained on 128 MQT-Bench programs run on twelve IBM mock backends and 2,880 randomized-benchmarking programs on two real LRZ devices, reaching R-squared up to about 0.99. An actor-critic RL pass selector operates over 92 MLIR/Quake passes with a potential-based reward and a guidance strategy called Dual-Annealed Exploration Priming (DAEP). On 1,548 molecular-Hamiltonian circuits, vanilla RL produces only 42.1% executable programs, while DAEP reaches 95.5% executability and correct termination in 153/154 cases, though average depth and operation counts still increase. The combined unified scheduler remains conceptual, and multi-objective claims (queue latency, compilation time) are untested. Main limitations: no baselines (random policy, heuristic sequences, MQT Predictor/Qiskit), undisclosed hyperparameters, single-run training without variance estimates, and circuit metrics that worsen on average under the learned policy. Code is anonymously available.
Review complete
Assessment at a glance
6
risk flags identified
● high 1
● medium 4
● low 1
5 strengths · 5 weaknesses · 5 author questions
AI text signal = Binoculars zero-shot detector score over 20 sampled passages (Fast-DetectGPT co-reported) — absence does not prove human authorship, and a flag does not imply misconduct.
Levels reflect verification complexity — not a quality score. Evolve never rates your paper.
Key claims under review
- Joint device selection and phase ordering form an NP-hard combinatorial problem (Theorem 1), motivating a unified learning-based selector.
- Supervised models predict per-device Hellinger fidelity accurately (R-squared up to ~0.99), with random forest the most robust candidate on the MQT-Bench set.
- Vanilla actor-critic RL cannot discover successful compilations by exploration alone (42.1% executable), whereas the DAEP guidance strategy achieves 95.5% executability and reliable termination.
- MLIR-native pass selection is feasible via an extended 92-pass Quake-dialect suite, validated on molecular-Hamiltonian circuits across two devices with different mappings and native gate sets.
Strengths
First substantial RL pass selection on MLIR/Quake
Prior RL phase-ordering work targets Qiskit (TuniQ [23]) or LLVM (AutoPhase [17]); this paper demonstrates data-driven selection over a 92-pass CUDA-Q/Quake suite with a pybind11 boundary separating Python policies from C++ passes (Section 4.2), addressing a real toolchain gap.
Honest negative control quantified
Table 1 contrasts vanilla actor-critic RL (42.1% executable programs, 0/154 Finish calls, avg reward 0.44) against DAEP (95.5% executable, 153/154 Finish, avg reward 4.32), and Section 6 states plainly that plain RL 'is not a viable approach' under their constraints.
Principled reward formalization
Device selection evaluated on simulated and real hardware
Code and public data lineage
Weaknesses
No baselines for the headline pass-selection result
Learned policy degrades circuits on average
Theorem 1 is a proof sketch
No variance reporting or hyperparameter disclosure
Claim/evaluation mismatch
Full Review Report
$1.99 · one-timeUnlock the complete preliminary analysis Evolve produced for this paper.
Report preview
Scope & Claims
MQSS-Selector (arXiv:2609.30104) targets two coupled decisions in NISQ program execution on the Munich Quantum Software Stack: quantum device selection and compiler pass (phase) ordering, framed on the MLIR/Quake (CUDA-Q) toolchain rather than Qiskit. Theorem 1 argues the joint problem is NP-hard via a Partition-style reduction, motivating a learning-based unified selector. Four contributions are claimed: (1) MLIR-native RL pass selection, (2) a training strategy called Dual-Annealed Exploration Priming (DAEP), (3) actor/critic evaluation metrics independent of other compilers, and (4) an expanded 92-pass MLIR suite. Notably, the abstract's 'unified selector' is aspirational: Section 4 admits the two components 'currently ... are planed [sic] to be integrated', and Fig. 2 shows only conceptual V1 (hybrid supervised+RL) and V2 (fully RL) designs.
The remaining sections cover Methodology Assessment, Statistical Evidence, Results & Reproducibility, Limitations & Risks, and Verdict & Recommendations.
- All 5 strengths & 5 weaknesses, analyzed in detail
- 6 risk flags with severity, section & reviewer questions
- Methods & statistical evidence assessment
- 5 questions a peer reviewer would ask
- Full six-dimension evaluation report & recommendations
One-time purchase · this paper only
One-time purchase, this paper only. AI-generated preliminary analysis — not peer review.
Frequently asked questions
What is MQSS-Selector?
MQSS-Selector is a machine-learning component of the Munich Quantum Software Stack that chooses the target quantum device and the order of compiler passes for a quantum program. It combines supervised models for device choice with a reinforcement-learning actor-critic agent for pass sequencing, built on the MLIR/Quake dialect of NVIDIA CUDA-Q rather than Qiskit.
What is DAEP (Dual-Annealed Exploration Priming)?
A training technique for the RL agent: during training rollouts, the agent's chosen action is periodically overwritten by a fixed guidance policy with a probability that decays over time, and a behavior-cloning-like loss with a decaying weight pulls the agent toward those guided actions. This counters the near-zero chance that random exploration finds a successful compilation. In the paper's tests it raised the share of executable compiled programs from 42.1% to 95.5%.
Does MQSS-Selector outperform Qiskit or MQT Predictor?
No head-to-head comparison is reported. The pass selector targets the MLIR/Quake dialect of CUDA-Q rather than Qiskit, and the authors argue comparisons are only meaningful with matched pass sets. The combined device-plus-pass 'unified scheduler' is described conceptually (two design sketches) but not implemented or benchmarked in this paper.
Is the code and data available?
Yes — the paper links two anonymous repositories (the pass selector and an extended MQSS MLIR pass suite). Device-selection data derive from public MQT-Bench programs run on simulated IBM fake backends, plus locally executed randomized-benchmarking programs on two real devices at the Leibniz Supercomputing Centre.
About this review
This is an AI-generated preliminary review by Evolve.Pub, produced from the openly available text of the paper. It is not peer review and does not represent the authors' views. The canonical record of the paper is at the publisher link above.
- Figures 1-3 are diagrams not analyzable from extracted text; Fig. 4-5 heatmap values were embedded as text and their row/column-to-model/backend mapping was inferred from the listing order.
- Training hyperparameters and decay schedules do not appear in the extracted text; they may live in the linked repositories rather than the paper.
- The two anonymous code repositories could not be inspected for this review.
- The abstract's scheduling/queue objectives lie outside the evaluated components; this review covers only what the text actually evaluates.
Go deeper on this paper
Wrote a paper? Stress-test it before reviewers do.
Upload your manuscript for the same structured scrutiny — reasoning, statistics, evidence, figures, and reproducibility.