mlsanityData to 7 Oct 2026

Position: Quantum Program Generation Must Prioritize Validity Over Probabilistic Scaling

arXiv 2607.15313 ↗First posted 15 Jul 2026cs.LGcited by 0

On the map: LLM reasoning → Reinforcement learning for reasoning

Abstract (arXiv)

The scaling hypothesis assumes that increasing model parameters yields emergent reasoning capabilities. This position paper argues that applying this probabilistic paradigm to generic quantum circuit synthesis is a directional error. Unlike natural languages, quantum circuits require strict adherence to mathematical constraints that manifest a significant syntax-semantics gap. Training on unverified quantum programs means that models learn syntax but fail to capture the physical semantics of the Hilbert space. Since the valid subset of circuit designs decays exponentially with the number of qubits, post-hoc filtering is mathematically intractable. We propose a pivot from human-centric copilots to verifier-centric agents. We integrate hierarchical constraints, topological masks, and symbolic proxies directly into generation. Our analysis suggests that scale alone cannot bridge the validity gap. Verification-aware architectures offer a viable path for modular quantum program generation. These considerations point toward generation methods that encode task-specific rules of quantum information, rather than relying on imitation alone.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Reinforcement learning for reasoning, one of 17 directions in LLM reasoning. Builds on 32 of its 64 references that are in the map. First posted on arXiv 15 Jul 2026.

Builds on

32 of 64 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023Let's Verify Step by Step31 May

Other references · 31

  1. 2021Evaluating Large Language Models Trained on Codecited by 8,053
  2. 2020Scaling Laws for Neural Language Modelscited by 6,411
  3. 2021Program Synthesis with Large Language Modelscited by 3,509
  4. 2022Training Compute-Optimal Large Language Modelscited by 2,927
  5. 2022Emergent Abilities of Large Language Modelscited by 2,483
  6. 2022Competition-Level Code Generation with AlphaCodecited by 1,427
  7. 2018Barren plateaus in quantum neural network training landscapescited by 624
  8. 2023Faith and Fate: Limits of Transformers on Compositionalitycited by 496
  9. 2021PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Modelscited by 402
  10. 2022Grounded Copilot: How Programmers Interact with Code-Generating Modelscited by 216
  11. 2023Inverse Scaling: When Bigger Isn't Bettercited by 205
  12. 2024LLMs Can't Plan, But Can Help Planning in LLM-Modulo Frameworkscited by 178
19 more
  1. 2021Constrained Language Models Yield Few-Shot Semantic Parserscited by 162
  2. 2020A Closer Look at Invalid Action Masking in Policy Gradient Algorithmscited by 160
  3. 2023A Large-Scale Survey on the Usability of AI Programming Assistants: Successes and Challengescited by 75
  4. 2023Large Language Models for Compiler Optimizationcited by 52
  5. 2025Inverse Scaling in Test-Time Computecited by 49
  6. 2024Quantum Circuit Optimization with AlphaTensorcited by 36
  7. 2025APOLLO: Automated LLM and Lean Collaboration for Advanced Formal Reasoningcited by 34
  8. 2023The Larger They Are, the Harder They Fail: Language Models do not Recognize Identifier Swaps in Pythoncited by 29
  9. 2024Qiskit HumanEval: An Evaluation Benchmark For Quantum Code Generative Modelscited by 29
  10. 2024Qiskit Code Assistant: Training LLMs for generating Quantum Computing Codecited by 24
  11. 2022Assessing requirements to scale to practical quantum advantagecited by 10
  12. 2024Testing and Debugging Quantum Programs: The Road to 2030cited by 5
  13. 2018Don't Panic! Better, Fewer, Syntax Errors for LR Parserscited by 4
  14. 2023Algorithmic Theory of Qubit Routingcited by 4
  15. 2024An Evaluation Benchmark for Autoformalization in Lean4cited by 4
  16. 2022Succinct Classical Verification of Quantum Computationcited by 1
  17. 2024SAIS: A Novel Bio-Inspired Artificial Immune System Based on Symbiotic Paradigmcited by 1
  18. 2024Multi-qubit Lattice Surgery Schedulingcited by 1
  19. 2025Beyond Natural Language Perplexity: Detecting Dead Code Poisoning in Code Generation Datasetscited by 1

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2026Generative AI for Quantum Circuits and Quantum Code: A Technical Review and Taxonomy
  2. 2025Agent-Q: Fine-Tuning Large Language Models for Quantum Circuit Generation and Optimization
  3. 2025QUASAR: Quantum Assembly Code Generation Using Tool-Augmented LLMs via Agentic RL
  4. 2025QHackBench: Benchmarking Large Language Models for Quantum Code Generation Using PennyLane Hackathon Challenges
  5. 2026Aligning Quantum Operators with Large Language Models
  6. 2025Enhancing LLM-based Quantum Code Generation with Multi-Agent Optimization and Quantum Error Correction
  7. 2026Quantum Circuit Vision: Cost-Aware Evaluation of Visual AI Agents for Quantum Code Generation
  8. 2026PennySynth: RAG-Driven Data Synthesis for Automated Quantum Code Generation