mlsanityData to 14 Oct 2026

SCALAR: A Neurosymbolic Framework for Automated Conjecture and Reasoning in Quantum Circuit Analysis

arXiv 2605.10327 ↗First posted 11 May 2026quant-ph cs.AI cs.SCcited by 0

On the map: LLM reasoning → Reinforcement learning for reasoning

Abstract (arXiv)

In this paper, we present SCALAR (Symbolic Conjecture and LLM-Assisted Reasoning), a neurosymbolic framework for automated conjecture generation in quantum circuit analysis built on top of the CUDA-Q open source framework. The system integrates quantum simulation, symbolic conjecture generation, and LLM-based interpretation. We evaluate SCALAR on 82 MaxCut instances from the MQLib benchmark dataset and extend the analysis to 2,000 randomly generated graphs across four topologies: regular, Erdos-Renyi, Barabasi-Albert, and Watts-Strogatz. The framework generates conjectured bounds relating optimal QAOA parameters to graph invariants, including known relationships such as periodicity constraints on the phase separation parameter γ. SCALAR also recovers previously reported parameter transfer phenomena across structurally similar instances. Additionally, the system identifies correlations between graph structural features and optimization landscape properties, which we characterize through invariant-based descriptors. Using CUDA-Q tensor network simulator, we scale experiments to instances of up to 77 qubits. We discuss the accuracy, generality, and limitations of the generated conjectures, including sensitivity to graph class and quantum circuit depth.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Reinforcement learning for reasoning, one of 17 directions in LLM reasoning. Builds on 10 of its 32 references that are in the map. First posted on arXiv 11 May 2026.

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10 of 32 references.

References · 10

  1. 2025AlphaEvolve: A coding agent for scientific and algorithmic discoverycited by 867
  2. 2024Barren Plateaus in Variational Quantum Computingcited by 133
  3. 2024Quantum Architecture Search: A Surveycited by 32
  4. 2026Semi-Autonomous Mathematics Discovery with Gemini: A Case Study on the ErdHos Problemscited by 32
  5. 2023Mathematical conjecture generation using machine intelligencecited by 8
  6. 2024Quantum Circuit Synthesis and Compilation Optimization: Overview and Prospectscited by 8
  7. 2025QAOA-GPT: Efficient Generation of Adaptive and Regular Quantum Approximate Optimization Algorithm Circuitscited by 8
  8. 2020Classical symmetries and the Quantum Approximate Optimization Algorithmcited by 5
  9. 2025Q-Fusion: Diffusing Quantum Circuitscited by 4
  10. 2024Theoretical Approximation Ratios for Warm-Started QAOA on 3-Regular Max-Cut Instances at Depth p=1cited by 1

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  2. 2023MNISQ: A Large-Scale Quantum Circuit Dataset for Machine Learning on/for Quantum Computers in the NISQ era
  3. 2022QuEst: Graph Transformer for Quantum Circuit Reliability Estimation
  4. 2025Quantum Circuit Reasoning Models: A Variational Framework for Differentiable Logical Inference
  5. 2026Differentiable Logical Programming for Quantum Circuit Discovery and Optimization
  6. 2025Agent-Q: Fine-Tuning Large Language Models for Quantum Circuit Generation and Optimization
  7. 2025Automating quantum feature map design via large language models
  8. 2025SAQR-QC: A Logic for Scalable but Approximate Quantitative Reasoning about Quantum Circuits ↗