Applying LLM-guided program evolution (such as the pipeline generating the [[n,1,n-1;n-q-1]]_q EAQECC family) to optimize Clifford deformations directly for a specific decoder and circuit-level noise model will achieve at least a 20% lower logical error rate (P_L) on the surface code (d=5) under phenomenological noise compared to classical greedy search optimization.

Adversarial Debate Score

53% survival rate under critique

Supporting Research Papers

• Decoder- and Noise-Aware Optimization Framework for Clifford-Deformed Quantum Error-Correcting Codes

  Practical fault-tolerant quantum computing requires qubit-efficient quantum error correction (QEC). Clifford deformations (CDs) modify the stabilizers of a QEC code to lower its logical error rate at ...

• Researcher-in-the-Loop Evolutionary Discovery of Entanglement-Assisted Quantum Code Families

  LLM-guided program evolution can produce mathematical constructions, but validity, generality, and reproducibility require distinct evidence. We develop a researcher-in-the-loop discovery pipeline tha...

• LiftDiff: Guided Discrete Diffusion for Quantum Code Construction

  The realization of fault-tolerant quantum computation relies on Quantum Error-Correcting Codes (QECC). Of particular interest are codes that combine a high encoding rate with sufficient distance. Exis...

• Graph Neural Post-selection for Quantum Error Correction

  Post-selection improves the logical reliability of quantum computation by rejecting shots which are likely to result in logical failure. We introduce graph neural networks that predict decoder failure...

• Exact Maximum Likelihood Decoding beyond Treewidth via Rank-Decomposition Dynamic Programming

  Maximum-likelihood decoding provides an optimal decoding strategy for quantum error correction under stochastic Pauli noise. However, computing logical-class probabilities is challenging, and the lead...

Formal Verification

Source