S Shi

Cheng-You Ho, Justin Luo, Henry Ng et al.

Dissipative cat qubits exponentially suppress one Pauli error channel with the mean photon number, leaving the conjugate bit-flip error as the dominant failure mode. This strong noise bias makes the full machinery of general quantum error correction unnecessary: a code need only protect against a single error type, and any classical linear code can be promoted to a Clifford stabilizer code that does exactly this. We use this observation to build a Clifford-only quantum Reed-Solomon (RS) code. Starting from the [7,3,5] RS code over $GF(2^{3})$ which is maximum distance separable, we expand each field symbol into three bits to obtain the [21,9,6] linear code over $GF(2)$, realized as a [[21,9, $d_{X}=6$, $d_{Z}=1$]] bit-flip code whose stabilizers are products of Z operators. Because no phase-flip correction is attempted, the construction avoids the non-Clifford quantum Fourier transform required by the Grassl-Beth quantum RS codes and is fully simulable in Stim. Errors are decoded by a lookup table of minimum-weight corrections. We then introduce a Tornado architecture: a two-layer concatenation that wraps every position of the outer RS code in an inner distance-three repetition code, yielding a [[63, 9, 18]] code decoded in two stages, a majority vote within each repetition block followed by the outer lookup table. Monte Carlo simulations show that at a physical bit-flip rate $p=0.1$ the Tornado code reaches a logical error rate $p_{L}\approx5.3\times10^{-3}$, below both parent codes, and that its logical error rate scales as $p_{L}\propto p^{6}$ at low p, in contrast to $p^{2}$ for the repetition code and $p^{3}$ for the standalone RS code. We give the exact construction, the error and circuit model, an asymptotic scaling analysis, and an account of the overhead cost and of the assumptions behind the noise model.

Bixuan Li, Yu Liu, Shuo Shi et al.

Materials science literature analysis requires simultaneous attention to composition, processing, characterization, and property relationships, yet conventional retrieval-augmented generation pipelines struggle to reconcile heterogeneous tasks within a single retrieve-then-generate architecture. Here we present AlphaAgent, a skill-driven agent framework that decouples retrieval-based question answering from paper-level report generation through explicit skill contracts. A dedicated retrieval skill rewrites user requests into material-specific search intents, queries a curated index of more than 300,000 papers from the Journal Citation Reports Metallurgy and Metallurgical Engineering category, and reformulates queries when initial evidence is insufficient. A separate report-generation skill parses full-text PDFs to produce structured per-paper analytical reports and cross-paper summaries. In a blind evaluation on 40 materials-science questions, half of which required deep analytical reasoning, AlphaAgent substantially outperformed a baseline system matched for underlying model, document index, and retrieval scale, with the largest gains in mechanistic explanation and awareness of credibility boundaries. These results indicate that explicit task separation, refined retrieval intent, and evidence-aware generation improve large-language-model-based literature analysis for materials research.