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新论文 · v1arXiv:2611.16271

Hierarchical Decoding for Quantum Error Correction

Xulong Tang, Narges Alavisamani, Joan Camps, Matthew Cha, Elise Jennings, Jack O'Connor, Humberto Munoz Bauza, Josh Izaac 等 21 位作者

原文摘要

Fault-tolerant quantum computing requires classical decoders that can keep up with syndrome data streams and supply correction information and logical measurement outcomes to the quantum control system. Meeting these requirements at scale requires high throughput while controlling the end-to-end latency from readout to usable correction. In this paper, we organize decoding algorithms, execution strategies, and code constructions using common classifications and examine their performance and trade-offs under the reported evaluation conditions. We distinguish decoder-level hierarchy, where multiple decoding stages or resources cooperate to decode a single code, from code-level hierarchy, where the code construction itself introduces multiple decoding levels. We first review single-code hierarchical decoders and their cascaded predecoding and windowed, streaming, and distributed implementations. We then study hierarchical and concatenated codes, focusing on how decoding is coordinated across code levels through hard decisions, soft reliability information, or syndrome information. We also examine how qubit modalities shape decoding workloads and timing requirements, and review how different decoding platforms meet these requirements, including central processing units (CPUs), graphics processing units (GPUs), field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), and cryogenic platforms. By assessing design trade-offs, this paper clarifies connections across the literature and provides a shared foundation for QEC design exploration. It also identifies gaps in performance reporting and system integration to guide future research toward practical quantum error correction.

quant-ph · cs.ET

版本更新 · v5arXiv:2606.31032

Structuring license permissiveness from pairwise comparisons

Hamidah Oderinwale, David Atkinson, Rachel Hong, Art Abal, Ben Laufer

原文摘要

Licenses are legal instruments that inventors rely upon to protect the technologies they build and regulate how they are used---however, the nature of their authorship and selection implies that how they are interpreted, chosen, and enforced is largely unstructured. In practice, this makes it difficult to compare licenses at scale---when is one license considered more permissive than the other, and when are their terms incomparable to each other? Currently, there is a growing list of licenses that are introduced and used, yet no systematic way to study their relationships. This matters for platforms such as Hugging Face, GitHub, and the Python Package Index, where developers publish or build upon technologies that each have their own licenses. Using large language models (LLMs), we introduce methods for comparing licenses at scale: first, in a pairwise fashion to construct and validate a partial ordering based on permissiveness; and by drawing on existing taxonomies of software licenses. Then, we try to recover the structure with the Bradley-Terry model to see if permissiveness can be judged more cheaply and observe a loss of $\sim$20\%---and classify this loss to feature coverage. The former coupled with model rationale allows us to trace restrictiveness, and the latter allows us to understand license selection as a combination of shared provisions.

cs.SE · cs.CY