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NTT Corporation

Industry researchasia · jp
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Representative Papers

Q3DE: A fault-tolerant quantum computer architecture for multi-bit burst errors by cosmic rays

Oct 01, 2022Micro

Multi-bit burst errors (MBBEs) induced by cosmic rays severely compromise the scalability of fault-tolerant quantum computing. Method: This paper proposes Q3DE, a low-overhead fault-tolerance enhancement architecture built within the surface code framework. Its core innovation is the first syndrome-based, transparent MBBE detection mechanism, integrated with dynamic logical encoding reconstruction and rollback-aware decoding—enabling real-time anomaly identification, decoding rollback, and recovery operation re-evaluation without hardware redundancy. Contribution/Results: By jointly optimizing dynamic code deformation and decoding, Q3DE reduces MBBE duration by 1000× and shrinks the affected qubit region by 50%, substantially alleviating stringent constraints on physical qubit density and chip footprint. This establishes a new paradigm for designing highly reliable, large-scale quantum processors.

15 citations3 influentialRead paper

NeoAMT: Neologism-Aware Agentic Machine Translation with Reinforcement Learning

Jan 07, 2026arXiv.org

This work addresses the long-overlooked challenge of translating neologisms in machine translation by proposing an intelligent agent framework that integrates Wiktionary retrieval with reinforcement learning. The framework introduces a translation-difficulty-aware adaptive rollout generation mechanism and designs a novel reward function grounded in external knowledge. To support systematic evaluation, we construct the first large-scale neologism translation dataset spanning 16 languages and 75 translation directions. Experimental results demonstrate that the proposed approach significantly improves translation accuracy for neologisms across diverse language pairs, effectively validating the framework’s efficacy and generalization capability in handling complex, emerging lexical phenomena.

1 citationsRead paper

Spectral Truncation Kernels: Noncommutativity in C*-algebraic Kernel Machines

May 28, 2024arXiv.org

This paper addresses the dual challenges of kernel selection difficulty and high computational cost in vector-valued reproducing kernel Hilbert space (RKHS) learning. Methodologically, it introduces a novel class of spectral-truncation-based $C^*$-algebra-valued kernels, the first to explicitly incorporate multiplicative noncommutativity of the output space into kernel design—thereby overcoming the modeling limitations of conventional separable or commutative kernels. Positive definiteness is ensured via spectral truncation, and a depth-wise extension framework is established. Theoretically, the proposed kernel class achieves a superior trade-off between representation capacity and computational complexity. Empirically, it demonstrates significant improvements in generalization performance on multi-output regression and functional learning tasks, while substantially reducing computational overhead for large-scale vector-valued kernel operations.

1 citationsRead paper
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