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Ju-Bong Kim

Publications and source records attributed to Ju-Bong Kim.

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Syndrome-as-Header: A Quantum Label-Switching Architecture via Uncorrectable Error Injection

Scaling quantum networks beyond point-to-point links requires packet forwarding that can tolerate control-plane timing uncertainty. Existing optical-burst switching (OBS) and quantum-wrapper-based packet switching (QW) rely on external classical headers whose jitter-prone processing must remain aligned with in-flight quantum payloads, creating guard-window or fiber-delay-line budget failures. This paper proposes Syndrome-as-Header (SAH), a quantum label-switching architecture that embeds routing labels into the syndrome structure of an encoded payload. The proposed scheme uses Uncorrectable Error Injection (UEI) to map flow labels to reference syndromes and builds a syndrome-space header codebook whose decoding regions remain distinguishable under correctable channel-induced syndrome deviations. Core routers extract the syndrome header, suppress the correctable residual channel-error component, and swap labels without measuring or decoding the logical payload. SAH supports FAST forwarding and VERIFICATION mode, the latter providing end-to-end consistency checking while retaining swappable labels. In NSFNet timing benchmarks with common per-hop quantum error correction (QEC), SAH eliminates the external-header/payload alignment condition, so timing uncertainty appears as memory residence and delivered latency rather than alignment-budget drops. With a $T_2$-based memory-residence penalty, the advantage depends on router memory coherence; for sufficiently long coherence, SAH maintains higher acceptance and throughput than OBS+QEC and QW+QEC under high jitter.

quant-ph

Strangeness-driven Exploration in Multi-Agent Reinforcement Learning

Efficient exploration strategy is one of essential issues in cooperative multi-agent reinforcement learning (MARL) algorithms requiring complex coordination. In this study, we introduce a new exploration method with the strangeness that can be easily incorporated into any centralized training and decentralized execution (CTDE)-based MARL algorithms. The strangeness refers to the degree of unfamiliarity of the observations that an agent visits. In order to give the observation strangeness a global perspective, it is also augmented with the the degree of unfamiliarity of the visited entire state. The exploration bonus is obtained from the strangeness and the proposed exploration method is not much affected by stochastic transitions commonly observed in MARL tasks. To prevent a high exploration bonus from making the MARL training insensitive to extrinsic rewards, we also propose a separate action-value function trained by both extrinsic reward and exploration bonus, on which a behavioral policy to generate transitions is designed based. It makes the CTDE-based MARL algorithms more stable when they are used with an exploration method. Through a comparative evaluation in didactic examples and the StarCraft Multi-Agent Challenge, we show that the proposed exploration method achieves significant performance improvement in the CTDE-based MARL algorithms.

cs.LG