arXiv subjects

Rasman Mubtasim Swargo

Publications and source records attributed to Rasman Mubtasim Swargo.

5 recordsLinked to original sources

arXiv2026

Scalable Quantum Key Distribution via GHZ Entanglement and Qubit Reuse

Conventional Quantum Key Distribution (QKD) requires the transmission of qubits proportional to or exceeding the length of the key, as protocols such as BB84 transmit more qubits than the final key size due to basis sifting and privacy amplification. Since quantum networks are still in their infancy and have limited capacity, this overhead puts significant pressure on network resources. To address this issue, we propose a Multi-Qubit Greenberger--Horne--Zeilinger (GHZ) State-based QKD scheme that reduces the number of qubits transmitted over the quantum channel. The proposed method transmits one GHZ qubit between endpoints and reuses the resulting entanglement to convey multiple classical key bits with the help of Quantum Non-Demolition (QND) measurements. Under the stated assumptions on authenticated classical communication, local reset verification, and bounded-error QND discrimination, one can transfer $L$ classical bits by generating an (L+1)-qubit GHZ state and transferring one qubit to the remote party. We verify correctness using the NetSquid quantum network simulator: the protocol achieves 100\% raw-key fidelity for keys of length up to 12 bits under both ideal conditions and depolarizing noise up to p = 0.005 per round. We further show that the proposed QKD algorithm can be extended to multi-party QKD and server-client deployment. The proposed scheme offers a transmitted-qubit-efficient, noise-tolerant alternative for bandwidth-limited quantum networks.

quant-ph↗

arXiv2026

Elastic Data Transfer Optimization with Hybrid Reinforcement Learning

Modern scientific data acquisition generates petabytes of data that must be transferred to geographically distant computing clusters. Conventional tools either rely on preconfigured sessions, which are difficult to tune for users without domain expertise, or they adaptively optimize only concurrency while ignoring other important parameters. We present \name, an adaptive data transfer method that jointly considers multiple parameters. Our solution incorporates heuristic-based parallelism, infinite pipelining, and a deep reinforcement learning based concurrency optimizer. To make agent training practical, we introduce a lightweight network simulator that reduces training time to less than four minutes and provides a $2750\times$ speedup compared to online training. Experimental evaluation shows that \name consistently outperforms existing methods across diverse datasets, achieving up to 9.5x higher throughput compared to state-of-the-art solutions.

cs.DC↗

arXiv2025

Adaptive Parallel Downloader for Large Genomic Datasets

Modern next-generation sequencing (NGS) projects routinely generate terabytes of data, which researchers commonly download from public repositories such as SRA or ENA. Existing download tools often employ static concurrency settings, leading to inefficient bandwidth utilization and prolonged download times due to their inability to adapt to dynamic network conditions. We introduce FastBioDL, a parallel file downloader designed for large biological datasets, featuring an adaptive concurrency controller. FastBioDL frames the download process as an online optimization problem, utilizing a utility function and gradient descent to adjust the number of concurrent socket streams in real-time dynamically. This approach maximizes download throughput while minimizing resource overhead. Comprehensive evaluations on public genomic datasets demonstrate that FastBioDL achieves up to $4x$ speedup over state-of-the-art tools. Moreover, in high-speed network experiments, its adaptive design was up to $2.1x$ faster than existing tools. By intelligently optimizing standard HTTP or FTP downloads on the client side, FastBioDL provides a robust and efficient solution for large-scale genomic data acquisition, democratizing high-performance data retrieval for researchers without requiring specialized commercial software or protocols.

cs.DC↗

arXiv2025

Modular Architecture for High-Performance and Low Overhead Data Transfers

High-performance applications necessitate rapid and dependable transfer of massive datasets across geographically dispersed locations. Traditional file transfer tools often suffer from resource underutilization and instability because of fixed configurations or monolithic optimization methods. We propose AutoMDT, a novel modular data transfer architecture that employs a deep reinforcement learning based agent to simultaneously optimize concurrency levels for read, network, and write operations. Our solution incorporates a lightweight network-system simulator, enabling offline training of a Proximal Policy Optimization (PPO) agent in approximately 45 minutes on average, thereby overcoming the impracticality of lengthy online training in production networks. AutoMDT's modular design decouples I/O and network tasks, allowing the agent to capture complex buffer dynamics precisely and to adapt quickly to changing system and network conditions. Evaluations on production-grade testbeds show that AutoMDT achieves up to 8x faster convergence and a 68% reduction in transfer completion times compared with state-of-the-art solutions.

cs.DC↗

arXiv2024

Exploring Attention Mechanisms in Integration of Multi-Modal Information for Sign Language Recognition and Translation

Understanding intricate and fast-paced movements of body parts is essential for the recognition and translation of sign language. The inclusion of additional information intended to identify and locate the moving body parts has been an interesting research topic recently. However, previous works on using multi-modal information raise concerns such as sub-optimal multi-modal feature merging method, or the model itself being too computationally heavy. In our work, we have addressed such issues and used a plugin module based on cross-attention to properly attend to each modality with another. Moreover, we utilized 2-stage training to remove the dependency of separate feature extractors for additional modalities in an end-to-end approach, which reduces the concern about computational complexity. Besides, our additional cross-attention plugin module is very lightweight which doesn't add significant computational overhead on top of the original baseline. We have evaluated the performance of our approaches on the RWTH-PHOENIX-2014 dataset for sign language recognition and the RWTH-PHOENIX-2014T dataset for the sign language translation task. Our approach reduced the WER by 0.9 on the recognition task and increased the BLEU-4 scores by 0.8 on the translation task.

cs.CV↗