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Hyeongjun Jeon

Publications and source records attributed to Hyeongjun Jeon.

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Ion-Trap Chip Architecture Optimized for Implementation of Quantum Error-Correcting Code

We propose a scalable trapped-ion quantum-computing architecture that efficiently incorporates quantum error correction. The chip design exploits orthogonal qubit connectivity by assigning horizontal trap regions to transversal logical gates and vertical regions to nontransversal gates and syndrome extraction, thereby enabling universal gate operations with minimal ion shuttling and reduced hardware complexity. Using a dedicated software tool, we evaluate the architecture on several benchmark algorithms and scheduling policies for two-dimensional color code of varying code distance. Our results demonstrate that increasing the code distance by two reduces the effective logical two-qubit gate error probability by approximately two orders of magnitude, reaching values as low as $10^{-8}$ with the $[[31, 1, 7]]$ color code. This improvement substantially expands the range of algorithms that can be executed reliably, up to scales of a few thousand logical qubits, depending on the algorithmic structure. Overall, these findings validate the practicality and scalability of the proposed architecture and its control strategies, highlighting a viable route toward fault-tolerant, trapped-ion quantum computing.

quant-ph

Optimal Qubit Mapping Search for Encoding Classical Data into Matrix Product State Representation with Minimal Loss

Matrix product state (MPS) offers a framework for encoding classical data into quantum states, enabling the efficient utilization of quantum resources for data representation and processing. This research paper investigates techniques to enhance the efficiency and accuracy of MPS representations specifically designed for encoding classical data. Based on the observations that MPS truncation error depends on the pattern of the classical data, we devised an algorithm that finds optimal qubit mapping for given classical data, thereby improving the efficiency and fidelity of the MPS representation. Furthermore, we evaluate the impact of the optimized MPS in the context of quantum classifiers, demonstrating their enhanced performance compared to the conventional mapping. This improvement confirms the efficacy of the proposed techniques for encoding classical data into quantum states. MPS representation combined with optimal qubit mapping can pave a new way for more efficient and accurate quantum data representation and processing.

quant-ph

Scalable Object Detection on Embedded Devices Using Weight Pruning and Singular Value Decomposition

This paper presents a method for optimizing object detection models by combining weight pruning and singular value decomposition (SVD). The proposed method was evaluated on a custom dataset of street work images obtained from https://universe.roboflow.com/roboflow-100/street-work. The dataset consists of 611 training images, 175 validation images, and 87 test images with 7 classes. We compared the performance of the optimized models with the original unoptimized model in terms of frame rate, mean average precision (mAP@50), and weight size. The results show that the weight pruning + SVD model achieved a 0.724 mAP@50 with a frame rate of 1.48 FPS and a weight size of 12.1 MB, outperforming the original model (0.717 mAP@50, 1.50 FPS, and 12.3 MB). Precision-recall curves were also plotted for all models. Our work demonstrates that the proposed method can effectively optimize object detection models while balancing accuracy, speed, and model size.

cs.CV