most citedQuantum Compiler Design for Qubit Mapping and Routing: A Cross-Architectural Survey of Superconducting, Trapped-Ion, and Neutral Atom Systems

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quant-ph2025

MUSS-TI: Multi-level Shuttle Scheduling for Large-Scale Entanglement Module Linked Trapped-Ion

Xian Wu, Chenghong Zhu, Jingbo Wang +1

Trapped-ion computing is a leading architecture in the pursuit of scalable and high fidelity quantum systems. Modular quantum architectures based on photonic interconnects offer a…

quant-ph20251 cited

Quantum Compiler Design for Qubit Mapping and Routing: A Cross-Architectural Survey of Superconducting, Trapped-Ion, and Neutral Atom Systems

Chenghong Zhu, Xian Wu, Zhaohui Yang +4

Quantum hardware development is progressing rapidly with substantial advancements achieved across leading platforms, including superconducting circuits, trapped-ion systems, and ne…

quant-ph2025

Scalable Quantum Architecture Search via Landscape Analysis

Chenghong Zhu, Xian Wu, Hao-Kai Zhang +3

Balancing trainability and expressibility is a central challenge in variational quantum computing, and quantum architecture search (QAS) plays a pivotal role by automatically desig…

quant-ph2025

S-SYNC: Shuttle and Swap Co-Optimization in Quantum Charge-Coupled Devices

Chenghong Zhu, Xian Wu, Jingbo Wang +1

The Quantum Charge-Coupled Device (QCCD) architecture is a modular design to expand trapped-ion quantum computer that relies on the coherent shuttling of qubits across an array of…

quant-ph2025

Predicting symmetries of quantum dynamics with optimal samples

Masahito Hayashi, Yu-Ao Chen, Chenghong Zhu +1

Identifying symmetries in quantum dynamics, such as identity or time-reversal invariance, is a crucial challenge with profound implications for quantum technologies. We introduce a…

quant-ph2025

Optimizer-Dependent Generalization Bound for Quantum Neural Networks

Chenghong Zhu, Hongshun Yao, Yingjian Liu +1

Quantum neural networks (QNNs) play a pivotal role in addressing complex tasks within quantum machine learning, analogous to classical neural networks in deep learning. Ensuring co…