Neutral atom quantum computers (NAQCs) have emerged as promising candidates for scalable quantum computing, thanks to their advanced hardware capabilities, particularly qubit movement and the Zoned Architecture (ZA). However, fully harnessing these features presents significant compilation challenges, requiring careful coordination across gate scheduling, qubit positioning, atom movement, and inter-zone communication. In this paper, we propose PowerMove, an efficient compiler for NAQCs that unlocks new optimization opportunities, significantly improving qubit movement strategies while seamlessly integrating ZA. Our evaluation demonstrates orders-of-magnitude fidelity improvements over state-of-the-art methods, with execution time reduced by up to 3.76× and compilation time accelerated by up to 216.9× across various NISQ applications. Furthermore, PowerMove extends naturally to the fault-tolerant quantum computing (FTQC) setting, where physical qubits are replaced by logical qubits encoded in QEC codes, achieving a 4.78× reduction in execution time. These results highlight PowerMove's impact on both near-term NISQ applications and long-term FTQC implementations. We have open-sourced our codes at https://github.com/Scarlett0815/PowerMove to facilitate further research and collaboration within the community.

Quantum Error Correction (QEC) is essential for fault-tolerant, large-scale quantum computation.However, error drift in qubits undermines QEC performance during long computations, necessitating frequent calibration.Conventional calibration methods disrupt quantum states, requiring system downtime and rendering in situ calibration impractical.To address this challenge, we propose QECali, a novel framework that enables in situ calibration for surface codes.Our evaluation demonstrates that QECali introduces modest qubit overhead and negligible increases in execution time, offering the first practical solution for in situ calibration in surface code based quantum computation.

The photonic platform holds great promise for quantum computing. Nevertheless, the intrinsic probabilistic characteristic of its native fusion operations introduces substantial randomness into the computing process, posing significant challenges to achieving scalability and efficiency in program execution. In this paper, we introduce a randomness-aware compilation framework designed to concurrently achieve scalability and efficiency. Our approach leverages an innovative combination of offline and online optimization passes, with a novel intermediate representation serving as a crucial bridge between them. Through a comprehensive evaluation, we demonstrate that this framework significantly outperforms the most efficient baseline compiler in a scalable manner, opening up new possibilities for realizing scalable photonic quantum computing.