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arXiv 2411.02447quant-phcs.SYeess.SY
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qGDP:超导量子计算机的量子合法化与详细布局

qGDP: Quantum Legalization and Detailed Placement for Superconducting Quantum Computers

Junyao Zhang, Guanglei Zhou, Feng Cheng, Jonathan Ku, Qi Ding, Jiaqi Gu, Hanrui Wang, Hai "Helen" Li, Yiran Chen

AI总结:

qGDP是针对超导量子计算机的量子合法化与详细布局工具,通过遵循量子空间约束和减少谐振器交叉来缓解串扰,在NISQ基准上较最先进引擎平均提升保真度34.4倍、减少空间违规16.9倍。

摘要:

噪声中等规模量子(NISQ)计算机目前受限于其量子比特数量,这阻碍了向容错量子计算发展的进程。扩展这些系统的一个主要挑战是串扰,它源于相邻组件(如量子比特和谐振器)之间的不必要相互作用。一种针对超导量子计算机量身定制的创新布局策略,可以在有限基底面积约束下系统地解决串扰问题。合法化是布局过程中的关键阶段,它优化全局布局后的配置以满足设计约束并提升布局质量。然而,现有的合法化器不支持量子布局的合法化。我们旨在通过qGDP解决这一空白,qGDP通过遵循量子空间约束并减少谐振器交叉来细致地合法化量子组件,以缓解各种串扰效应。我们的结果表明,qGDP有效地合法化并微调了布局,解决了各种设备拓扑中固有的量子特定空间约束。通过评估多种NISQ基准,qGDP始终优于最先进的合法化引擎,在保真度提升和空间违规减少方面带来了显著改进,平均增益分别为34.4倍和16.9倍。

英文摘要:

Noisy Intermediate-Scale Quantum (NISQ) computers are currently limited by their qubit numbers, which hampers progress towards fault-tolerant quantum computing. A major challenge in scaling these systems is crosstalk, which arises from unwanted interactions among neighboring components such as qubits and resonators. An innovative placement strategy tailored for superconducting quantum computers can systematically address crosstalk within the constraints of limited substrate areas. Legalization is a crucial stage in placement process, refining post-global-placement configurations to satisfy design constraints and enhance layout quality. However, existing legalizers are not supported to legalize quantum placements. We aim to address this gap with qGDP, developed to meticulously legalize quantum components by adhering to quantum spatial constraints and reducing resonator crossing to alleviate various crosstalk effects. Our results indicate that qGDP effectively legalizes and fine-tunes the layout, addressing the quantum-specific spatial constraints inherent in various device topologies. By evaluating diverse NISQ benchmarks. qGDP consistently outperforms state-of-the-art legalization engines, delivering substantial improvements in fidelity and reducing spatial violation, with average gains of 34.4x and 16.9x, respectively.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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