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English(EN) Neural-Network Inverse Design of SRF Cavities and Transmons for Bosonic Quantum Computation

深度神经网络加速量子计算硬件设计

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了两种深度神经网络（DNN）方法来解决用于玻色子量子计算的超导射频（SRF）腔和Transmon量子比特的逆向设计问题。这些DNN能够快速生成满足特定电磁和耦合目标的候选器件几何形状，与传统的迭代模拟方法相比，大大降低了计算成本。第一个DNN提出了SRF腔设计，第二个DNN设计了Transmon量子比特以实现所需的耦合率、量子比特频率和非简谐性，恢复的设计在几个百分点内与目标匹配。 AI

影响 通过利用深度学习加速量子计算硬件的设计过程。

排序理由 该集群包含一篇详细介绍新研究方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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深度神经网络加速量子计算硬件设计

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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Joseph Yaker, Jovan Markovic, Alessandro Reineri, Doga Murat Kurkcuoglu, Silvia Zorzetti ·

    用于玻色子量子计算的超导射频腔和Transmon的神经网络逆向设计

    arXiv:2607.02289v1 Announce Type: cross Abstract: Three-dimensional superconducting radio-frequency (SRF) cavities provide exceptionally long-lived electromagnetic modes and, when coupled to nonlinear elements such as transmon qubits, become promising architectures for bosonic qu…

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