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English(EN) Reducing the Cost of Qudit State Preparation by Learning a Sparse Structure for SNAP Gates

新方法学习量子状态制备的最优相位

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种新方法，利用SNAP-位移序列优化玻色子Qudit中的量子状态制备。该技术学习哪些相位是必需的及其值，采用可微分掩码和稀疏性惩罚来去除冗余相位。该方法旨在通过简化具有多个可训练相位的系统所需的复杂控制脉冲来减少控制误差。与全参数化和预定义的稀疏ansatzes相比，根据目标状态和Qudit维度，该方法在保真度-资源权衡方面表现出具有竞争力或有所改进。 AI

排序理由 学术论文，详细介绍了一种新的量子状态制备方法。[lever_c_demoted from research: ic=1 ai=0.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新方法学习量子状态制备的最优相位

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学术论文，详细介绍了一种新的量子状态制备方法。[lever_c_demoted from research: ic=1 ai=0.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Marco Pennazio, Leonardo Ignazio Pagliochini, Riccardo Pellini, Maurizio Ferrari Dacrema, Paolo Cremonesi ·

    通过学习SNAP门稀疏结构降低量子比特状态制备成本

    arXiv:2610.21557v1 Announce Type: cross Abstract: Preparing quantum states in bosonic qudits with SNAP--Displacement sequences requires both optimizing the gate parameters and deciding which Fock levels should be controlled by each SNAP gate. A fully parameterized ansatz assigns …

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