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English(EN) Data-Driven Hamiltonian Reduction for Superconducting Qubits via Meta-Learning

元学习框架HAML助力超导量子比特哈密顿量约简

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了HAML（Hamiltonian Adaptation via Meta-Learning，通过元学习实现的哈密顿量自适应）框架，该框架专为超导量子处理器中有效哈密顿量模型的快速在线调整而设计。该系统采用两阶段方法：首先在模拟设备上进行监督训练，以将控制输入映射到哈密顿量系数；然后进行在线自适应阶段，利用最少的硬件测量来识别设备参数。HAML通过学习从复杂多模哈密顿量到简单量子比特描述的直接约简，绕过了传统的微扰理论，有望改善量子计算中的校准和控制。 AI

影响 引入了一种新颖的元学习方法用于量子系统表征，可能加速量子处理器校准和控制。

排序理由 介绍量子计算新框架的学术论文。

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

元学习框架HAML助力超导量子比特哈密顿量约简

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介绍量子计算新框架的学术论文。
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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Arielle Sanford, Andrew T. Kamen, Frederic T. Chong, Andy J. Goldschmidt ·

    面向超导量子比特的基于数据的元学习哈密顿量降阶方法

    arXiv:2604.24912v1 Announce Type: cross Abstract: We introduce HAML (Hamiltonian Adaptation via Meta-Learning), a framework for fast online adaptation of effective Hamiltonian models of superconducting quantum processors. HAML proceeds in two phases. A supervised training phase u…

  2. arXiv cs.LG TIER_1 English(EN) · Andy J. Goldschmidt ·

    面向超导量子比特的基于数据的元学习哈密顿量降阶方法

    We introduce HAML (Hamiltonian Adaptation via Meta-Learning), a framework for fast online adaptation of effective Hamiltonian models of superconducting quantum processors. HAML proceeds in two phases. A supervised training phase uses an ensemble of simulated devices to learn an o…

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