发表机构
Keio University; Technical University of Denmark; National Institute of Information and Communications Technology (NICT)（庆应义塾大学; 丹麦技术大学; 信息通信研究机构）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出基于高斯线性组合分解的混合离散-连续变量量子电路模拟框架，避免Fock截断，成本随模式数多项式增长，并成功模拟GKP态生成。
AI 中文摘要
结合离散变量（DV）和连续变量（CV）子系统的混合量子系统出现在广泛的物理平台中，并使得超越纯量子比特设计的量子信息处理成为可能。然而，其经典模拟具有挑战性，因为CV和DV系统的计算成本均呈指数增长。本文中，我们提出了一种基于高斯线性组合（LCoG）分解的CV-DV混合系统模拟框架。我们将混合密度矩阵分解为DV基中的块，并将每个块表示为LCoG展开。在该表示中，玻色子子系统上的高斯操作独立作用于每个高斯函数。我们推导了解析公式，描述复高斯函数在受控高斯操作下如何变换。我们进一步将该框架扩展到具有状态依赖高斯噪声的受控高斯信道。该方法避免了CV子系统的Fock空间截断，且更新每个高斯项的成本随模式数量呈多项式增长。该框架为分析混合量子算法、量子模拟协议和非高斯态生成方案提供了通用计算工具。作为示例，我们模拟了基于腔QED系统的Gottesman-Kitaev-Preskill（GKP）态生成。
英文摘要
Hybrid quantum systems combining discrete-variable (DV) and continuous-variable (CV) subsystems arise across a broad range of physical platforms and enable quantum information processing beyond purely qubit-based designs. Their classical simulation, however, is challenging since computational costs of both CV and DV systems exponentially increase. In this paper, we introduce a simulation framework for CV--DV hybrid systems based on linear combination of Gaussian (LCoG) decomposition. We decompose the hybrid density matrix into blocks in a DV basis and represent each block as an LCoG expansion. Within the representation, Gaussian operations on the bosonic subsystem independently act on each Gaussian function. We derive analytic formulas describing how complex Gaussian functions transform under controlled Gaussian operations. We further extend the framework to controlled Gaussian channels with state-dependent Gaussian noise. The method avoids a Fock-space truncation for the CV subsystem, and the cost of updating each Gaussian term scales polynomially with the number of modes. The framework provides a general computational tool for analyzing hybrid quantum algorithms, quantum simulation protocols, and non-Gaussian state generation schemes. As an example, we simulate Gottesman--Kitaev--Preskill (GKP) state generation based on a cavity-QED system.
Comments20 pages, 5 figures
