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arXiv 2512.00577quant-ph
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量子通道的几何参数化及其在多量子比特系统准反向通道中的应用

Geometric Parameterization of Kraus Operators with Applications to Quasi Inverse Channels for Multi Qubit Systems

Zain Ateeq, Muhammad Faryad

AI总结:

本文提出了一种基于几何参数化的量子通道优化方法,通过梯度下降优化准反向通道,适用于多量子比特系统。

摘要:

本文提出了一种可微的几何参数化方法,用于量子通道的Kraus表示,可以高效地探测未知量子通道。我们探讨了其在寻找准反向通道方面的可行性,这对于复杂噪声过程来说通常是繁琐的分析性工作,且仅在有限参数范围内可行。为此,已成功利用基于机器学习的算法来寻找量子通道的准反向。在该方案中,量子通道的空间是一个单位超球面,其中相互约束的单位向量的组件用于构建物理有效的量子通道。辛约束、正交性和向量的单位长度足以维持通道的完全正定性和迹保持性质。通过在该参数空间上使用基于保真度的损失函数进行梯度下降,发现该方法能够优化多种量子通道的准反向,不仅限于单量子比特,证明了其有效性。

英文摘要:

This work presents a differentiable geometric parameterization of quantum channels in Kraus representation, which can be efficiently probed to find an unknown quantum channel. We explore its feasibility in finding the quasi inverse channels, which can be a tedious analytically for complex noise processes and is often achievable only for a limited range of parameters. In this regard, machine learning based algorithms have been employed successfully to find quasi inverse of quantum channels. The space of quantum channels in this scheme is a unit hypersphere, and components of mutually constrained unit vectors residing in this space, are used to construct a physically valid quantum channel. Symplectic constraints, orthogonality, and unit length of the vectors suffice to maintain complete positivity and the trace-preserving property of the channels. By performing gradient descent on this parametric space with a fidelity-based loss function, this approach is found to optimize quasi inverse of a variety of quantum channels, not limited to single-qubits, proving its effectiveness.

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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