arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2602.09909quant-phcs.ET
收藏

Tucker迭代量子态制备

Tucker iterative quantum state preparation

data cybernetics ssc GmbH(数据控制论ssc有限公司)

Carsten Blank, Israel F. Araujo

AI总结:

Q-Tucker通过利用目标态的全局纠缠结构,自适应构建浅层确定性量子电路,实现高效的量子态制备。

摘要:

量子态制备是量子算法中的基础组件,特别是在量子机器学习和数据处理中,其中经典数据必须高效地编码到量子态中。现有幅度编码技术往往依赖于递归双分或张量分解,这要么导致深度电路,要么缺乏对电路构造的实际指导。在本工作中,我们引入了Tucker迭代量子态制备(Q-Tucker)方法,这是一种新型方法,通过利用目标态的全局纠缠结构,自适应地构建浅层、确定性的量子电路。基于Tucker分解,我们的方法将目标量子态分解为一个核心张量和模式特定的操作符,从而在多个子系统上实现直接分解。

英文摘要:

Quantum state preparation is a fundamental component of quantum algorithms, particularly in quantum machine learning and data processing, where classical data must be encoded efficiently into quantum states. Existing amplitude encoding techniques often rely on recursive bipartitions or tensor decompositions, which either lead to deep circuits or lack practical guidance for circuit construction. In this work, we introduce Tucker Iterative Quantum State Preparation (Q-Tucker), a novel method that adaptively constructs shallow, deterministic quantum circuits by exploiting the global entanglement structure of target states. Building upon the Tucker decomposition, our method factors the target quantum state into a core tensor and mode-specific operators, enabling direct decompositions across multiple subsystems.

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。

↑