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arXiv 2202.00348quant-phcs.LG
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通过混淆将纠缠破裂理解为相变

Learning entanglement breakdown as a phase transition by confusion

Russian Quantum Center(俄罗斯量子中心);Moscow Institute of Physics and Technology(莫斯科物理技术学院);Schaffhausen Institute of Technology(沙夫豪森理工学院);Steklov Mathematical Institute of Russian Academy of Sciences(俄罗斯科学院斯捷克洛夫数学研究所)

M. A. Gavreev, A. S. Mastiukova, E. O. Kiktenko, A. K. Fedorov

摘要:

量子技术需要制备和操纵纠缠多粒子态的方法。然而,确定给定的量子态是纠缠的还是可分离的问题通常被认为是NP难问题,甚至检测给定类别的量子态的纠缠破裂的任务也是困难的。在这项工作中,我们开发了一种使用机器学习技术揭示纠缠分解的方法,这种方法被称为“混淆学习”。我们考虑一个量子态族,它是参数化的,使得有一个单一的临界值划分成单独的和纠缠的家庭内的状态。我们证明了“学习混乱”计划,使我们能够确定的临界值。具体地,我们研究了该方法对两量子比特、两量子比特和两量子比特纠缠态的性能。此外,我们研究了局域去极化和广义振幅阻尼通道的混淆方案的框架下的性质。在我们的方法和设置的参数化的特殊轨迹,我们得到了一个量子信道,这表明区域的纠缠(可分离)状态和纠缠击穿区域的纠缠击穿“相图”。然后,我们扩展了利用“混淆学习”方案来识别任意给定态是纠缠态还是可分态的方法。我们表明,所开发的方法提供了正确的答案,为各种状态,包括纠缠态的正部分转置。我们还提出了一个更实用的版本的方法,这是适合于研究纠缠崩溃的噪声中尺度量子器件。我们使用一个可用的基于云的IBM量子处理器来演示其性能。

英文摘要:

Quantum technologies require methods for preparing and manipulating entangled multiparticle states. However, the problem of determining whether a given quantum state is entangled or separable is known to be an NP-hard problem in general, and even the task of detecting entanglement breakdown for a given class of quantum states is difficult. In this work, we develop an approach for revealing entanglement breakdown using a machine learning technique, which is known as 'learning by confusion'. We consider a family of quantum states, which is parameterized such that there is a single critical value dividing states within this family into separate and entangled. We demonstrate the 'learning by confusion' scheme allows us to determine the critical value. Specifically, we study the performance of the method for the two-qubit, two-qutrit, and two-ququart entangled state. In addition, we investigate the properties of the local depolarization and the generalized amplitude damping channel in the framework of the confusion scheme. Within our approach and setting the parameterization of special trajectories, we obtain an entanglement-breakdown 'phase diagram' of a quantum channel, which indicates regions of entangled (separable) states and the entanglement-breakdown region. Then we extend the way of using the 'learning by confusion' scheme for recognizing whether an arbitrary given state is entangled or separable. We show that the developed method provides correct answers for a variety of states, including entangled states with positive partial transpose. We also present a more practical version of the method, which is suitable for studying entanglement breakdown in noisy intermediate-scale quantum devices. We demonstrate its performance using an available cloud-based IBM quantum processor.

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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