论文标题
通过推荐系统有效地表征量子演变
Efficient Characterization of Quantum Evolutions via a Recommender System
论文作者
论文摘要
我们通过矩阵分解算法(推荐系统(RS)的一种特定类型的量子化算法来表征量子发展。经历量子进化的系统可以通过多种方式表征。在这里,我们选择(i)通过熵,消极或不和谐等度量量化的量子相关性,以及(ii)状态前景。使用量子寄存器最多10 Quarbits,我们证明RS可以有效地表征单一和非正式的演变。在对RS进行了两个量子位的详细性能分析后,我们表明它可以用来区分量子相关性的干净数据库与嘈杂或假货的数据库。此外,我们发现RS为构建大型量子不和谐数据库带来了重要的计算优势,因此不存在简单的封闭式表达。此外,RS可以有效地表征正在降低量子不和谐以及状态前后的系统的系统。最后,我们利用RS在非线性量子系统中构建不和谐相位空间。
We demonstrate characterizing quantum evolutions via matrix factorization algorithm, a particular type of the recommender system (RS). A system undergoing a quantum evolution can be characterized in several ways. Here we choose (i) quantum correlations quantified by measures such as entropy, negativity, or discord, and (ii) state-fidelity. Using quantum registers with up to 10 qubits, we demonstrate that an RS can efficiently characterize both unitary and nonunitary evolutions. After carrying out a detailed performance analysis of the RS in two qubits, we show that it can be used to distinguish a clean database of quantum correlations from a noisy or a fake one. Moreover, we find that the RS brings about a significant computational advantage for building a large database of quantum discord, for which no simple closed-form expression exists. Also, RS can efficiently characterize systems undergoing nonunitary evolutions in terms of quantum discord reduction as well as state-fidelity. Finally, we utilize RS for the construction of discord phase space in a nonlinear quantum system.