论文标题
通过学习KRAUS操作员，梯度降低量子过程断层扫描
Gradient-descent quantum process tomography by learning Kraus operators
论文作者
论文摘要
我们通过使用KRAUS操作员学习过程表示，对离散和连续变量的量子系统执行量子过程断层扫描（QPT）。 kraus形式确保重建过程是完全积极的。为了使过程保持痕量保护，我们在优化期间在所谓的Stiefel歧管上使用受约束的梯度 - 偏生度（GD）方法，以获得Kraus operators。我们的Ansatz使用一些Kraus操作员来避免直接估计大型过程矩阵，例如Choi矩阵，用于低级量子过程。 GD-QPT匹配压缩 - 感应（CS）的性能和投影最小二乘（PLS）的基准测试中的最小二乘（PLS）QPT具有两个Qubit的随机过程，但是通过结合这两种方法的最佳功能来发光。与CS相似（但与PLS不同），GD-QPT可以从少量随机测量中重建一个过程，并且与PLS相似（但与CS不同）也适用于更大的系统大小，最多可达至少五个Qubits。我们设想，GD-QPT的数据驱动方法可以大大降低中等规模量子系统中QPT的成本和计算工作的实用工具。
We perform quantum process tomography (QPT) for both discrete- and continuous-variable quantum systems by learning a process representation using Kraus operators. The Kraus form ensures that the reconstructed process is completely positive. To make the process trace-preserving, we use a constrained gradient-descent (GD) approach on the so-called Stiefel manifold during optimization to obtain the Kraus operators. Our ansatz uses a few Kraus operators to avoid direct estimation of large process matrices, e.g., the Choi matrix, for low-rank quantum processes. The GD-QPT matches the performance of both compressed-sensing (CS) and projected least-squares (PLS) QPT in benchmarks with two-qubit random processes, but shines by combining the best features of these two methods. Similar to CS (but unlike PLS), GD-QPT can reconstruct a process from just a small number of random measurements, and similar to PLS (but unlike CS) it also works for larger system sizes, up to at least five qubits. We envisage that the data-driven approach of GD-QPT can become a practical tool that greatly reduces the cost and computational effort for QPT in intermediate-scale quantum systems.
