论文标题
QGOPT：量子技术的Riemannian优化
QGOpt: Riemannian optimization for quantum technologies
论文作者
论文摘要
量子技术中的许多理论问题都可以被提出，并作为约束优化问题。最常见的量子机械约束，例如，等距和单位矩阵的正交性，量子通道的CPTP特性以及密度矩阵的条件，可以看作是商或嵌入的Riemannian歧管。这允许使用Riemannian优化技术来解决量子力学约束优化问题。在目前的工作中，我们介绍了Qgopt，这是量子技术中受限优化的库。 QGOPT依赖于量子力学约束的基础riemannian结构，并允许在保留量子机械约束的同时应用基于标准梯度的优化方法。此外，QGOPT写在TensorFlow的顶部，这使自动分化能够计算优化的必要梯度。我们展示了两个申请示例：量子门分解和量子断层扫描。
Many theoretical problems in quantum technology can be formulated and addressed as constrained optimization problems. The most common quantum mechanical constraints such as, e.g., orthogonality of isometric and unitary matrices, CPTP property of quantum channels, and conditions on density matrices, can be seen as quotient or embedded Riemannian manifolds. This allows to use Riemannian optimization techniques for solving quantum-mechanical constrained optimization problems. In the present work, we introduce QGOpt, the library for constrained optimization in quantum technology. QGOpt relies on the underlying Riemannian structure of quantum-mechanical constraints and permits application of standard gradient based optimization methods while preserving quantum mechanical constraints. Moreover, QGOpt is written on top of TensorFlow, which enables automatic differentiation to calculate necessary gradients for optimization. We show two application examples: quantum gate decomposition and quantum tomography.
