论文标题
量子状态层析成像的有效分类梯度下降算法
Efficient factored gradient descent algorithm for quantum state tomography
论文作者
论文摘要
重建量子多体系统的状态在量子信息任务中至关重要，但由于维度的诅咒，极具挑战性。在这项工作中，我们提出了一种有效的量子断层扫描协议，该协议将状态因素与特征值映射结合在一起，以解决排名缺陷的问题，并结合了动量加速的梯度下降算法，以加快优化过程。我们实施了广泛的数值实验，以证明我们的梯度下降算法有效地减轻了排名缺陷的问题，并接受了更好的层造影精度和更快的收敛速度。我们还发现，我们的方法可以在一分钟内完成随机11 Quit混合状态的全州断层扫描。
Reconstructing the state of quantum many-body systems is of fundamental importance in quantum information tasks, but extremely challenging due to the curse of dimensionality. In this work, we present an efficient quantum tomography protocol that combines the state-factored with eigenvalue mapping to address the rank-deficient issue and incorporates a momentum-accelerated gradient descent algorithm to speed up the optimization process. We implement extensive numerical experiments to demonstrate that our factored gradient descent algorithm efficiently mitigates the rank-deficient problem and admits orders of magnitude better tomography accuracy and faster convergence. We also find that our method can accomplish the full-state tomography of random 11-qubit mixed states within one minute.
