近日，中国科学技术大学的彭新华教授及其研究小组与南方科技大学的翁文康副教授等人合作并取得一项新进展。经过不懈努力，他们成功利用迭代梯度上升脉冲工程算法加速量子最优控制。相关研究成果已于2023年11月7日在国际知名学术期刊《物理评论A》上发表。

为了解决梯度上升脉冲工程(GRAPE)算法在大规模量子系统中实现的难题，该研究团队创新性地提出了迭代GRAPE算法(iGRAPE)。这一算法巧妙地结合了解纠缠操作，将复杂的优化问题简化为一系列低维子问题，从而显著降低了计算复杂度。通过在核磁共振和超导量子系统等物理平台上的数值模拟，研究人员发现，iGRAPE算法在态制备速度上实现了显著的提升。具体而言，与原始的GRAPE算法相比，iGRAPE在使用12个量子比特制备Greenberger–Horne–Zeilinger态时，速度提高了高达5倍；在使用8个量子比特制备任意态时，速度更是提升了惊人的13倍。

为了进一步验证iGRAPE算法的有效性，研究人员还在一个四量子比特的核磁共振系统上进行了实验验证，结果再次证实了iGRAPE的优越性。总体来说，iGRAPE算法为大规模量子系统的最优控制问题提供了一种高效且实用的解决方案，对于在嘈杂的中等规模量子时代推动量子技术的发展具有巨大的潜力。

据悉，量子最优控制已成为实现量子技术的基石之一，它是一个强大的工具箱，能设计最优控制场调制，以最佳方式最精确地实现所需的量子操作。其中，梯度上升脉冲工程(GRAPE)算法作为一种广泛应用的量子最优控制方法，在不同的物理平台上都取得了显著的成功。然而，随着量子比特数量的增加，其计算复杂度呈指数级增长，这使得在大规模量子系统中实现该算法变得极具挑战性。

附：英文原文

Title: Accelerating quantum optimal control through iterative gradient-ascent pulse engineering

Author: Yuquan Chen, Yajie Hao, Ze Wu, Bi-Ying Wang, Ran Liu, Yanjun Hou, Jiangyu Cui, Man-Hong Yung, Xinhua Peng

Issue&Volume: 2023/11/07

Abstract: Quantum optimal control, a powerful toolbox for engineering an optimal control field modulation that most precisely implements a desired quantum operation in the best way possible, has evolved into one of the cornerstones for enabling quantum technologies. The gradient ascent pulse engineering (GRAPE) algorithm is a widely used method in quantum optimal control, which has achieved great success in different physical platforms. However, its computational complexity increases exponentially with the number of qubits, making it challenging to be implemented for large-scale quantum systems. To mitigate this issue, we present the iterative GRAPE algorithm (iGRAPE), which reduces the optimization problem into a series of lower-dimensional subproblems by incorporating disentanglement operations. Our numerical simulations on physical platforms such as nuclear magnetic resonance and superconducting quantum systems demonstrate that iGRAPE significantly enhances state preparation speed. Specifically, compared to GRAPE, iGRAPE achieves up to a five-fold acceleration in preparing Greenberger–Horne–Zeilinger states using a 12-qubit implementation, and up to a 13-fold acceleration for arbitrary state preparation with eight qubits. To further validate our findings, we conduct experimental validation of iGRAPE on a four-qubit nuclear magnetic resonance system. Overall, iGRAPE offers an efficient solution for implementing optimal control in large-scale quantum systems, holding great potential for advancing quantum technologies during the noisy intermediate-scale quantum era.

DOI: 10.1103/PhysRevA.108.052603

Source: https://journals.aps.org/pra/abstract/10.1103/PhysRevA.108.052603