Ding Yongcheng, Ban Yue, Chen Xi
International Center of Quantum Artificial Intelligence for Science and Technology (QuArtist) and Department of Physics, Shanghai University, Shanghai 200444, China.
Department of Physical Chemistry, University of the Basque Country UPV/EHU, Apartado 644, 48080 Bilbao, Spain.
Entropy (Basel). 2022 Nov 29;24(12):1743. doi: 10.3390/e24121743.
We propose the combination of digital quantum simulation and variational quantum algorithms as an alternative approach to numerical methods for solving quantum control problems. As a hybrid quantum-classical framework, it provides an efficient simulation of quantum dynamics compared to classical algorithms, exploiting the previous achievements in digital quantum simulation. We analyze the trainability and the performance of such algorithms based on our preliminary works. We show that specific quantum control problems, e.g., finding the switching time for bang-bang control or the digital quantum annealing schedule, can already be studied in the noisy intermediate-scale quantum era. We foresee that these algorithms will contribute even more to quantum control of high precision if the hardware for experimental implementation is developed to the next level.
我们提出将数字量子模拟和变分量子算法相结合，作为解决量子控制问题的数值方法的替代途径。作为一种混合量子-经典框架，与经典算法相比，它利用数字量子模拟的先前成果，提供了对量子动力学的高效模拟。我们基于初步工作分析了此类算法的可训练性和性能。我们表明，特定的量子控制问题，例如找到bang-bang控制的切换时间或数字量子退火调度，在有噪声的中等规模量子时代已经可以进行研究。我们预计，如果用于实验实现的硬件发展到下一个水平，这些算法将对高精度量子控制做出更大贡献。
