Counterdiabatic Optimized Local Driving
Counterdiabatic Optimized Local Driving
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DOI：
10.1103/prxquantum.4.010312
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发表时间：
2022-03
期刊：
影响因子：
9.7
通讯作者：
Ieva Čepaitė;A. Polkovnikov;A. Daley;C. Duncan
中科院分区：
文献类型：
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作者：
Ieva Čepaitė;A. Polkovnikov;A. Daley;C. Duncan
绝热协议在各种量子技术中使用，从实现状态准备和作为较大设备的构建块的单独操作，到量子退火和绝热量子计算中的更高级别协议。加速这些过程的问题已经引起了大量的兴趣，导致了一系列的方法，最引人注目的是量子最优控制和绝热性的捷径。这两种方法是互补的：最优控制操纵控制领域，以引导在最小允许的时间内的动态，而绝热性的捷径旨在保持绝热条件后，加速。我们概述了一种新的方法，它结合了这两种方法，并利用各自的优势。新技术改进了近似局部反绝热驱动与时间相关的控制字段的增加。我们将这种新方法称为反绝热优化局部驱动（COLD），我们表明，当应用于退火协议，状态准备方案，纠缠生成和晶格上的人口转移时，它可以导致实质性的改善。我们还展示了一种新的方法来优化控制领域，不需要访问的波函数或系统动力学的计算。COLD可以提高现有的先进的最优控制方法，我们探索这一点，使用斩波随机基础方法和梯度上升脉冲工程。
Adiabatic protocols are employed across a variety of quantum technologies, from implementing state preparation and individual operations that are building blocks of larger devices, to higher-level protocols in quantum annealing and adiabatic quantum computation. The problem of speeding up these processes has garnered a large amount of interest, resulting in a menagerie of approaches, most notably quantum optimal control and shortcuts to adiabaticity. The two approaches are complementary: optimal control manipulates control fields to steer the dynamics in the minimum allowed time while shortcuts to adiabaticity aim to retain the adiabatic condition upon speed-up. We outline a new method which combines the two methodologies and takes advantage of the strengths of each. The new technique improves upon approximate local counterdiabatic driving with the addition of time-dependent control fields. We refer to this new method as counterdiabatic optimised local driving (COLD) and we show that it can result in a substantial improvement when applied to annealing protocols, state preparation schemes, entanglement generation and population transfer on a lattice. We also demonstrate a new approach to the optimisation of control fields which does not require access to the wavefunction or the computation of system dynamics. COLD can be enhanced with existing advanced optimal control methods and we explore this using the chopped randomised basis method and gradient ascent pulse engineering.