论文标题
连续的量子门集和脉冲类元优化
Continuous quantum gate sets and pulse class meta-optimization
论文作者
论文摘要
减少量子电路的电路深度是实现量子技术的关键瓶颈。该深度与已合成的可用量子门的数量成反比。此外，量子门的合成和控制问题表现出大量的外部参数依赖性，无论是物理和应用特定的。在本文中，我们解决了学习最佳控制脉冲的家庭的可能性，这些脉冲依赖于各种参数，以便从潜在参数值的空间到控制空间，从而获得全局最佳映射，从而获得连续的门类。我们提出的方法在不同的实验相关量子门上进行了测试，即使在有多个变量或不确定参数的情况下，也能够产生高保真脉冲。
Reducing the circuit depth of quantum circuits is a crucial bottleneck to enabling quantum technology. This depth is inversely proportional to the number of available quantum gates that have been synthesised. Moreover, quantum gate synthesis and control problems exhibit a vast range of external parameter dependencies, both physical and application-specific. In this article we address the possibility of learning families of optimal control pulses which depend adaptively on various parameters, in order to obtain a global optimal mapping from the space of potential parameter values to the control space, and hence continuous classes of gates. Our proposed method is tested on different experimentally relevant quantum gates and proves capable of producing high-fidelity pulses even in presence of multiple variable or uncertain parameters with wide ranges.
