论文标题
准备激发态在量子计算机上的核动力学
Preparation of excited states for nuclear dynamics on a quantum computer
论文作者
论文摘要
我们研究了两种不同的方法来准备量子计算机上激发态，这是在线性响应理论中研究动态的关键初步步骤。第一个方法在短时间内使用统一进化，$ t = \ MATHCAL {O}（\ sqrt {1-f}）$用fidelity $ f $和成功概率$ p \ of1-f $近似于激发运算符$ \ hat $ \ hat {o} $的动作。第二种方法概率地使用单位（LCU）算法的线性组合使用激发算子。我们使用玩具模型进行热中子蛋白捕获的玩具模型，将这些技术基准在模拟和真实的量子设备上进行基准测试。尽管具有更大的内存足迹，但基于LCU的方法即使在当前一代噪声设备上也有效，并且可以以低的门成本来实施，而不是天真的分析所表明的。这些发现表明，旨在实现容错量子设备上良好渐近缩放的量子技术也可能在连通性和栅极保真度有限的设备上提供实际好处。
We study two different methods to prepare excited states on a quantum computer, a key initial step to study dynamics within linear response theory. The first method uses unitary evolution for a short time $T=\mathcal{O}(\sqrt{1-F})$ to approximate the action of an excitation operator $\hat{O}$ with fidelity $F$ and success probability $P\approx1-F$. The second method probabilistically applies the excitation operator using the Linear Combination of Unitaries (LCU) algorithm. We benchmark these techniques on emulated and real quantum devices, using a toy model for thermal neutron-proton capture. Despite its larger memory footprint, the LCU-based method is efficient even on current generation noisy devices and can be implemented at a lower gate cost than a naive analysis would suggest. These findings show that quantum techniques designed to achieve good asymptotic scaling on fault tolerant quantum devices might also provide practical benefits on devices with limited connectivity and gate fidelity.
