论文标题
探索具有遗传算法的近似状态制备量子回路的最佳性
Exploring the optimality of approximate state preparation quantum circuits with a genetic algorithm
论文作者
论文摘要
我们通过应用遗传算法来生成量子电路以进行状态制备，研究了嘈杂的中间量子量子（NISQ）计算机上的近似状态准备问题。该算法可以在评估电路时（例如本机栅极集和Qubit连接性）中考虑物理机器的特定特征。我们使用我们的遗传算法来优化Araujo等人引入的低级状态制备算法提供的电路，并在制备有限数量的CNOT门的HAAR随机状态方面找到了忠诚度的实质性改善。此外，我们观察到，对于具有有限的量子连接性和显着噪声水平（IBM Falcon 5T）的5 Q量量子处理器（IBM Falcon 5T），HAAR随机状态的最大保真度是通过短近似近似状态制备电路而不是精确的准备回路实现的。我们还对近似状态制备电路复杂性进行了理论分析，以激发我们的发现。我们用于量子电路发现的遗传算法可在https://github.com/beratyenilen/qc-ga上自由获得。
We study the approximate state preparation problem on noisy intermediate-scale quantum (NISQ) computers by applying a genetic algorithm to generate quantum circuits for state preparation. The algorithm can account for the specific characteristics of the physical machine in the evaluation of circuits, such as the native gate set and qubit connectivity. We use our genetic algorithm to optimize the circuits provided by the low-rank state preparation algorithm introduced by Araujo et al., and find substantial improvements to the fidelity in preparing Haar random states with a limited number of CNOT gates. Moreover, we observe that already for a 5-qubit quantum processor with limited qubit connectivity and significant noise levels (IBM Falcon 5T), the maximal fidelity for Haar random states is achieved by a short approximate state preparation circuit instead of the exact preparation circuit. We also present a theoretical analysis of approximate state preparation circuit complexity to motivate our findings. Our genetic algorithm for quantum circuit discovery is freely available at https://github.com/beratyenilen/qc-ga .
