We investigate the potential of bio-inspired evolutionary algorithms for designing quantum circuits with specific goals, focusing on two particular tasks. The first one is motivated by the ideas of Artificial Life that are used to reproduce stochastic cellular automata with given rules. We test the robustness of quantum implementations of the cellular automata for different numbers of quantum gates The second task deals with the sampling of quantum circuits that generate highly entangled quantum states, which constitute an important resource for quantum computing. In particular, an evolutionary algorithm is employed to optimize circuits with respect to a fitness function defined with the Mayer-Wallach entanglement measure. We demonstrate that, by balancing the mutation rate between exploration and exploitation, we can find entangling quantum circuits for up to five qubits. We also discuss the trade-off between the number of gates in quantum circuits and the computational costs of finding the gate arrangements leading to a strongly entangled state. Our findings provide additional insight into the trade-off between the complexity of a circuit and its performance, which is an important factor in the design of quantum circuits.

翻译：我们研究了受生物启发的进化算法在设计具有特定目标的量子电路方面的潜力，重点关注两个具体任务。第一个任务受人工生命思想启发，旨在复现具有给定规则的随机元胞自动机。我们测试了不同量子门数量下元胞自动机量子实现的鲁棒性。第二个任务涉及对生成高度纠缠量子态的量子电路进行采样，这种量子态是量子计算的重要资源。具体而言，我们采用进化算法，根据以Mayer-Wallach纠缠度量定义的适应度函数来优化电路。我们证明，通过在探索与利用之间平衡突变率，可以找到最多五个量子比特的纠缠量子电路。我们还讨论了量子电路中门数量与寻找导致强纠缠态的门排列的计算成本之间的权衡。我们的研究结果为电路复杂度与其性能之间的权衡提供了新的见解，这是量子电路设计中的一个重要因素。