Abstract
Quantum computing leverages quantum mechanics to address computational problems in ways that differ fundamentally from classical approaches. While current quantum hardware remains error-prone and limited in scale, Variational Quantum Circuits offer a noise-resilient framework suitable for today's devices. The performance of these circuits strongly depends on the underlying architecture of their parameterized quantum components. Identifying efficient, hardware-compatible quantum circuit architectures -- known as Quantum Architecture Search (QAS) -- is therefore essential. Manual QAS is complex and error-prone, motivating efforts to automate it. Among various automated strategies, Reinforcement Learning (RL) remains underexplored, particularly in Quantum Machine Learning contexts. This work introduces RL-QAS, a framework that applies RL to discover effective circuit architectures for classification tasks. We evaluate RL-QAS using the Iris and binary MNIST datasets. The agent autonomously discovers low-complexity circuit designs that achieve high test accuracy. Our results show that RL is a viable approach for automated architecture search in quantum machine learning. However, applying RL-QAS to more complex tasks will require further refinement of the search strategy and performance evaluation mechanisms.
Abstract (translated)
量子计算利用量子力学来解决计算问题，这与传统的计算方法从根本上不同。尽管当前的量子硬件仍然存在错误并且规模有限，但变分量子电路提供了一种适用于现有设备的抗噪框架。这些电路的性能在很大程度上取决于其参数化量子组件的基础架构。因此，识别高效的、硬件兼容的量子电路架构——称为量子架构搜索（QAS）——是至关重要的。手动进行QAS既复杂又容易出错，这促使了自动化它的努力。在各种自动策略中，尽管强化学习（RL）在量子机器学习环境中尚未得到充分探索，但本文介绍了一种名为RL-QAS的框架，该框架利用RL来发现适用于分类任务的有效电路架构。我们使用鸢尾花和二进制MNIST数据集对RL-QAS进行了评估。代理自主发现了低复杂度且测试准确率高的电路设计。我们的结果表明，RL是一种在量子机器学习中进行自动架构搜索的可行方法。然而，要将RL-QAS应用于更复杂的任务，则需要进一步优化搜索策略和性能评估机制。
URL
https://arxiv.org/abs/2509.11198
