Abstract
A significant hurdle in the noisy intermediate-scale quantum (NISQ) era is identifying functional quantum circuits. These circuits must also adhere to the constraints imposed by current quantum hardware limitations. Variational quantum algorithms (VQAs), a class of quantum-classical optimization algorithms, were developed to address these challenges in the currently available quantum devices. However, the overall performance of VQAs depends on the initialization strategy of the variational circuit, the structure of the circuit (also known as ansatz), and the configuration of the cost function. Focusing on the structure of the circuit, in this thesis, we improve the performance of VQAs by automating the search for an optimal structure for the variational circuits using reinforcement learning (RL). Within the thesis, the optimality of a circuit is determined by evaluating its depth, the overall count of gates and parameters, and its accuracy in solving the given problem. The task of automating the search for optimal quantum circuits is known as quantum architecture search (QAS). The majority of research in QAS is primarily focused on a noiseless scenario. Yet, the impact of noise on the QAS remains inadequately explored. In this thesis, we tackle the issue by introducing a tensor-based quantum circuit encoding, restrictions on environment dynamics to explore the search space of possible circuits efficiently, an episode halting scheme to steer the agent to find shorter circuits, a double deep Q-network (DDQN) with an $\epsilon$-greedy policy for better stability. The numerical experiments on noiseless and noisy quantum hardware show that in dealing with various VQAs, our RL-based QAS outperforms existing QAS. Meanwhile, the methods we propose in the thesis can be readily adapted to address a wide range of other VQAs.
Abstract (translated)
在嘈杂的中规模量子（NISQ）时代，一个重大的挑战是确定功能量子电路。这些电路还必须遵守当前量子硬件的限制。为了应对这些挑战，已经开发了一种类量子经典优化算法——变分量子算法（VQAs）。然而，VQAs的总体性能取决于变分电路的初始化策略、电路的结构（也称为解法）和成本函数的配置。 在本文中，我们通过使用强化学习（RL）自动搜索最优电路结构来提高VQAs的性能。在本论文中，我们通过提高VQAs的搜索深度、总门电路数和解决给定问题的准确性来评估优化电路的优劣。 量子架构搜索（QAS）是自动搜索最优量子电路的任务。然而，QAS研究的主要集中在无噪声场景。然而，对QAS中噪声对搜索空间的影响尚缺乏充分的探讨。 本文我们通过引入基于张量的量子电路编码、环境动态限制，探索可能的电路搜索空间，实现一个 episode halting scheme，以及一个双深 Q-网络（DDQN）和一个具有 $\epsilon$-greedy 策略的量子环网络，来解决这一问题。 在无噪声和有噪声的量子硬件上进行数值实验证明，基于强化学习的 QAS在处理各种VQAs方面优于现有QAS。与此同时，我们提出的 thesis 中所提出的方法可以很容易地适应解决各种其他 VQAs。
URL
https://arxiv.org/abs/2402.13754
