The study of Variational Quantum Eigensolvers (VQEs) has been in the spotlight in recent times as they may lead to real-world applications of near-term quantum devices. However, their performance depends on the structure of the used variational ansatz, which requires balancing the depth and expressivity of the corresponding circuit. In recent years, various methods for VQE structure optimization have been introduced but the capacities of machine learning to aid with this problem has not yet been fully investigated. In this work, we propose a reinforcement learning algorithm that autonomously explores the space of possible ans{\"a}tze, identifying economic circuits which still yield accurate ground energy estimates. The algorithm is intrinsically motivated, and it incrementally improves the accuracy of the result while minimizing the circuit depth. We showcase the performance of our algorithm on the problem of estimating the ground-state energy of lithium hydride (LiH). In this well-known benchmark problem, we achieve chemical accuracy, as well as state-of-the-art results in terms of circuit depth.
翻译:变化量子Eigensovers(VQEs)的研究近些年来一直受到关注,因为它们可能导致短期量子装置在现实世界的应用,但其性能取决于所使用的变异 ansatz 的结构,这需要平衡相应的电路的深度和广度。近年来,引入了各种VQE结构优化方法,但尚未充分调查机器学习协助这一问题的能力。在这项工作中,我们建议采用一种强化学习算法,自主探索可能的 ans'a'tze 的空间,确定仍然产生准确地面能源估计的经济电路。算法具有内在动机,在尽量减少电路深度的同时逐步提高了结果的准确性。我们展示了我们关于估计锂氢化氢(LiH)地面能源问题的算法的性能。在这个众所周知的基准问题中,我们实现了化学准确性,以及电路深方面的最新结果。