Adversarial learning is one of the most successful approaches to modelling high-dimensional probability distributions from data. The quantum computing community has recently begun to generalize this idea and to look for potential applications. In this work, we derive an adversarial algorithm for the problem of approximating an unknown quantum pure state. Although this could be done on universal quantum computers, the adversarial formulation enables us to execute the algorithm on near-term quantum computers. Two parametrized circuits are optimized in tandem: One tries to approximate the target state, the other tries to distinguish between target and approximated state. Supported by numerical simulations, we show that resilient backpropagation algorithms perform remarkably well in optimizing the two circuits. We use the bipartite entanglement entropy to design an efficient heuristic for the stopping criterion. Our approach may find application in quantum state tomography.
翻译:反versarial 学习是模拟数据高维概率分布的最成功方法之一。 量子计算界最近开始推广这一想法并寻找潜在的应用。 在这项工作中, 我们为接近未知量子纯状态的问题得出了一种对抗算法。 虽然这可以在通用量子计算机上进行, 但对抗配方使我们能够在近期量子计算机上执行算法。 两条配比化电路同步优化: 一个试图接近目标状态, 另一个试图区分目标状态和近似状态。 在数字模拟的支持下, 我们显示有弹性的反反对算法在优化两条电路方面表现极好。 我们使用双边缠绕酶酶酶来设计高效的阻断标准。 我们的方法可能在量子状态成像学中找到应用 。