论文标题
数字量子模拟的强化学习
Reinforcement Learning for Digital Quantum Simulation
论文作者
论文摘要
数字量子模拟是量子计算机的有前途的应用。它们的自由程序性提供了潜力，可以通过通过一系列基本量子门离散时间演化运算符来模拟任何多体哈密顿量的单一演化，通常使用Trotterization实现。在这种情况下，一个基本挑战源于涉及量子门的实验缺陷，这严重限制了合理精度内可达到的门的数量，因此可以实现的系统大小和模拟时间。在这项工作中，我们将一种增强学习算法引入了系统地构建优化的量子电路，以构建数字量子模拟，以对允许的量子门的数量施加强大的限制。因此，我们始终获得量子电路，这些电路可长时间繁殖物理可观察到的物理可观察到的，并且长时间纠缠了大门和大型系统尺寸。作为具体的例子，我们将形式主义应用于远距离iSing链和晶格schwinger模型。我们的方法使大规模的数字量子模拟在当前实验技术的范围内成为可能。
Digital quantum simulation is a promising application for quantum computers. Their free programmability provides the potential to simulate the unitary evolution of any many-body Hamiltonian with bounded spectrum by discretizing the time evolution operator through a sequence of elementary quantum gates, typically achieved using Trotterization. A fundamental challenge in this context originates from experimental imperfections for the involved quantum gates, which critically limits the number of attainable gates within a reasonable accuracy and therefore the achievable system sizes and simulation times. In this work, we introduce a reinforcement learning algorithm to systematically build optimized quantum circuits for digital quantum simulation upon imposing a strong constraint on the number of allowed quantum gates. With this we consistently obtain quantum circuits that reproduce physical observables with as little as three entangling gates for long times and large system sizes. As concrete examples we apply our formalism to a long range Ising chain and the lattice Schwinger model. Our method makes larger scale digital quantum simulation possible within the scope of current experimental technology.
