标题： 量子计算中的动力学平均场理论 显示英文标题

标题： Dynamical mean field theory with quantum computing

摘要： 近期内的量子处理器在量子比特和门的数量方面受到限制。 它们仍然提供了对可编程量子系统的前所未有的访问，这些系统可以高效但不完美地模拟量子时间演化。 另一方面，动力学平均场理论将像Hubbard模型这样的强关联格点模型映射到更简单的、但仍为多体模型的杂质模型。 其计算瓶颈归结为在添加或移除一个粒子时研究杂质的动力学。 这个任务对于经典算法来说非常困难，这促使开发了特定的经典算法，称为“杂质求解器”，它们在某些参数范围内表现良好，但仍难以达到某些参数范围。 在这些讲义中，我们介绍了可用于克服这些经典杂质求解器局限性的量子计算工具和方法，无论是长期——使用完全量子算法，还是短期——使用混合量子-经典算法。 显示英文摘要

摘要： Near-term quantum processors are limited in terms of the number of qubits and gates they can afford. They nevertheless give unprecedented access to programmable quantum systems that can efficiently, although imperfectly, simulate quantum time evolutions. Dynamical mean field theory, on the other hand, maps strongly-correlated lattice models like the Hubbard model onto simpler, yet still many-body models called impurity models. Its computational bottleneck boils down to investigating the dynamics of the impurity upon addition or removal of one particle. This task is notoriously difficult for classical algorithms, which has warranted the development of specific classical algorithms called "impurity solvers" that work well in some regimes, but still struggle to reach some parameter regimes. In these lecture notes, we introduce the tools and methods of quantum computing that could be used to overcome the limitations of these classical impurity solvers, either in the long term -- with fully quantum algorithms, or in the short term -- with hybrid quantum-classical algorithms.