Prime factorization is a difficult problem with classical computing, whose exponential hardness is the foundation of Rivest-Shamir-Adleman (RSA) cryptography. With programmable quantum devices, adiabatic quantum computing has been proposed as a plausible approach to solve prime factorization, having promising advantage over classical computing. Here, we find there are certain hard instances that are consistently intractable for both classical simulated annealing and un-configured adiabatic quantum computing (AQC). Aiming at an automated architecture for optimal configuration of quantum adiabatic factorization, we apply a deep reinforcement learning (RL) method to configure the AQC algorithm. By setting the success probability of the worst-case problem instances as the reward to RL, we show the AQC performance on the hard instances is dramatically improved by RL configuration. The success probability also becomes more evenly distributed over different problem instances, meaning the configured AQC is more stable as compared to the un-configured case. Through a technique of transfer learning, we find prominent evidence that the framework of AQC configuration is scalable -- the configured AQC as trained on five qubits remains working efficiently on nine qubits with a minimal amount of additional training cost.
翻译:古典计算是一个棘手的问题,古典计算具有指数硬度,是量子分解的最佳配置基础。用可编程的量子装置,已提出半巴量子计算,作为解决质子分解的一种合理方法,在古典计算中具有大有希望的优势。在这里,我们发现,对于古典模拟肛门和未经配置的不配置的直径量子计算(AQC)来说,有些硬度是始终难以解决的。为了建立量子分解最佳配置的自动结构,我们采用了深加固学习(RL)方法来配置AQC算法。我们通过将最坏问题案例的成功概率设定为RL的奖励,我们展示了AQC在困难案例中的成绩因RL配置而大大改善。成功概率也因不同的问题案例而更加均衡地分布,这意味着,与未配置的AQC相比,配置的AQC较稳定。通过学习技术,我们发现显著的证据是,AQC配置的框架在五位位高的训练成本上仍然可量化。