  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.
