Hard instance learning for quantum adiabatic prime factorization
This work addresses a critical bottleneck in quantum computing for cryptography by making adiabatic factorization more robust and scalable, though it is incremental as it builds on existing methods.
The authors tackled the problem of hard instances in quantum adiabatic prime factorization by using deep reinforcement learning to configure the adiabatic quantum computing algorithm, resulting in dramatically improved success probabilities and more stable performance across instances, with transfer learning showing scalability from five to nine qubits with minimal additional training.
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.