Automated quantum programming via reinforcement learning for combinatorial optimization
This work addresses the challenge of automated quantum programming for researchers and practitioners in quantum computing, though it appears incremental as it builds on existing reinforcement learning techniques applied to quantum systems.
The authors tackled the problem of programming hybrid quantum-classical systems for combinatorial optimization by developing a reinforcement learning method that generates short quantum programs, achieving high-quality solutions on both simulated and real quantum computers with generalization to unseen problems and hardware.
We develop a general method for incentive-based programming of hybrid quantum-classical computing systems using reinforcement learning, and apply this to solve combinatorial optimization problems on both simulated and real gate-based quantum computers. Relative to a set of randomly generated problem instances, agents trained through reinforcement learning techniques are capable of producing short quantum programs which generate high quality solutions on both types of quantum resources. We observe generalization to problems outside of the training set, as well as generalization from the simulated quantum resource to the physical quantum resource.