Quantum Observables for continuous control of the Quantum Approximate Optimization Algorithm via Reinforcement Learning
This work addresses the challenge of continuous control in quantum computing for researchers in quantum optimization, though it is incremental as it builds on existing QAOA and reinforcement learning methods.
The authors tackled the problem of controlling quantum devices for combinatorial optimization by applying reinforcement learning to the Quantum Approximate Optimization Algorithm (QAOA), achieving optimal results on MAXCUT instances up to size N=21 and enabling knowledge transfer from shorter to longer training episodes.
We present a classical control mechanism for Quantum devices using Reinforcement Learning. Our strategy is applied to the Quantum Approximate Optimization Algorithm (QAOA) in order to optimize an objective function that encodes a solution to a hard combinatorial problem. This method provides optimal control of the Quantum device following a reformulation of QAOA as an environment where an autonomous classical agent interacts and performs actions to achieve higher rewards. This formulation allows a hybrid classical-Quantum device to train itself from previous executions using a continuous formulation of deep Q-learning to control the continuous degrees of freedom of QAOA. Our approach makes a selective use of Quantum measurements to complete the observations of the Quantum state available to the agent. We run tests of this approach on MAXCUT instances of size up to N = 21 obtaining optimal results. We show how this formulation can be used to transfer the knowledge from shorter training episodes to reach a regime of longer executions where QAOA delivers higher results.