Quantum Observables for continuous control of the Quantum Approximate Optimization Algorithm via Reinforcement Learning

Understand

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.