Reinforcement learning to train quantum algorithm

This visualization of search path is obtained through reinforcement learning (RL) on a test problem with two parameters. The RL method leveraging past experience in solving similar problems quickly moves towards the solution to the unseen but similar problem. Credit: Prasanna Balaprakash / Argonne National Laboratory

Scientists at the U.S. Department of Energy’s (DOE) Argonne National Laboratory have developed a new algorithm based on reinforcement learning to find the optimal parameters for the Quantum Approximate Optimization Algorithm (QAOA).

QAOA allows a quantum computer to solve certain combinatorial problems such as those that arise in materials design, chemistry and wireless communications.

This new algorithm learns how to configure QAOA through a feedback mechanism. A particularity of the proposed algorithm is that it can be trained on smaller problem instances, and the trained model can adapt QAOA to larger problem instances. (TechExplore)

The paper has been published in the Proceedings of the AAAI Conference on Artificial Intelligence.

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