Recent advancements in quantum computing have driven the scientific community's quest to solve a certain class of complex problems for which quantum computers would be better suited than traditional supercomputers. In a new study, 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), which allows a quantum computer to solve certain combinatorial problems such as those that arise in materials design, chemistry and wireless communications. QAOA is a hybrid quantum-classical algorithm that uses both classical and quantum computers for approximately solving combinatorial optimization problems. 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.