Optimizing the performance of the Quantum Approximate Optimizaton Algorithm (QAOA) using Policy Gradients.

The QAOA (https://arxiv.org/abs/1411.4028) is a variational quantum algorithm for approximating the ground state of some Hamiltonian, H. The quality of the approximation obtained from the QAOA depends on several input parameters, γ and β . In this project, I used reinforcement learning in order to find optimial values of γ and β much faster than what was obtained by using classical optimization techniques alone.

• The model tries to maximize the average performance of the QAOA on the MaxCut problem across a collection of 3-Regular, 4-Regular, and Erdos-Renyi graphs of varying densities.
• The model is trained to find the optimal distribution from which to select γ and β, as was proposed in https://arxiv.org/pdf/2002.01068.pdf.
• The model also uses graph convolutions and Spatial Pyramidal Pooling (https://arxiv.org/abs/1406.4729) to create additional input features to improve the quality of the model predictions.

• How to use Tensorflow, Tensorflow Quantum, and perform quantum computing simulations with Google's Cirq
• How the QAOA works from a physics and a computational perspective
• How reinforcement learning works from a computational and mathematical perspective
• How to implement Policy Gradients in a continuous action space
• How to implement convolutional neural networks in Tensorflow
• How to manage .csv files in python
• How to manage large datasets with pandas