Optimizing the performance of the Quantum Approximate Optimizaton Algorithm (QAOA) using recurrent neural networks.
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 metalearning 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 values of γ and β, as was proposed in https://arxiv.org/abs/1907.05415.
- 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 metalearning works from a computational and mathematical perspective
- How to implement a metalearner from scratch in Tensorflow
- How to build custom models in Tensorflow