Quantum Approximate Optimization Algorithm and How It Compares to Other Algorithms on the Max-2 Satisfiability Problem

Google Quantum Al is a subsidiary of Google that primarily focuses on quantum hardware and software. Some of their biggest accomplishments have been developing quantum chips and creating Cirq, a quantum computing simulation package in python. Through using tools like Cirq and meeting with quantum computing research scientists and engineers, we developed Our Own implementation of algorithms and compared them against the Max-2 Sat. This project was based on the Quantum Approximate Optimization Algorithm (QAOA). The Quantum Approximate Optimization Algorithm was developed in 2014 by Farhi, et al as a novel method for solving combinatorial optimization problems.The algorithm is based on the already existing Structure of the Variational Quantum Eigensolver (VQE). In addition to building our own implementation of QAOA for the Max-2 Sat, we also studied algorithms such as Grover-QAOA, which replaces the mixer operator in the QAOA circuit with a modified diffusion operator based off the workings of Grover's quantum algorithm for unstructured search. Our current goal is to now apply this modified QAOA to the Max-2 Sat instances of different problems and compare the results with normal QAOA, random guessing, and classical algorithms.