Chronological feed of everything captured from Edward Farhi.
This document summarizes a panel discussion on the future of quantum computing that took place at the 8th International Conference on Quantum Techniques in Machine Learning. The panel, held on November 26th, 2024, featured four discussants and was hosted by the University of Melbourne. The content focuses on the discussion points raised during this session.
The authors leverage the Quantum Approximate Optimization Algorithm (QAOA) to establish new lower bounds for the MaxCut problem on 3-regular graphs with high minimum girth. By utilizing classical numerical analysis of QAOA's expected performance, they prove the existence of cuts that exceed previous lower bounds for girth $g ext{ ≥ } 16$ at circuit depth $p ext{ ≥ } 7$.
This paper investigates strategies to reduce the computational overhead of the Quantum Approximate Optimization Algorithm (QAOA). It explores the use of instance-independent "tree" parameters for standard QAOA, demonstrating near-optimal performance without extensive parameter optimization. Additionally, it modifies the warm-start QAOA, showing comparable performance to the Goemans-Williamson algorithm for certain graph types. These findings suggest practical approaches for implementing QAOA on larger quantum systems.