Southwest Quantum Information and Technology

Quantum Adversarial Learning in Emulation of Monte-Carlo Methods for Max-cut Approximation: QAOA is not optimal

Presenting Author: Cem Unsal, University of Maryland
Contributing Author(s): Lucas Brady

One of the leading candidates for near-term quantum advantage is the class of Variational Quantum Algorithms, but these algorithms suffer from classical difficulty in optimizing the variational parameters as the number of parameters increases. Therefore, it is important to understand the expressibility and power of various ansätze to produce target states and distributions. To this end, we apply notions of emulation to Variational Quantum Annealing and the Quantum Approximate Optimization Algorithm (QAOA). We show that QAOA is outperformed by variational annealing schedules with equivalent numbers of parameters. Our Variational Quantum Annealing schedule is based on a novel polynomial parameterization that can be optimized in a similar gradient-free way as QAOA, using the same physical ingredients. We also develop and incorporate statistical notions of Monte-carlo methods (not to be confused with Monte Carlo integration) to further elucidate the theoretical framework around these quantum algorithms.

(Session 5 : Thursday from 5:00 pm - 7:00 pm)