Southwest Quantum Information and Technology

Digitized Quantum Annealing with oscillating transverse fields in solving optimization problems

Presenting Author: Zhijie Tang, Colorado School of Mines

In recent years, there has been a great deal of focus on Quantum Annealing (QA) and its low-depth digitized counterpart, the Quantum Approximate Optimization Algorithm, as promising tools for solving hard optimization problems. Recent work on RFQA, a modification to QA where local, low-frequency oscillations are added to transverse field terms, has shown it is capable of generating noise-tolerant polynomial quantum speedups in solving hard optimization problems. Inspired by the performance of RFQA, we explore a digitized version that could be simulated on universal gate model machines. We apply the digitized version of RFQA to various trial problems using classical numerical simulation and show that digitized RFQA is a potentially promising tool for solving hard problems in optimization and machine learning in digital quantum computing. We also explore how the chosen timestep can change the effective tunneling rate at exponentially small gaps, and how this effect interacts with the acceleration from RFQA.

(Session 5 : Thursday from 12:00pm-2:00 pm)