Power of Non-Stoquastic Quantum Annealing Optimization
Abstract
Despite the excitement brought on by recent technological breakthroughs that have made quantum annealing computing (QuAnCo) optimizers consisting of thousands of quantum bits commercially available, quantum adiabatic protocols have so far failed to deliver on their promise to serve as useful optimizers, i.e., to find bit assignments that minimize the energy, or cost, of discrete combinatorial optimization problems faster than is possible classically. Thus far, no examples, neither experimental nor theoretical, of practical relevance have been found to indicate a superiority of quantum adiabatic optimization (QAO) over traditional methods. The objective of this proposal is to develop a clear understanding of the type of advantages non-stoquastic quantum fluctuations may have to offer in the field of quantum annealing optimization. The effort focuses on comparing the projected performance of quantum adiabatic algorithms equipped with non-stoquastic driving quantum fluctuations with the corresponding performance of their stoquastic, efficiently simulable, analogues and with state-of-the-art conventional solvers running on standard computers.
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 08, 2020
- Accession Number
- AD1108201
Entities
People
- Itay Hen
Organizations
- University of Southern California