Abstract
Quantum annealing solves combinatorial optimization problems by finding the energetic ground states of an embedded Hamiltonian. However, quantum annealing dynamics under the embedded Hamiltonian may violate the principles of adiabatic evolution and generate excitations that correspond to errors in the computed solution. Here we empirically benchmark the probability of chain breaks and identify sweet spots for solving a suite of embedded Hamiltonians. We further correlate the physical location of chain breaks in the quantum annealing hardware with the underlying embedding technique and use these localized rates in a tailored post-processing strategies. Our results demonstrate how to use characterization of the quantum annealing hardware to tune the embedded Hamiltonian and remove computational errors.
| Original language | English |
|---|---|
| Article number | 025029 |
| Journal | Quantum Science and Technology |
| Volume | 7 |
| Issue number | 2 |
| DOIs | |
| State | Published - Apr 2022 |
| Externally published | Yes |
Funding
This manuscript has been authored by UT-Battelle, LLC, under Contract No. DE-AC0500OR22725 with the US Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for the United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan. This work was supported by the Department of Energy, Office of Science Early Career Research Program. This research used resources of the Oak Ridge Leadership Computing Facility, which is a DOE Office of Science User Facilities supported by the Oak Ridge National Laboratory under Contract DE-AC05-00OR22725. We would like to thank Benjamin Stump from Oak Ridge National Laboratory’s National Transportation Research Center for aiding in the formulation of equation (). We would also like to thank Paul Kairys from the Bredesen Center at University of Tennessee for helpful discussion around results in figure .
Keywords
- benchmarking
- chain
- embedding
- optimization
- quantum annealing