Benchmarking Embedded Chain Breaking in Quantum Annealing

TL;DR

This paper empirically benchmarks chain break probabilities in quantum annealing, identifying optimal conditions and post-processing strategies to improve solution accuracy in embedded Hamiltonians.

Contribution

It introduces a systematic benchmarking approach for chain breaks in quantum annealing and proposes hardware-aware tuning and post-processing methods to reduce errors.

Findings

Identified sweet spots for embedded Hamiltonian solutions

Correlated chain breaks with hardware embedding techniques

Developed tailored post-processing strategies to mitigate errors

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.