Mitigating Precision Errors in Quantum Annealing via Coefficient Reduction of Embedded Hamiltonians

TL;DR

This paper evaluates methods to reduce the dynamic range of embedded Hamiltonians in quantum annealing, demonstrating that certain coefficient reduction techniques improve solution quality on D-Wave hardware.

Contribution

It revisits and assesses three coefficient-reduction methods under minor-embedding constraints, highlighting the effectiveness of interaction-extension in improving sample quality.

Findings

Interaction-extension method effectively reduces dynamic range and improves sample quality.

Bounded-coefficient encoding and augmented Lagrangian have limited effects.

Minor-embedding naturally reduces external field coefficients, lessening the need for explicit reduction.

Abstract

Quantum annealing is a quantum algorithm to solve combinatorial optimization problems. In the current quantum annealing devices, the dynamic range of the input Ising Hamiltonian, defined as the ratio of the largest to the smallest coefficient, significantly affects the quality of the output solution due to limited hardware precision. Several methods have been proposed to reduce the dynamic range by reducing large coefficients in the Ising Hamiltonian. However, existing studies do not take into account minor-embedding, which is an essential process in current quantum annealers. In this study, we revisit three existing coefficient-reduction methods under the constraints of minor-embedding. We evaluate to what extent these methods reduce the dynamic range of the minor-embedded Hamiltonian and improve the sample quality obtained from the D-Wave Advantage quantum annealer. The results show that, on the set of problems tested in this study, the interaction-extension method effectively improves the sample quality by reducing the dynamic range, while the bounded-coefficient integer encoding and the augmented Lagrangian method have only limited effects. Furthermore, we empirically show that reducing external field coefficients at the logical Hamiltonian level is not required in practice, since minor-embedding automatically has the role of reducing them. These findings suggest future directions for enhancing the sample quality of quantum annealers by suppressing hardware errors through preprocessing of the input problem.