Quantum annealing inspired algorithms for the NISQ Era

TL;DR

This paper explores quantum annealing-inspired algorithms tailored for NISQ devices, enhancing variational quantum optimization through approximate annealing and evolving Hamiltonian schemes.

Contribution

It introduces approximate quantum annealing with resource-efficient regimes and evolving Hamiltonian quantum optimization to improve performance on NISQ hardware.

Findings

Approximate quantum annealing reproduces annealing behavior with fewer resources.

Using annealing-inspired parameters as warm starts improves QAOA performance.

Numerical simulations show enhanced optimization on hard 2-SAT instances.

Abstract

We study algorithms inspired by quantum annealing that are suited for the NISQ era. First, we analyze approximate quantum annealing (AQA), which employs a discretized annealing ansatz in which the time step and the number of layers are allowed to deviate from a faithful implementation of quantum annealing. Parameter scans identify regimes that reproduce annealing-like behavior with reduced resources, making them more suitable for NISQ devices. The resulting parameters can then be used as an effective warm start for the quantum approximate optimization algorithm (QAOA), improving its performance compared to random initializations. We also introduce evolving Hamiltonian quantum optimization (EHQO), a multistep variational scheme that guides the optimization process through intermediate Hamiltonians derived from the standard annealing Hamiltonian. Numerical simulations on sets of hard 2-SAT instances suggest that quantum annealing-inspired algorithms provide practical strategies for enhancing variational quantum optimization.