Performance of quantum versus dequantized algorithms on more complex material models

Determine how QENM-based quantum algorithms and their dequantized classical counterparts perform on more complex physical systems—including doped graphene, vacancy and Stone–Wales defects, and more sophisticated interaction potentials—by characterizing runtime scaling, accuracy, and regimes of quantum advantage.

Background

The paper discusses distinct advantage regimes: long-time dynamics with super-polynomial advantage and short-time dynamics that have recently been dequantized, yielding high-order polynomial advantages. These conclusions, however, are drawn under relatively idealized assumptions.

In realistic materials, complexities such as doping, defects, and non-harmonic potentials may impact algorithmic assumptions and performance. The authors explicitly state that it is not immediately clear how the quantum or dequantized algorithms will fare under these more complex conditions, motivating further research to establish the resulting advantage landscape.

References

Additionally, when the physical system under study becomes more complex, for example by doping, defects or more complex potentials, it is not immediately clear how the quantum or dequantized algorithm will perform and further research is required to establish if one could expect greater, similar or smaller quantum advantage.

— Quantum Elastic Network Models and their Application to Graphene  (2601.05161 - Kolotouros et al., 8 Jan 2026) in Section 6 (Discussion: Road to realistic molecular dynamics simulations)