Implementing transferable annealing protocols for combinatorial optimisation on neutral atom quantum processors: a case study on smart-charging of electric vehicles

TL;DR

This paper develops transferable annealing protocols for quantum optimization, demonstrating their effectiveness on combinatorial problems and real-world smart-charging applications using neutral atom quantum processors.

Contribution

It extends transferability concepts to annealing protocols with Bayesian optimization, enabling scalable, resource-efficient quantum optimization for practical problems.

Findings

Transferable parameters improve optimization efficiency across similar problem instances.

Optimal parameters tend to concentrate, facilitating transferability.

Experimental validation on Orion Alpha platform with up to 100 qubits.

Abstract

In the quantum optimization paradigm, variational quantum algorithms face challenges with hardware-specific and instance-dependent parameter tuning, which can lead to computational inefficiencies. The promising potential of parameter transferability across problem instances with similar local structures has been demonstrated in the context of the quantum approximate optimization algorithm. In this paper we build on these advancements by extending the concept to annealing-based protocols, employing Bayesian optimization to design robust quasi adiabatic schedules. Our study reveals that, for maximum independent set problems on graph families with shared geometries, optimal parameters naturally concentrate, enabling efficient transferability between similar instances and from smaller to larger ones. Experimental results on the Orion Alpha platform validate the effectiveness of our approach, scaling to problems with up to $100$ qubits. We apply this method to address a smart-charging optimization problem on a real dataset. These findings highlight a scalable, resource-efficient path for hybrid optimization strategies applicable in real-world scenarios.