Enhanced Maximum Independent Set Preparation with Rydberg Atoms Guided by the Spectral Gap

TL;DR

This paper introduces ADGLB, a spectral-gap-guided schedule engineering method that improves adiabatic quantum maximum independent set preparation with Rydberg atoms, demonstrating scalability and efficiency on various lattice sizes.

Contribution

The paper presents ADGLB, a novel spectral-gap-guided schedule modification that enhances MIS preparation without extra Hamiltonian complexity or iterative optimization.

Findings

Significant increase in MIS preparation probability with ADGLB on 10-atom chains.

Schedule optimized on small instances effectively applied to larger lattices.

Method remains effective for instances with higher hardness parameters.

Abstract

Adiabatic quantum computation with Rydberg atoms provides a natural route for solving combinatorial optimization problems such as the maximum independent set (MIS). However, its performance is fundamentally limited by the reduction of the spectral gap with increasing system size and connectivity, which induces population leakage from the ground state during finite-time evolution. Here we introduce the Adjusted Detuning for Ground-Energy Leakage Blockade (ADGLB), a spectral-gap-guided schedule engineering method that modifies the laser detuning profile to suppress leakage without introducing additional Hamiltonian terms or iterative optimization loops. We experimentally benchmark ADGLB on a quasi-one-dimensional chain of $N=10$ atoms, and the MIS preparation probability increases substantially compared with the standard adiabatic schedule. Furthermore, we show that the schedule optimized for smaller instances can be directly applied to larger two-dimensional triangular lattices with $N=25$ and $N=37$. With a small heuristic offset, the method also remains effective for instances with higher hardness parameters. These findings demonstrate that spectral-gap-guided schedule engineering offers a scalable and hardware-efficient strategy for enhancing adiabatic quantum optimization on neutral-atom platforms.