Quantum adiabatic optimization with Rydberg arrays: localization phenomena and encoding strategies

TL;DR

This paper investigates how encoding classical optimization problems onto Rydberg atom quantum hardware can cause localization effects that hinder adiabatic performance, and proposes modifications to mitigate these issues.

Contribution

It analyzes the quantum effects of a specific encoding scheme, revealing localization phenomena and proposing quantum-aware improvements for better adiabatic optimization.

Findings

Encoded problems can exhibit exponentially closing gaps due to localization.

Localization impacts success probability on the QuEra Aquila hardware.

Quantum-aware modifications significantly improve adiabatic performance.

Abstract

Quantum adiabatic optimization seeks to solve combinatorial problems using quantum dynamics, requiring the Hamiltonian of the system to align with the problem of interest. However, these Hamiltonians are often incompatible with the native constraints of quantum hardware, necessitating encoding strategies to map the original problem into a hardware-conformant form. While the classical overhead associated with such mappings is easily quantifiable and typically polynomial in problem size, it is much harder to quantify their overhead on the quantum algorithm, e.g., the transformation of the adiabatic timescale. In this work, we address this challenge on the concrete example of the encoding scheme proposed in [Nguyen et al., PRX Quantum 4, 010316 (2023)], which is designed to map optimization problems on arbitrarily connected graphs into Rydberg atom arrays. We consider the fundamental building blocks underlying this encoding scheme and determine the scaling of the minimum gap with system size along adiabatic protocols. Even when the original problem is trivially solvable, we find that the encoded problem can exhibit an exponentially closing minimum gap. We show that this originates from a quantum coherent effect, which gives rise to an unfavorable localization of the ground-state wavefunction. On the QuEra Aquila neutral atom machine, we observe such localization and its effect on the success probability of finding the correct solution to the encoded optimization problem. Finally, we propose quantum-aware modifications of the encoding scheme that avoid this quantum bottleneck and lead to an exponential improvement in the adiabatic performance. This highlights the crucial importance of accounting for quantum effects when designing strategies to encode classical problems onto quantum platforms.