Quantum Annealing Learning Search Implementations

TL;DR

This paper details the implementation and testing of the Quantum Annealing Learning Search (QALS) algorithm on a D-Wave quantum annealer, highlighting its capabilities and limitations in solving specific combinatorial problems.

Contribution

It provides practical implementations of QALS in C++ and Python and evaluates its performance on NPP and TSP problems, demonstrating its ability to encode problems not directly mappable to quantum hardware.

Findings

QALS struggles compared to classical methods on NPP.

QALS successfully encodes problems not directly mappable to QPU.

Implementation details facilitate future research in quantum-classical hybrid algorithms.

Abstract

This paper presents the details and testing of two implementations (in C++ and Python) of the hybrid quantum-classical algorithm Quantum Annealing Learning Search (QALS) on a D-Wave quantum annealer. QALS was proposed in 2019 as a novel technique to solve general QUBO problems that cannot be directly represented into the hardware architecture of a D-Wave machine. Repeated calls to the quantum machine within a classical iterative structure and a related convergence proof originate a learning mechanism to find an encoding of a given problem into the quantum architecture. The present work considers the Number Partitioning Problem (NPP) and the Travelling Salesman Problem (TSP) for the testing of QALS. The results turn out to be quite unexpected, with QALS not being able to perform as well as the other considered methods, especially in NPP, where classical methods outperform quantum annealing in general. Nevertheless, looking at the TSP tests, QALS has fulfilled its primary goal, i.e., processing QUBO problems not directly mappable to the QPU topology.