Hamiltonian-based Quantum Reinforcement Learning for Neural Combinatorial Optimization

TL;DR

This paper introduces Hamiltonian-based Quantum Reinforcement Learning, a novel approach combining quantum computing and neural methods to solve a wide range of combinatorial optimization problems more effectively.

Contribution

It presents a new Hamiltonian-based QRL approach that is more broadly applicable and trainable than previous quantum algorithms like QAOA.

Findings

Favorable trainability compared to hardware-efficient ansatzes

Broad applicability to various combinatorial problems

Competitive performance with QAOA

Abstract

Advancements in Quantum Computing (QC) and Neural Combinatorial Optimization (NCO) represent promising steps in tackling complex computational challenges. On the one hand, Variational Quantum Algorithms such as QAOA can be used to solve a wide range of combinatorial optimization problems. On the other hand, the same class of problems can be solved by NCO, a method that has shown promising results, particularly since the introduction of Graph Neural Networks. Given recent advances in both research areas, we introduce Hamiltonian-based Quantum Reinforcement Learning (QRL), an approach at the intersection of QC and NCO. We model our ansatzes directly on the combinatorial optimization problem's Hamiltonian formulation, which allows us to apply our approach to a broad class of problems. Our ansatzes show favourable trainability properties when compared to the hardware efficient ansatzes, while also not being limited to graph-based problems, unlike previous works. In this work, we evaluate the performance of Hamiltonian-based QRL on a diverse set of combinatorial optimization problems to demonstrate the broad applicability of our approach and compare it to QAOA.