Quantum-Efficient Reinforcement Learning Solutions for Last-Mile On-Demand Delivery

TL;DR

This paper explores the use of quantum-enhanced reinforcement learning to efficiently solve large-scale last-mile delivery routing problems, demonstrating potential advantages over classical methods in complexity and scalability.

Contribution

It introduces a novel quantum-augmented RL framework with a problem-specific quantum encoding circuit for optimizing delivery routes under real-world constraints.

Findings

Quantum RL outperforms classical methods in solution scale

Proposed quantum encoding circuit improves training efficiency

Method effectively handles complex delivery constraints

Abstract

Quantum computation has demonstrated a promising alternative to solving the NP-hard combinatorial problems. Specifically, when it comes to optimization, classical approaches become intractable to account for large-scale solutions. Specifically, we investigate quantum computing to solve the large-scale Capacitated Pickup and Delivery Problem with Time Windows (CPDPTW). In this regard, a Reinforcement Learning (RL) framework augmented with a Parametrized Quantum Circuit (PQC) is designed to minimize the travel time in a realistic last-mile on-demand delivery. A novel problem-specific encoding quantum circuit with an entangling and variational layer is proposed. Moreover, Proximal Policy Optimization (PPO) and Quantum Singular Value Transformation (QSVT) are designed for comparison through numerical experiments, highlighting the superiority of the proposed method in terms of the scale of the solution and training complexity while incorporating the real-world constraints.