Hardware-Efficient Quantum Optimization for Transportation Networks via Compressed Adiabatic Evolution

TL;DR

This paper introduces a hybrid quantum optimization framework that compresses adiabatic evolution segments to improve solution efficiency for transportation network problems on near-term quantum hardware.

Contribution

It develops a hardware-efficient hybrid quantum approach combining Approximate Quantum Compilation with variational layers, tested on IBM quantum computers for transportation optimization.

Findings

Moderate prefix compression reduces two-qubit gate depth.

Compressed prefix maintains or improves feasible solution discovery.

Compatibility between compressed prefix and variational ansatz affects performance.

Abstract

Transportation systems such as urban logistics, vehicle routing, and infrastructure planning require solving large-scale combinatorial optimization problems under complex constraints. Problems such as the vehicle routing problem (VRP), traveling salesman problem (TSP), and facility location problem (FLP) involve large discrete search spaces and the need to generate multiple feasible solutions in real time. In this work, we develop a hardware-grounded hybrid quantum optimization framework that uses Approximate Quantum Compilation (AQC) to compress early segments of digitized adiabatic evolution into shallow circuits. The compressed prefix is combined with variational layers, enabling a systematic study of how initialization, circuit depth, and expressivity interact on near-term quantum hardware. All experiments are performed on an IBM gate-based quantum computer, and circuits are evaluated as stochastic generators of candidate transportation plans. Results show that moderate prefix compression reduces two-qubit gate depth while maintaining or improving feasible solution discovery, particularly for routing problems. These benefits depend on compatibility between the compressed prefix and the variational ansatz: while standard QAOA effectively leverages AQC initialization, linear-chain QAOA shows limited improvement. Overall, this work demonstrates that hybrid AQC-QAOA methods provide a practical pathway for hardware-efficient quantum optimization, positioning quantum algorithms as candidate generators within transportation decision-making workflows.