Qubit efficient quantum algorithms for the vehicle routing problem on NISQ processors

TL;DR

This paper demonstrates that a qubit-efficient quantum algorithm can solve large vehicle routing problems on NISQ devices, achieving comparable solutions to classical methods while using fewer qubits, thus advancing quantum optimization for industry applications.

Contribution

It introduces a qubit encoding scheme that reduces qubit requirements for quantum VRPTW solutions, enabling larger problem instances to be tackled on NISQ hardware.

Findings

Quantum approach yields solutions comparable to classical Gurobi.

Qubit reduction allows larger problem instances to be processed.

Results are validated on multiple quantum hardware platforms.

Abstract

The vehicle routing problem with time windows (VRPTW) is a common optimization problem faced within the logistics industry. In this work, we explore the use of a previously-introduced qubit encoding scheme to reduce the number of binary variables, to evaluate the effectiveness of NISQ devices when applied to industry relevant optimization problems. We apply a quantum variational approach to a testbed of multiple VRPTW instances ranging from 11 to 3964 routes. These intances were formulated as quadratic unconstrained binary optimization (QUBO) problems based on realistic shipping scenarios. We compare our results with standard binary-to-qubit mappings after executing on simulators as well as various quantum hardware platforms, including IBMQ, AWS (Rigetti), and IonQ. These results are benchmarked against the classical solver, Gurobi. Our approach can find approximate solutions to the VRPTW comparable to those obtained from quantum algorithms using the full encoding, despite the reduction in qubits required. These results suggest that using the encoding scheme to fit larger problem sizes into fewer qubits is a promising step in using NISQ devices to find approximate solutions for industry-based optimization problems, although additional resources are still required to eke out the performance from larger problem sizes.