11 research outputs found
Quantum-Assisted Solution Paths for the Capacitated Vehicle Routing Problem
Many relevant problems in industrial settings result in NP-hard optimization
problems, such as the Capacitated Vehicle Routing Problem (CVRP) or its reduced
variant, the Travelling Salesperson Problem (TSP). Even with today's most
powerful classical algorithms, the CVRP is challenging to solve classically.
Quantum computing may offer a way to improve the time to solution, although the
question remains open as to whether Noisy Intermediate-Scale Quantum (NISQ)
devices can achieve a practical advantage compared to classical heuristics. The
most prominent algorithms proposed to solve combinatorial optimization problems
in the NISQ era are the Quantum Approximate Optimization Algorithm (QAOA) and
the more general Variational Quantum Eigensolver (VQE). However, implementing
them in a way that reliably provides high-quality solutions is challenging,
even for toy examples. In this work, we discuss decomposition and formulation
aspects of the CVRP and propose an application-driven way to measure solution
quality. Considering current hardware constraints, we reduce the CVRP to a
clustering phase and a set of TSPs. For the TSP, we extensively test both QAOA
and VQE and investigate the influence of various hyperparameters, such as the
classical optimizer choice and strength of constraint penalization. Results of
QAOA are generally of limited quality because the algorithm does not reach the
energy threshold for feasible TSP solutions, even when considering various
extensions such as recursive, warm-start and constraint-preserving mixer QAOA.
On the other hand, the VQE reaches the energy threshold and shows a better
performance. Our work outlines the obstacles to quantum-assisted solutions for
real-world optimization problems and proposes perspectives on how to overcome
them.Comment: Submitted to the IEEE for possible publicatio