Requirements for Early Quantum Utility and Quantum Utility in the Capacitated Vehicle Routing Problem
pith:CBPBRMM7 Add to your LaTeX paper
What is a Pith Number?\usepackage{pith}
\pithnumber{CBPBRMM7}
Prints a linked pith:CBPBRMM7 badge after your title and writes the identifier into PDF metadata. Compiles on arXiv with no extra files. Learn more
read the original abstract
We introduce a transparent, encoding-agnostic framework for determining when the Capacitated Vehicle Routing Problem (CVRP) can achieve early quantum advantage. Our analysis shows this is unlikely on noisy intermediate scale quantum (NISQ) hardware even in best case scenarios that use the most qubit-efficient direct encodings. Closed-form resource counts, combined with recent device benchmarks, yield three decisive go/no-go figures of merit: the quantum feasibility point and the qubit- and gate-feasibility lines, which place any CVRP instance on a single decision diagram. Contrasting a direct QUBO mapping with a space-efficient higher-order (HOBO) encoding reveals a large gap. Applied to early-advantage benchmarks such as Golden-5, our diagram shows that HOBO circuits require only 7,685 qubits, whereas comparable QUBO encodings still exceed 200,000 qubits. In addition to identifying candidate instances for early quantum advantage in CVRP, the framework provides a unifying go/no-go metric that ingests any CVRP encoding together with any hardware profile and highlights when quantum devices could challenge classical heuristics. Quantum advantage in CVRP would likely require innovative problem decomposition techniques.
This paper has not been read by Pith yet.
Forward citations
Cited by 3 Pith papers
-
Optimal, Qubit-Efficient Quantum Vehicle Routing via Colored-Permutations
A qubit-efficient colored-permutation encoding for CVRP enables Constraint-Enhanced QAOA to recover verified optimal solutions on benchmarks without additional capacity qubits.
-
Qubit-Scalable CVRP via Lagrangian Knapsack Decomposition and Noise-Aware Quantum Execution
A hybrid quantum framework decomposes CVRP into bounded-width knapsack subproblems, trains a reinforcement learning controller for Lagrangian multipliers, and uses a contextual bandit to adapt quantum hardware executi...
-
Quantum optimisation in cities: Limitations and prospects of urban transport systems
Quantum methods lack proven benefits for full-scale urban transport optimization and are best suited to small combinatorial subproblems within hybrid classical-quantum frameworks.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.