Equation-of-state accuracy ε is proposed as a QA fidelity metric; classical QMC benchmarks reach 10^8 Rydberg atoms at ε ~ 10^{-2}–10^{-4}, outperforming estimated experimental platforms.
Phillipson, Quantum Computing in Logistics and Supply Chain Management an Overview (2024), arXiv:2402.17520 [quant-ph]
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
abstract
The work explores the integration of quantum computing into logistics and supply chain management, emphasising its potential for use in complex optimisation problems. The discussion introduces quantum computing principles, focusing on quantum annealing and gate-based quantum computing, with the Quantum Approximate Optimisation Algorithm and Quantum Annealing as key algorithmic approaches. The paper provides an overview of quantum approaches to routing, logistic network design, fleet maintenance, cargo loading, prediction, and scheduling problems. Notably, most solutions in the literature are hybrid, combining quantum and classical computing. The conclusion highlights the early stage of quantum computing, emphasising its potential impact on logistics and supply chain optimisation. In the final overview, the literature is categorised, identifying quantum annealing dominance and a need for more research in prediction and machine learning is highlighted. The consensus is that quantum computing has great potential but faces current hardware limitations, necessitating further advancements for practical implementation.
fields
quant-ph 5years
2026 5representative citing papers
AQUIRE is the first error-aware adaptive Bayesian protocol for simultaneously estimating the mean and error of observables on qudit quantum computers using generalized Pauli operators and overlap grouping.
A DQN-guided controller selectively invokes quantum sampling within ALNS repair for constrained VRP, finding quantum repair admissible in ~16% of states but beneficial in 29/36 matched-budget settings.
AtomTreeSearch embeds a neutral-atom quantum MWIS subroutine inside Monte Carlo Tree Search and matches or exceeds OR-Tools and simulated annealing on TSP instances up to 100 cities.
Iterative cutting-plane generation and arc preprocessing reduce TSP model size and yield performance gains on classical, direct quantum, and hybrid D-Wave solvers.
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A fidelity metric for quantum annealing benchmarked by extreme scaling quantum Monte-Carlo simulations
Equation-of-state accuracy ε is proposed as a QA fidelity metric; classical QMC benchmarks reach 10^8 Rydberg atoms at ε ~ 10^{-2}–10^{-4}, outperforming estimated experimental platforms.
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An Error-aware and Adaptive Method for the Estimation of Quantum Observables on Qudit-Based Quantum Computers
AQUIRE is the first error-aware adaptive Bayesian protocol for simultaneously estimating the mean and error of observables on qudit quantum computers using generalized Pauli operators and overlap grouping.
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RL-Guided Quantum-ALNS for Constrained VRP
A DQN-guided controller selectively invokes quantum sampling within ALNS repair for constrained VRP, finding quantum repair admissible in ~16% of states but beneficial in 29/36 matched-budget settings.
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Quantum-enhanced Monte Carlo Tree Search framework for combinatorial optimization problems
AtomTreeSearch embeds a neutral-atom quantum MWIS subroutine inside Monte Carlo Tree Search and matches or exceeds OR-Tools and simulated annealing on TSP instances up to 100 cities.
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Cutting-plane methodology via quantum optimization for solving the Traveling Salesman Problem
Iterative cutting-plane generation and arc preprocessing reduce TSP model size and yield performance gains on classical, direct quantum, and hybrid D-Wave solvers.