Quantum computing use cases in logistics

Where quantum computing could apply in logistics

The McKinsey Quantum Technology Monitor, April 2026, covers travel, transport and logistics together and lists five segments: freight network coordination, physical freight transport networks, warehouse and inventory flow networks, passenger transport and hospitality. Its candidate problems include multimodal routing, vehicle routing and scheduling, cargo loading, warehouse layout and order picking. Almost all are optimization problems, and the public evidence is uneven.

Fleet and shipment assignment: a recent preprint with numbers

A preprint by IonQ and Einride authors, submitted in April 2026 and revised in August 2026, addresses the shipment selection problem for Einride, a freight company with electric trucks: filling idle gaps in electric truck schedules left by cancelled shipments. The authors formulate it as an optimization problem, solve it with a quantum algorithm called Iterative-QAOA, and use the answer as a warm start for Einride’s own routing solver. On anonymized real customer data, instances reached 130 qubits. Most results come from noiseless simulation, and the 20 to 35 qubit subset ran on trapped-ion hardware and closely matched the simulation.

Against Einride’s operational solver, the quantum warm start delivered 1.7 percent more shipments on average and 12.1 percent more in the best single scenario, with total operational cost effectively unchanged. The raw quantum answer on its own delivered 5.1 percent fewer shipments on average. The paper does not claim a quantum advantage. Its text shows the limits: large instances rely on approximate simulation, hardware runs cover only 20 to 35 qubits, and average gains are smaller than best-case gains.

Retail, packaging and aviation: company-reported pilots

The Pattison Food Group, a western Canadian food and health retailer with more than 100 stores offering online ordering, uses a scheduler built on D-Wave’s technology to assign weekly work to more than 500 delivery drivers. D-Wave’s case story says it cut a task of 80 person-hours a week to 15 and went into production in late 2022. It also says the remaining time is mostly manual data entry in a legacy system. The figures come from the vendor’s own document.

On July 23, 2026 Telefónica and Würth España announced a pilot that combines quantum computing and AI to pack products into shipping boxes at Würth’s logistics center in La Rioja. With the research center Tecnalia and the start-up QCentroid, the pilot improved packaging efficiency across 14 percent of more than 6,000 orders, cut box use by about 3 percent, and reduced truck transport volume by about 7 percent and cardboard use by more than 6 percent. The release does not name the quantum hardware or the classical baseline.

In December 2020 Machine Learning Reply won the aircraft loading problem in the Airbus Quantum Computing Challenge with an algorithm built on quantum annealing, a way to find the best cargo layout within weight and balance limits. Reply’s release reports no performance figures and says testing with Airbus experts would begin in 2021.

Peer-reviewed routing benchmarks

The published benchmarks are less favorable. A 2019 paper in Frontiers in ICT by researchers at LMU Munich, Volkswagen Data:Lab and other institutions split the capacitated vehicle routing problem, solving the routing step on a D-Wave 2000Q annealer. Deviations from the best known solutions ranged from 2.66 percent to 19.46 percent on the vehicle routing instances, the method did not beat classical metaheuristics, and the authors state that it has not brought clear benefit in solution quality or computation time.

A study by Unisys researchers, which the company says was published in AIP Advances in January 2026, tested a commercial annealing platform on the same problem over more than 30 hours of access. It found that solution quality declines as constraint density rises, and that model complexity matters more than problem size.

A 2025 paper by Fraunhofer CML and eleQtron researchers in TransNav benchmarked a D-Wave hybrid solver against the classical solver CPLEX on maritime inventory routing, with 2 to 4 ports, 2 to 7 vessels and up to 45 time periods. The hybrid system failed to find solutions in the same time as CPLEX on about half the instances, and the solutions it found averaged about 65 to 75 percent of the classical objective values. The authors attribute part of the gap to the problem formulation.

Quantum-inspired methods versus quantum hardware

Several logistics trials use quantum-inspired methods, which run on classical hardware. In a September 2020 announcement, Toyota Systems and Fujitsu reported a trial of a Digital Annealer, a quantum-inspired system and not a quantum computer, on automotive parts distribution in Japan. The problem ran from hundreds of suppliers through transit warehouses to dozens of factories, with more than 3 million possible delivery routes. The system found a route in about 30 minutes, and simulations suggested a potential logistics cost reduction of 2 to 5 percent compared with conventional methods. The two companies said they would verify and refine the solution before commercializing it. In July 2023 Hitachi announced that it would use its quantum-inspired CMOS Annealing to generate delivery routes for Vietnam Post, starting with small demonstration tests, and reported no results.

Review articles agree on the stage. A 2024 review of quantum computing in logistics and supply chain management surveys routing, network design, fleet maintenance, cargo loading, prediction and scheduling, finds that most reviewed solutions are hybrid and that quantum annealing dominates, and describes the field as early stage, with current hardware limits that need further advances before practical use. A review in Nature Reviews Physics by 46 authors stresses benchmarking against classical optimization and proposes metrics for fair comparison.

Shipping, container management and challenge entries

IBM Research and ExxonMobil modeled maritime inventory routing for energy products on quantum devices. IBM’s case study describes the work as modeling that lays the foundation for practical solutions, and reports no results from hardware runs. In July 2024 the shipping group CMA CGM announced an investment in Pasqal and a quantum computing center at its training center, aimed at container management including loading on ships, with no results reported.

In December 2024 Airbus and BMW Group announced the winners of a quantum computing challenge hosted by TQI, with more than 100 submissions. The logistics winner, the UK start-up 4colors Research, used quantum computing to reduce CO2 emissions and other costs in supply chain logistics. The release does not say whether any result came from quantum hardware and quotes a BMW Group executive saying the work is still at an early stage.

McKinsey lists warehouse layout, order picking and port operations as candidate problems. None has a published pilot with results, so TQI does not rate them.

What the value estimates say, and what they do not

The McKinsey Quantum Technology Monitor, April 2026, estimates that quantum computing could be worth $200 billion to $500 billion by 2035 for travel, transport and logistics together, excluding “black swan” breakthroughs. The range includes passenger transport and hospitality, so it is not a logistics-only figure. The impact levers are footnoted as the combined impact of quantum computing and AI, savings are measured against the situation today and not against future technology alternatives, and the report says implementation depends on factors unique to each company. TQI reads it as an order of magnitude and not a forecast for any one carrier.

How to read a quantum logistics claim

1. What is the baseline? Einride’s gains are against its own operational solver. The Fraunhofer benchmark used CPLEX, and the Telefónica release names no baseline.
2. Who measured it? The Pattison and Telefónica figures come from the companies involved. The 2019, 2025 and 2026 benchmark papers are published studies with stated baselines.
3. Best case or average? The 12.1 percent figure is the best single scenario, and the mean is 1.7 percent.
4. Hardware or simulation? Most of the Einride results are noiseless simulation, and hardware covered 20 to 35 qubits.
5. Quantum or quantum-inspired? The Digital Annealer and CMOS Annealing systems are classical hardware.
6. Result or announcement? The CMA CGM, Hitachi and challenge items report plans and no results, and the Toyota Systems saving of 2 to 5 percent is a simulation estimate.

What a logistics team can do now

• Write the problem as an optimization model, run today’s solver, and measure how far its answer is from the best known one. If the gap is small, there is little for any new method to add.
• Test quantum-inspired and classical solvers first. They are the baseline any quantum claim has to beat.
• Ask any vendor or adviser for the instance size, the baseline, and whether the run used hardware or a simulator.
• Keep clean operational data. The Einride study used anonymized real customer data, and results on synthetic instances say less.

Related guides

The series index lists every industry in this series. Supply chain planning also appears in the automotive guide, and shipping of energy products in the energy guide. The finance guide has the longest list of bank pilots in the series. For company and funding data see TQI Data.

Questions

Sources

1. McKinsey Quantum Technology Monitor 2026, April 2026 (travel, transport and logistics value estimate and segment slide read from the report text).
2. Hybrid quantum-classical optimization workflows for the shipment selection problem, Lopez-Ruiz et al., arXiv:2604.11758, submitted April 13, 2026 (v3 August 20, 2026; preprint).
3. Quantum in production: optimizing e-commerce logistics, D-Wave case story on the Pattison Food Group (undated).
4. Telefónica and Würth use quantum computing to optimize logistics packaging, Telefónica press release, July 23, 2026.
5. Machine Learning Reply wins the Airbus Quantum Computing Challenge, Reply press release, December 17, 2020.
6. A hybrid solution method for the capacitated vehicle routing problem using a quantum annealer, Feld et al., Frontiers in ICT 6, 13, June 25, 2019.
7. Performance of commercial quantum annealing solvers for the capacitated vehicle routing problem, Sinno et al., arXiv:2309.05564, September 11, 2023; published in AIP Advances per the Unisys release of January 29, 2026.
8. Benchmarking the maritime inventory routing problem on a quantum annealing-hybrid system, Szal, Rubbert and Rizvanolli, TransNav 19(1), 113 to 122, March 2025.
9. Fujitsu and Toyota Systems optimize large-scale supply chain logistics, Fujitsu press release, September 10, 2020.
10. Hitachi press release on digital infrastructure and CMOS Annealing for Vietnam Post, Hitachi, July 6, 2023.
11. Quantum computing in logistics and supply chain management: an overview, Phillipson, arXiv:2402.17520 (v2 April 2, 2025).
12. Challenges and opportunities in quantum optimization, Abbas et al., arXiv:2312.02279, published in Nature Reviews Physics (2024).
13. ExxonMobil and IBM maritime inventory routing case study, IBM (undated; cites 2021 data).
14. CMA CGM Group invests in Pasqal, companies to explore quantum computing for optimizing maritime and logistics operations, The Quantum Insider, July 9, 2024 (report of the companies’ announcement).
15. BMW Group and Airbus reveal winners of quantum computing challenge, BMW Group press release, December 10, 2024.