Telefónica and Würth Test Quantum Computing to Optimize Logistics Packaging

Quantum computing is often discussed in logistics in connection with vehicle routing, scheduling and supply-chain optimization. But one of the most practical applications may begin much earlier in the logistics process: deciding how products should be packed before they are transported.

On July 23, 2026, Telefónica and Würth announced the results of an industrial project using quantum computing and artificial intelligence to optimize logistics packaging at Würth's logistics center in Agoncillo, La Rioja, Spain. The project was developed with TECNALIA and QCentroid and focused on the three-dimensional bin-packing problem.

The problem is straightforward to describe but difficult to optimize at scale.

A logistics operation may need to determine which box should be used for an order and how the individual products should be positioned inside it. When an order contains products of different sizes and shapes, finding an efficient arrangement becomes a combinatorial optimization problem.

For Würth, the challenge is particularly relevant because its logistics operation handles thousands of different product references, including tools, fasteners and consumables.

The July announcement provides one of the clearest examples this year of quantum computing being tested against a specific physical logistics problem rather than remaining solely at the theoretical or laboratory stage.

From routing to packaging

Much of the discussion around quantum logistics has focused on transportation routes.

The vehicle-routing problem asks how vehicles should travel between multiple destinations while satisfying constraints such as capacity and operating requirements.

Packaging creates a different optimization challenge.

The objective is to determine how physical products can be placed inside available containers while using space efficiently.

This is known as the 3D Bin Packing Problem.

According to TECNALIA, the Würth project involved real B2B order-preparation and dispatch operations, with thousands of product references and orders containing combinations such as loose screws and larger tools. The system was designed to determine appropriate box selection and three-dimensional product arrangements.

That makes the use case particularly relevant to logistics.

Packaging decisions affect what happens later in the transportation chain.

If an order requires more boxes than necessary, the shipment may occupy more physical space. More packaging can also mean additional cardboard consumption and additional handling.

The project therefore examines optimization at a point in the logistics process that directly precedes transportation.

How the system works

The project does not rely on a quantum computer to run the entire logistics operation.

Instead, it uses a hybrid quantum-classical approach.

Telefónica's earlier documentation for the Würth use case identifies a combination of technologies including a D-Wave quantum annealing system, a TECNALIA solver and QCentroid's quantum framework.

The underlying optimization problem is divided into computational components.

The system needs to determine which boxes are appropriate and how products should be arranged within them. 

The output is intended to provide an actionable recommendation for the logistics operation, including recommended boxes, three-dimensional distributions and alternatives based on operational requirements.

 

TECNALIA describes considerations such as fragility, priority, speed and robustness within the solution.

This hybrid model is important because current quantum computers are not generally used as standalone replacements for conventional logistics software.

Classical computing remains responsible for many parts of the workflow, while quantum techniques can be applied to selected optimization tasks.

That is a more realistic representation of how quantum computing can currently be incorporated into logistics technology.

What happened in the Würth pilot

Telefónica reports that the project evaluated more than 6,000 orders.

According to the company's July 23 announcement, packaging efficiency was improved across 14% of the orders processed. Telefónica reports that the optimization produced approximately a 3% reduction in the number of boxes used, an approximately 7% reduction in truck transport volume, and a reduction of more than 6% in cardboard consumption.

These figures are important, but they need to be interpreted correctly.

They are results reported by the organizations involved in the project. They are not independent industry-wide measurements demonstrating that quantum computing will deliver the same improvements across other warehouses or logistics networks.

The 14% figure also refers to the portion of the evaluated orders for which the reported packaging efficiency improvement occurred. It should therefore not be described as a 14% improvement across the entire logistics operation.

This distinction matters when evaluating emerging technology.

A pilot can demonstrate that an approach works for particular problem instances without proving that the same approach will deliver identical results under different operational conditions.

Why packaging optimization matters to transportation

Packaging may appear to be a warehouse issue rather than a transportation issue.

In practice, the two are closely connected.

The physical dimensions of shipments determine how much space cargo occupies during transportation.

If packages are larger than necessary, trucks and other transportation assets may carry more empty space.

A better packing arrangement can potentially allow products to be consolidated more efficiently.

This does not automatically mean that every improvement in packaging will translate into an equivalent reduction in transportation costs. The actual impact depends on factors including vehicle loading patterns, shipment consolidation, delivery schedules and other operational constraints.

Nevertheless, the relationship between packaging and transportation volume is direct enough to make packaging optimization a meaningful logistics problem.

Telefónica's reported pilot results specifically connect fewer boxes with reduced transport volume and cardboard consumption.

The project therefore illustrates how optimization can influence several stages of a supply chain at once.

Why quantum computing is being considered

The central reason researchers investigate quantum computing for problems such as 3D bin packing is the number of possible combinations.

As the number of products, boxes and constraints increases, the number of possible arrangements can become very large.

Traditional optimization techniques are already capable of solving many packaging problems.

That is an important point.

The Würth project does not mean that classical optimization has become incapable of handling packaging decisions.

Instead, the project investigates whether quantum and hybrid algorithms can identify useful improvements in complex combinations of products and packaging constraints.

TECNALIA describes the project as an assessment of whether quantum and hybrid algorithms can produce measurable improvements over manual or heuristic approaches for certain order profiles.

This is a more precise description than saying quantum computing has "solved" logistics packaging.

The technology is being tested against a particular optimization problem under particular conditions.

The role of artificial intelligence

Quantum computing is only one component of the project.

Telefónica describes the initiative as combining quantum computing with advanced artificial intelligence algorithms.

This is significant because logistics optimization frequently depends on multiple types of computation.

Artificial intelligence can help process information and identify patterns, while optimization algorithms can search for feasible combinations under defined constraints.

Quantum computing can potentially be introduced into specific optimization stages.

The result is a hybrid architecture rather than a single technology replacing the existing logistics system.

This approach is also consistent with other quantum-logistics research emerging in 2026.

Researchers are increasingly examining ways to combine quantum algorithms with classical optimization rather than assuming that quantum processors must handle an entire logistics workflow.

The project began before the July announcement

The July announcement was not the first time the Würth use case had been presented.

Telefónica showcased the 3D bin-packing project at Mobile World Congress in Barcelona in March 2026. Its event documentation identified the project as a collaboration with Würth and described a demonstration involving a D-Wave Advantage 2 quantum annealing system.

TECNALIA also documented the project in March, describing the three-dimensional bin-packing case study as an example of applied quantum computing being developed for industrial challenges.

The July announcement is therefore best understood as a later project milestone in which the organizations reported results from the industrial pilot.

This chronology is important because it prevents the article from incorrectly presenting July as the first demonstration of the concept.

What businesses should learn from the project

The most important lesson is not that every logistics company should immediately begin purchasing quantum-computing services.

The more useful lesson is that specific logistics optimization problems can now be tested using hybrid quantum-classical approaches against real operational data.

That changes the discussion.

Instead of asking whether quantum computing will eventually transform logistics, businesses can begin asking more specific questions:

• Which optimization problems consume the most computational resources?

• Which problems have sufficiently large solution spaces to justify testing alternative methods?

• Can quantum-assisted methods produce better solutions than the company's existing optimization approach?

• How much computing time and infrastructure would be required?

• And does any improvement translate into measurable operational value?

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These are questions that can be tested.

The Würth project provides an example of this process.

What businesses should watch next

The next important development will be independent validation and broader testing.

The reported results are encouraging within the scope of the pilot, but businesses should avoid assuming that the same percentages will automatically apply to other warehouses or product categories.

Future tests could examine larger order volumes, different product mixes and additional operational constraints.

Another important area will be comparisons against strong classical optimization methods.

The relevant question is not whether a quantum or hybrid system can produce a feasible packing arrangement.

Classical systems can already do that.

The important question is whether the hybrid approach can consistently produce better solutions, do so within commercially acceptable computational times, or provide another measurable advantage for difficult cases.

The economics will matter as well.

A technically superior solution does not automatically create a business advantage if the computational resources required to obtain it are too expensive or operationally complex.

A practical example of quantum logistics

The Würth project is valuable because it provides a tangible example of where quantum computing may fit into logistics.

The system is not controlling trucks autonomously.

It is not replacing a warehouse management system.

It is not claiming to have solved every packaging problem.

Instead, quantum and classical computing are being applied to one specific optimization challenge: determining how products can be packed efficiently.

That narrower application may actually be more representative of the technology's current stage.

Quantum computing does not need to replace an entire logistics operation to be useful.

It may first become valuable by improving selected computationally difficult decisions inside existing systems.

If those improvements can be demonstrated consistently, the technology could gradually become part of larger logistics workflows.

Conclusion

The July 23, 2026 announcement from Telefónica and Würth provides a concrete example of quantum computing being tested against a real logistics problem. The project, developed with TECNALIA and QCentroid, applies quantum and classical computing to three-dimensional bin packing at Würth's logistics operation in Spain.

Telefónica reports that the pilot evaluated more than 6,000 orders and found packaging-efficiency improvements in 14% of those orders. The company reports approximately 3% fewer boxes, up to 7% lower transported truck volume and more than 6% lower cardboard consumption in the evaluated results.

Those figures should be understood as reported pilot results, not as proof of universal quantum advantage or guaranteed savings for other logistics companies.

The more significant development is the use of real industrial data to test a hybrid quantum-classical approach against an actual packaging problem.

For businesses, the next question is whether similar results can be reproduced across larger datasets, different product mixes and other logistics environments, and how the approach compares with the strongest classical optimization methods.

The Würth case therefore represents a useful step in the development of quantum logistics: not a replacement for conventional logistics technology, but a real-world experiment testing whether quantum optimization can improve one specific and measurable part of the supply chain.

If future pilots can demonstrate repeatable advantages at larger scale, packaging optimization could become one of the areas where quantum computing finds an early practical role in logistics.