For years, quantum computing has existed in an uncomfortable space between fascination and exaggeration. For some, it is the next great technological revolution; for others, it is a promise too distant to deserve much attention. The truth, as is often the case with important technologies, lies somewhere in the middle: we are not dealing with science fiction, but neither are we facing a tool that will transform logistics overnight. What matters today is not waiting for miracles, but clearly understanding what quantum computing can contribute, what kinds of problems it could help solve, and how companies should prepare realistically.
To begin with, it is worth explaining what quantum computing is without turning it into a spectacle. Classical computing — the kind that supports our ERPs, TMS, WMS and planning systems, works with bits that take values of either 0 or 1. Quantum computing works with qubits, which can represent combinations of states thanks to properties such as superposition and entanglement. This does not mean that it magically “tests all solutions at once,” but it does mean that it can structure certain problems in a very different way from traditional computing. It is also important to remember that today’s quantum computers are still rudimentary and prone to errors, and that they will not replace classical computers, but will most likely work alongside them.
Google’s Quantum Computer Achieves Quantum Supremacy Again – https://www.youtube.com/shorts/gH4SsABvqP0
Why does all this matter in logistics?
Because logistics is full of problems where the number of possible combinations grows extremely quickly. We are not only talking about “moving goods from point A to point B,” but about deciding routes with constraints related to time, capacity, cost, priority, traffic, emissions, fleet availability, delivery windows and last-minute changes. On top of this, there are decisions involving network design, hub location, inventory allocation, vehicle loading, operational sequencing and resource planning. A recent academic review on quantum computing in logistics and supply chain summarizes this map of applications precisely: routing, logistics network design, fleet optimization, cargo loading, prediction, inventory management and operational scheduling.
Nor should we forget one of the most promising applications: the Physical Internet. This revolutionary model seeks to apply the logic of digital networks, sending goods in standardized, shared containers as if they were data packets, to create a hyperconnected global network capable of eliminating empty routes and drastically reducing emissions. I have written about this fascinating paradigm in previous articles available on my website.
Classical Computing vs. Quantum Computing
This is where the truly useful difference between classical and quantum computing applied to supply chain appears. Classical computing will continue to be excellent for executing operations, processing transactions, automating flows and solving a large part of everyday problems with heuristics, advanced optimizers and artificial intelligence. Quantum computing is not here to replace that operational muscle. In the best-case scenario, it will become an accelerator for certain classes of extremely complex problems, especially those involving combinatorial optimization and advanced simulation.
Microsoft puts it very clearly: in practice, quantum computing is hybrid; in other words, it combines classical and quantum processing within the same architecture.
How Amazon Plans To Catch Google And Microsoft In The Quantum Computing Race –
Quantum Computing Today: Applications in Logistics
In the logistics sector, route optimization is one of the most promising fields. A clear example is the Capacitated Vehicle Routing Problem, or CVRP, a classic problem in operations research that accurately reflects the complexity of the real world: designing efficient routes for a fleet with load constraints, while minimizing costs and respecting operational limits.
For this reason, it makes sense that research published in 2026 by Unisys focused on this challenge using a commercial quantum annealing platform. Although this milestone does not prove that quantum computing already outperforms classical computing in real large-scale operations, it does mark a turning point: the sector is moving beyond the purely theoretical field and entering a phase of applied testing on tangible transportation and distribution challenges.
Beyond routing, quantum computing could be especially relevant for complex simulations. In supply chain, good simulation is extremely valuable; it makes it possible to test disruptions, bottlenecks, sudden changes in demand, geopolitical tensions, energy disruptions or cost variations before they affect real operations.
DHL, for example, identifies real-time data-based scenario simulation as one of the areas where it sees potential for logistics, together with applications in pricing and the optimization of warehouse layouts and flows. These possibilities should be read as exploratory horizons, not as guaranteed benefits. Even so, they help us visualize where quantum computing could generate value: in those areas where complexity makes accurate anticipation almost as important as fast execution.
https://lot.dhl.com/quantum-leap-logistics/
What Can and Cannot Be Expected from Quantum Computing?
The central value of this debate lies in its ability to moderate expectations without diminishing the technology’s potential. Academic reviews in the field of logistics and supply chain insist on two realities: most current solutions adopt hybrid approaches, and hardware limitations still prevent practical large-scale implementation. They also emphasize that today’s systems are still in a trial-and-error phase. Even so, an ambitious roadmap has already been drawn up, pointing toward fault-tolerant quantum computing by 2029, the year in which true quantum advantage is expected to reach the market. In short, the advances are real, but the technology is still in an early stage.
The Quantum Paradox: What Supply Chain Leaders Need To Know –
So, What Is a Reasonable Time Horizon for the Logistics Sector?
A cautious reading of current sources suggests three different stages:
- The first is the immediate present: learning, technology monitoring and controlled experimentation.
- The second, over the next few years, points toward hybrid pilots in very specific, high-value use cases, especially in difficult optimization.
- The third, further ahead, would be a more mature integration into enterprise software, if error correction, hardware scalability and algorithm quality continue to progress.
It is not a revolution for the next quarter, but neither is it a conversation that can be postponed indefinitely.
The good news is that preparing today does not mean spending huge budgets or rebuilding the entire technology architecture. How can companies do it?
- By starting to identify which operational problems are truly complex and costly: routing with multiple constraints, network design, capacity allocation, sequencing or scenario simulation.
- To improve data quality and the ability to model constraints, because no future optimizer, classical, quantum or hybrid, will solve a poorly formulated problem effectively.
- Finally, the need to develop internal judgment: understanding what hype is, what is a pilot, what is a roadmap and what could become a competitive advantage. Intelligent preparation does not begin by buying technology; it begins with asking the right business questions.
In that sense, quantum computing is very much like modern logistics itself: it does not reward those who get carried away by the excitement of the moment, but those who know how to coordinate complexity with vision, method and timing.
Companies that expect immediate results will probably be frustrated. Those that dismiss it as fantasy may arrive too late. Those that start now to observe, learn and select use cases intelligently will have an important advantage when this technology stops being a laboratory experiment and begins to become a decision-making infrastructure.
The future of logistics will not belong to those who chase technological noise, but to those who know how to recognize in time which innovation is not yet ready, and which one is already knocking at the door.
