**By solving problems in the haulage and transportation sectors, quantum computing could reduce greenhouse emissions and help countries meet Paris Agreement targets.**

*Image Credit: Travel mania/Shutterstock.com *

Many countries are facing significant problems with logistics, the process of managing how resources are acquired, stored and transported to their final destination.

Calculating how to transport a required volume of products and materials with a limited amount of vehicles and operators is no easy task, as events unfolding in the United Kingdom are a stark reminder.

As this rapidly growing sector becomes more and more essential to our way of life, the pressure that the transportation sector is placing on the environment has never been greater.

At a time when governments are struggling to meet the targets set by the Paris Agreement, of keeping the rise in average global temperature below 2⁰C, the pressure is being transferred to the transportation sector.

In the United States, the Environmental Protection Agency (EPA) is working towards the roll-out of a new generation of cleaner, more fuel-efficient trucks by encouraging the development and deployment of new and advanced cost-effective technologies. The agency hopes that this could help lower CO2 emissions by approximately 1.1 billion metric tons.

Getting these new, cleaner, vehicles into circulation will only occur as quickly as companies seek to replace older vehicles, meaning this is by no means a “quick fix.”

That means that in the meantime, logistics and haulage companies must seek out a more immediate solution.

New research published in the journal *Computational Logistics *and authored by Sheir Yarkoni, a quantum computing researcher at Volkswagen AG, Leiden University, Bayern University, suggests that the quantum revolution occurring in computing could assist in the solution of logistics problems.

## What is Quantum Computing?

Unsurprisingly, given the name, quantum computing is a technology that is founded upon some of the phenomena described in quantum physics, a revolution in our understanding of the very small that occurred in the early years of the twentieth century.

In particular, quantum computing relies on some of the more counterintuitive aspects of quantum theory. This includes entanglement, the idea that two particles can be linked in such a way that changing one causes an instantaneous change in the other — even if they are at opposite ends of the Universe.

Quantum computing also relies on superposition, the idea that a quantum system can occupy two or more different states at the same time, even if these states are contradictory.

The most famous example of this is the idea that a traveling particle can’t be found on a straight-line path to its destination. In quantum physics there is no “trajectory,” a traveling particle takes an infinite number of paths to its target, meaning technically at any point between point A and point B, it could be anywhere in the Universe. However, as quantum mechanics hinges on probabilities, some paths are much more probable than others.

*Image Credit: Bartlomiej K. Wroblewski/Shutterstock.com*

The quantum state of a system is modeled with waves, which means to describe all possibilities these wavefunctions are placed on top of each other. This means that the peaks and troughs of each wave act to either accentuate each other or to cancel each other out, known as constructive interference and destructive interference respectively. This is another vital part of quantum computing.

Just like computing is founded upon an essential unit, the bit, quantum computing relies on qubits. The advantage qubits have over conventional bits is whereas a bit can be envisioned as a “yes” or “no” switch, a qubit is a sphere that can make almost infinite “yes” and “no” or “on” and “off” determinations.

You might quite rightly be wondering, with the massive boosts in computers offered by quantum technology, why it isn’t more widely adopted?

The problem is that quantum systems are easily disturbed. This means that quantum systems are extremely vulnerable to temperature fluctuations or magnetic fields.

As companies like IBM and Microsoft work to overcome these hindrances, the delicacy of quantum systems hasn’t stopped researchers from looking for quantum solutions to real-world problems: problems like logistics.

## Quantum Solutions to Real World Logistics

In the *Computational Logistics *study, the authors describe the problem they applied a quantum computing solution to.

“*We consider the scenario of partially filled trucks transporting shipments between a network of hubs*,” the authors say. “*By selecting alternative routes for some shipment paths, we optimize the trade-off between merging partially filled trucks using fewer trucks in total and the increase in distance associated with shipment rerouting.*”

The team of researchers goes on to explain that the goal of the optimization conducted was to shorten the distance traveled by a fleet of vehicles transporting shipments.

Considering that the approach they suggested would have to be validated in the “real world” the researchers tested it against data collected from a shipping network that operates in Europe.

The team constrained the problem as something called a quadratic unconstrained binary optimization (QUBO) problem — a broad class of optimization problems with a range of practical applications.

Solving these problems with classic computing requires an exponential amount of time, but the growing field of quantum computers — with its vastly boosted computational power — can radically speed up the process.

The researchers found that the key to solving the QUBO logistics was actually an amalgamation of classical computers with a quantum computing algorithm.

## References and Further Reading

Yarkoni. S., Huck. A., Schülldorf. H., *et al, *[2020], *‘*Solving the Shipment Rerouting Problem with Quantum Optimization Techniques.’ *Computational Logistics, *[https://doi.org/10.1007/978-3-030-87672-2_33]

Guerreschi. G. G., [2020], ‘Solving Quadratic Unconstrained Binary Optimization with divide-and-conquer and quantum algorithms,’ https://arxiv.org/abs/2101.07813v1

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