Imagine you’re traveling for work. You need to leave Chattanooga, visit Knoxville, Nashville and Memphis, then make it back home before the end of the week. There are several ways to make the trip, but one route gets you home using the least time and distance. Easy enough, right?
Now imagine the trip gets more complicated. Instead of four cities, you have 24. Some meetings have to happen at certain times. Others depend on stops you’ve already made. A flight is delayed. Suddenly, one change ripples through the rest of your itinerary.
Finding a route may still be easy. Finding the best route becomes much harder.
Still following? Good. Because you may not have had “learn computer science” on today’s bingo card, but you’ve just met one of the field’s most famous problems: the Traveling Salesman Problem (TSP).
The TSP has fascinated researchers for decades, but not because anyone is especially concerned about whether the salesperson gets home on time. Instead, it captures a much bigger challenge, one appearing when the number of possible solutions grows faster than we can compare them.
We’re talking about optimization, which (thankfully) already has a familiar meaning. In business, it’s about operating more efficiently. In computer science, it’s about finding the best solution among many possibilities, whether that’s the shortest route, the lowest cost, or the smartest schedule. As those possibilities multiply, finding the best answer becomes dramatically harder.
So, what does this have to do with quantum?
A lot, actually.
Optimization problems show up everywhere. Hospitals coordinate patients, staff and operating rooms. Delivery companies decide which packages go on which trucks and in what order. Different industries, same challenge.
The good news is that classical computers are far from obsolete. In fact, the computers we use every day, like the ones powering your laptop and your cell phone, already solve many of today’s optimization problems remarkably well. They’re practical workhorses, built to find good solutions quickly.
Trouble begins when a problem demands the absolute best answer from a web of interconnected decisions. Add more deliveries, more employees, more overlapping constraints, and the number of possible solutions explodes.
Classical computers navigate the massive web of interconnected decisions in two ways. They can explore different paths one at a time, or they can use clever shortcuts to narrow the search and avoid unnecessary work. Eventually, even the shortcuts aren’t enough, and finding the best answer becomes impractical.
Enter: quantum computing.
Quantum computers could change the game by using the laws of physics to tackle enormous webs of possible solutions all at once instead of one by one. This fundamentally different way of processing information could unlock some of the world’s most complex optimization problems.
There’s a catch, though. Today’s quantum computers can only handle so much. They’re still small, prone to errors, and limited in their capacity. Many of the problems researchers hope to solve require more resources than today’s hardware can provide.
Before you reach for a bigger bag…
Let’s go back to the work trip. You’ve packed, but your suitcase is full. You have two options. Buy a bigger suitcase, or get better at packing the one you already have. Fold smarter, use every inch, and fit more into the same space.
This is (basically) the choice facing quantum researchers today.
The first option is the bigger suitcase: build larger, more stable quantum computers capable of tackling bigger problems. It’s the direction receiving most of the attention, and researchers around the world are making steady progress.
The second option is packing smarter.
Before a problem can run on a quantum computer, it has to be translated in a way the machine understands. There are many ways to do this, and some approaches are more efficient than others. Pack a problem efficiently enough, and today’s hardware has room for bigger challenges.
Both approaches matter. Bigger hardware expands what’s possible. Better algorithms make better use of the hardware we already have.
While much of the field is building bigger quantum computers, researchers at the University of Tennessee at Chattanooga are focused on building smarter quantum algorithms.
How much more can today’s machines do if we pack smarter?
Packing the problem down to size.
Quantum algorithms, one of the core research pillars at the UTC Quantum Center, are the instructions telling a computer how to solve a problem. It’s the “packing smarter” side of quantum computing, and it’s where UTC researchers have made an important contribution.
UTC researchers started with the Traveling Salesman Problem, a common way researchers compare new ideas because its complexity grows so quickly. They developed a new algorithm to solve this complex problem using a fraction of the quantum computer’s limited resources.
The problem didn’t change. The packing did.
For the problems the team tested, UTC’s approach outperformed existing methods in both efficiency and accuracy, showing the approach could extend beyond the lab.
The next step was applying the same resource-efficient approach to a real industry challenge.
Working with the University of Hamburg and Lufthansa Industry Solutions, the technology arm of one of the world’s largest airlines, UTC researchers tackled a familiar airport problem: deciding which arriving aircraft should use which gate.
Every gate assignment affects the next, and every delay creates new decisions. Get it right, and passengers walk less, make their connections, and move through the terminal without friction. Get it wrong, and everyone feels it.
This is optimization in its natural habitat—messy, consequential, and complex. Using their more efficient packing method, UTC reduced the quantum resources needed to tackle the airport gate assignment problem by roughly 90%.
Same problem, using only a fraction of the resources.
Quantum computers aren’t assigning airport gates today, but that wasn’t the goal. UTC showed that smarter algorithms, better ways of “packing” problems, can dramatically reduce the quantum resources needed to tackle complex optimization challenges.
As larger, more capable quantum computers come online, UTC is helping ensure they’re ready to solve meaningful real-world problems from day one.
From problems to possibilities.
The airline gate assignment problem is less about airplanes than it is about using limited resources efficiently. Just like the Traveling Salesman Problem isn’t really about the salesperson, it’s about finding the best solutions among countless possibilities.
These are classic optimization problems, the kind businesses work to solve every day. Planning delivery routes. Managing supply chains. Scheduling hospital staff. Balancing power across an electric grid. Different industries, same underlying challenge.
Better solutions create real advantages.
Even small improvements can save time, reduce costs, use fewer resources, and make entire systems work more efficiently. Multiply those gains across thousands or millions of decisions, and the impact grows quickly.
Quantum computing has become one of the world’s biggest technology races because solving these kinds of problems could transform entire industries. Hardware alone won’t unlock that promise. Every machine, no matter how powerful, still depends on the algorithms telling it what to do and how to do it.
This is where UTC’s work matters most.
By finding smarter ways to represent complex optimization problems, UTC researchers are making quantum computers more capable while preparing future machines for meaningful real-world work.
The problems aren’t getting any smaller, and UTC is helping ensure quantum computers are ready to carry them.
Learn more
The gap between quantum and reality: Soap bubbles, windstorms, and real infrastructure
Why quantum? Why now? And why UTC?
