Most new technologies get a lot of hype, but they rarely live up to it. Quantum computing could be a striking exception. While it’s still in its early stages, quantum computing already has a wide range of valuable use cases lined up.
The number of qubits in quantum computers is growing exponentially. It wasn’t too long ago that Google achieved “quantum supremacy” in 2019 with a 53-qubit computer. Now, the company is targeting a one million qubit computer by the end of the decade.
We’re beginning to see amazing examples of quantum computing in the real world. As more types of quantum computers become commercially viable, we’re going to see even more use cases. So without further ado, here are the top 10 quantum computing applications.
Quantum Cryptography
RSA encryption is all over the place today. Over 90% of all internet connections rely on it to encrypt data. So what do you think would happen if the entire encryption scheme were to become obsolete… practically overnight?
That’s the risk that quantum computers pose.
You see, classical encryption schemes like RSA rely on how hard it is to factor large numbers. And they work great… at least under current paradigms. For example, a classical computer would take 300 trillion years to crack a RSA-2048 bit encryption.
But quantum computers are different. They can run special algorithms that classical computers cannot. One of these, Shor’s algorithm, is actually designed to factor large numbers! A perfect quantum computer would break that same RSA-2048 bit key within 10 seconds.
Of course, it’s not all doom and gloom. Quantum systems also offer a way forward. Traditional cryptography makes assumptions that certain math problems are very hard to solve. If those assumptions no longer hold, the security collapses.
But quantum cryptography isn’t based on those same assumptions. It’s based on the laws of quantum mechanics. That means it’s robust to brute force attacks, no matter how powerful the machine is. Plus, attempts to eavesdrop can be detected when they disturb the quantum states.
Drug Discovery
Simulating human biology is extremely tough. One human cell has 100 trillion atoms. Conventional computers simply can’t simulate biology at that large scale. How they store information (as bits of 0 or 1) doesn’t lend itself to the probabilistic nature of biological states.
Quantum computers, however, are physics based. They store information in quantum bits, which can be in a superposition of 0 and 1 at the same time. As a result, they can more naturally model molecular interactions.
That’s why drug discovery is one of the most lucrative quantum computing applications. Quantum computers can also simulate those interactions at a much larger scale. Scientists would be able to “test” millions of molecules at once… without actually synthesizing them.
They could pick a desired outcome, and then let the quantum computer find a shortlist of candidates. And they could do all this while pre-screening to avoid side effects as much as possible. This process, called quantum simulation, would shave months off the drug discovery timeline.
Materials Science
Quantum computers are good at modeling complex physical systems. That’s why they’re so suitable for drug discovery. But why stop there? Quantum simulation will also make waves in materials science.
Researchers could just give the quantum computer a list of properties they want. These could range from strength to conductivity to looks even. The quantum simulator would then spit out a shortlist of options.
Quantum computing could lead to breakthroughs that have been elusive for a long time. High-efficiency solar cells or room-temperature superconductors? With quantum simulation, these are no longer pipe dreams.
In turn, the cascade effects of these breakthroughs would be huge. For example, commercial solar cells today have a PV efficiency of 17% to 20%. But researchers theorize the upper bound to be nearly 50%. Quantum simulation could help us reach that bound much earlier than we could’ve dreamed.
Optimization Problems
One theme that you’ll see throughout many applications of quantum computing is the idea of optimization. Optimization problems are all around us.
These include questions like, “how do make sure the supply chain is working efficiently?” Or, “what’s the best route for a delivery person?” Or, “how can we train a neural network to drive a car?”
So how does quantum computing help? Well, it has to do with a fundamental principle of physics. Things tend to move to a state where they use the least energy. For example, hot things cool over time and objects roll down hills.
Now here’s the clever thing. We can reframe optimization problems as energy minimization problems. Quantum computers can then use a method called quantum annealing to find the lowest energy state among many possibilities.
Artificial Intelligence and Machine Learning
AI is often labeled the most influential technology of the past decade. And many believe quantum computing could be the most influential of the next decade. So it’s no surprise that many are paying close attention to the junction of the two.
Put simply, quantum computing can dramatically speed up machine learning algorithms. In machine learning, operations like “matrix inversion” and “eigenvalue decomposition” are common. (You don’t need to know the fancy math terms. Just know that these are very computationally expensive.)
Quantum computers can run special algorithms that regular computers cannot. These algorithms can solve those problems exponentially faster. So what previously took days to run could be reduced to minutes. We’d be able to use datasets that are many orders of magnitude larger than the ones we use currently.
Climate and Weather Modeling
People have always wanted to predict the weather. For ancient farmers, it was a matter of life or death. From weather stones to astronomy, we’ve devised countless methods along the way.
Today, meteorology is more sophisticated than ever—for good reason. Weather data is highly multi-dimensional. Temperature, pressure, humidity, wind speed, etc… all across different locations and times. But crunching through all this data is still tough for classical computers.
Quantum algorithms could analyze this multi-dimensional data much faster, making forecasts more accurate. They’d also open the door to more complex problems, like predicting and averting natural disasters.
Financial Modeling
A huge quantum computing application will be in the financial industry. It allows banks and funds to do what they already do… just better and faster.
For example, the Black-Scholes model is usually used for option pricing. But it has limitations and doesn’t capture all market conditions. Monte Carlo simulations are more flexible, but are taxing to run on classical computers. Quantum computers would run them exponentially faster.
On the buy side, hedge funds often aim to find arbitrage windows, where assets can be flipped for a guaranteed profit. This involves crunching through big data to find price mismatches. Classical computers can struggle with this if the data gets too “big.” But quantum search algorithms like Grover’s algorithm can find those windows quickly.
Smart Cities
On average, drivers in New York City spend 92 hours per year sitting in traffic. Combine all the licensed drivers in NYC, and that’s roughly 561,000,000 hours per year in total. Needless to say, traffic and infrastructure is a huge problem for the world’s growing urban centers.
That’s why more and more cities are undergoing “smart city” transformations. Everything will be connected. Everything will be optimized. Ultimately, smart cities are a mesh of systems—public transportation, power grids, traffic routing, public utilities, etc.
Quantum computers will play a vital role here. As you stack more systems into the mesh, the problem becomes more complex. And once you start adding future tech like self-driving cars, the problem becomes untenable for classical computers.
Telecom & Space Communications
Remember, the theme is when you see “optimization”… think quantum computers. Network optimization in telecom is a perfect example.
Routing data packets through a huge maze of connections? That’s a really complex optimization problem. Classical computers can get us “decent” solutions, but not the “best” ones. But in a $2 trillion industry, the difference between “decent” and “best” is billions of dollars.
But what’s even harder than communication? How about communication in space? Quantum technologies could play a huge role here too.
For example, a property called quantum entanglement could allow us to send secure messages across the galaxy… with zero time delay. That might sound crazy, but NASA has already shown the concept to work. As you can imagine, this is a real national security priority.
Space Exploration
We want spacecraft to be strong, light, and radiation-proof. We want them to be cheap to produce, without the need of rare minerals. And ideally, we want them to be reusable. If that sounds like daydreaming, it’s because it was… up until recently.
In the past, space missions have been flat out too expensive. Only national space programs could afford them. But advances in 3D printing and materials science have already cut costs by a lot. Quantum simulation—through materials science—will take that to the next level.
Another quantum computing application will be in mission planning. Yes, we want to reduce fuel consumption. But we also need to deal with things like gravitational fields and celestial bodies. For distant missions, tiny tweaks in trajectory can lead to huge cost savings.
As we’ve said, quantum computers are very potent at optimization. So while we already use supercomputers for mission planning… quantum computers will still be faster. They’ll dramatically expand the range of possible missions.