How will businesses use quantum computers?

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09/02/2020 Leave a comment

Quantum computers have four fundamental capabilities that differentiate them from today’s classical computers: quantum simulation, in which quantum computers model complex molecules; optimization (that is, solving multivariable problems with unprecedented speed); quantum artificial intelligence (AI), with better algorithms that could transform machine learning across industries as diverse as pharma and automotive; and prime factorization, which could revolutionize encryption.

The best way to understand the business potential of quantum computing is to see how those capabilities could tackle a variety of use cases. Certain industries have specific problems that are particularly well suited to quantum computing. In total, we’ve reviewed more than 100 nascent use cases and found that they cover a wide range of problems and sectors, including pharmaceuticals, cybersecurity, finance, materials science, and telecommunications. Our research also suggests significant diversity in the development life cycle of these applications, and in the nature of business benefit they could confer. To paint a richer picture of these dynamics at work, let’s consider four high-potential applications:

1. Cut development time for chemicals and pharmaceuticals with simulations
Scientists looking to develop new drugs and substances often need to examine the exact structure of a molecule to determine its properties and understand how it might interact with other molecules. Unfortunately, even relatively small molecules are extremely difficult to model accurately using classical computers, since each atom interacts in complex ways with other atoms. Currently, it’s almost impossible for today’s computers to simulate basic molecules that have relatively few atoms—and proteins, to cite just one example, have thousands of them. That’s why today’s scientists are forced to actually create the molecules in question (using synthetic chemistry) to physically measure their properties. Often the molecule doesn’t work as expected, entailing more synthesis and testing. Each optimization cycle is expensive and time-consuming. This is one reason why developing new drugs and chemicals is such a lengthy process.
Quantum computers are intrinsically well suited to tackle this problem, since the interaction of atoms within a molecule is itself a quantum system. In fact, experts believe that quantum computers will be able to model even the most complex molecules in our bodies. Every bit of progress in this direction will drive faster development of new drugs and other products, and potentially lead to transformative new cures.

It’s almost impossible for today’s computers to simulate basic molecules that have relatively few atoms. Quantum computers will be able to model even the most complex molecules.

2. Solve optimization problems with unprecedented speed
Across every industry, many complex business problems involve a host of variables. Where should I place robots on the factory floor? What’s the shortest route for my delivery truck? What’s the most efficient way to deploy cars, motorcycles, and scooters to create a transportation network that meets user demand? How can I optimize the performance and risk of a financial portfolio? These are just three of the many examples that business leaders confront.
Solving these problems with classical computing is an arduous, hit-and-miss process. To isolate the inputs that drive performance gains or losses, the number of variables that can be shifted in any calculation must be seriously limited. As a result, companies must make one complicated calculation after another, a costly, time-consuming process given the multiplicity of variables. But, since quantum computers work with multiple variables simultaneously, they can be used first to dramatically narrow the range of possible answers in a very short time. Classical computing can then be called in to zero in on one precise answer, and its work will still seem slow compared with that of quantum. But, since quantum has eliminated so many possibilities, this hybrid approach will drastically cut the time it takes to find the best solution.

3. Accelerate autonomous vehicles with quantum AI
It’s possible that quantum computers could speed the arrival of self-driving vehicles. At Ford, GM, Volkswagen, and other car manufacturers, and at a host of start-ups in the new mobility sector, engineers are running hours upon hours of video, image, and lidar data through complex neural networks. Their goal: use AI to teach a car to make crucial driving decisions, such as how to take a turn, where to speed up and slow down, and, crucially, how to avoid other vehicles, not to mention pedestrians. Training an AI algorithm this way requires a set of computationally intensive calculations, which become increasingly difficult as more data and more complex relationships within the variables are added. This training can tax the world’s fastest computers for days or even months. Since quantum computers can perform multiple complex calculations with multiple variables simultaneously, they could exponentially accelerate the training of such AI systems. It’s not going to happen anytime soon. Translating classical data sets to quantum ones is arduous work, and early quantum AI algorithms have resulted in only modest gains.

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