This month IBM and Google both said they aim to commercialize quantum computers within the next few years (Google specified five), selling access to the exotic machines in a new kind of cloud service. The competitors predict a new era in which computers are immensely more powerful, with dividends including more efficient routing for logistics and mapping companies, new forms of machine learning, better product recommendations, and improved diagnostic tests.

But before any of that, the first quantum computer to start paying its way with useful work in the real world looks likely to do so by helping chemists trying to do things like improve batteries or electronics. So far, simulating molecules and reactions is the use case for early, small quantum computers sketched out in most detail by researchers developing the new kind of algorithms needed for such machines.

Quantum computers, which represent data using quantum-mechanical effects apparent at tiny scales, should be able to perform computations impossible for any conventional computer. Recent advances in hardware that might be used to build them has led to a flurry of investment from companies including Microsoft, Intel, Google, and IBM (see “10 Breakthrough Technologies 2017: Practical Quantum Computers”).

“From the point of view of what is theoretically proven, chemistry is ahead,” says Scott Crowder, chief technology officer for the IBM division that today sells hardware including supercomputers and hopes to add cloud-hosted quantum computers to its product line-up in the next few years. “We have more confidence in the smaller systems for chemistry.”

Researchers have long used simulations of molecules and chemical reactions to aid research into things like new materials, drugs, or industrial catalysts. The tactic can reduce time spent on physical experiments and scientific dead ends, and it accounts for a significant proportion of the workload of the world’s supercomputers.

Yet the payoffs are limited because even the most powerful supercomputers cannot perfectly re-create all the complex quantum behaviors of atoms and electrons in even relatively small molecules, says Alán Aspuru-Guzik, a chemistry professor at Harvard. He’s looking forward to the day simulations on quantum computers can accelerate his research group’s efforts to find new light-emitting molecules for displays, for example, and batteries suitable for grid-scale energy storage.

“Right now we have to calibrate constantly with experimental data,” says Aspuru-Guzik, who pioneered methods for simulating molecules on quantum computers. “Some of that will go away if we have a quantum computer.”

Simulating the quantum effects that shape molecular structures and reactions is a natural problem for quantum computers, because their power comes from encoding data into those same challenging quantum states. The components that make up quantum computers, known as qubits, can use quantum-mechanical processes to take computational shortcuts impossible for a conventional machine. (The Canadian company D-Wave already offers a chip with quantum properties to industrial and academic researchers, but it isn’t clear whether the device offers the benefits expected of quantum computers.)

Microsoft is betting on a less mature form of quantum hardware than IBM and Google (see “Microsoft’s Quantum Mechanics”), but it has one of the most advanced efforts to develop practical quantum algorithms. Chemistry and materials science are among of its primary areas of focus. The group’s researchers have recently tried to show how hybrid systems in which a conventional computer and a small quantum computer work together could simulate chemistry.

“It has great promise for studying molecules,” says Krysta Svore, who leads Microsoft’s group working on quantum algorithms. Looking for new, practical superconducting materials is one possible application of the hybrid model that shouldn’t require very large quantum computers, she says. Conventional computers struggle to replicate the quantum behavior of electrons that underpins superconductivity.

When—or if—it comes, early success in chemistry should bode well for the future of quantum computers. Their potential for studying molecules is just one manifestation of their flair for what computer scientists call optimization problems, which involve identifying the best possible solution from many alternatives. That can mean the most stable configuration of an atom’s electrons, or the most efficient delivery route around a city.

Today, chemistry simulations may be the type of practical optimization problem researchers best understand how to pose to a quantum computer, says Chris Monroe, a professor at the University of Maryland and cofounder of the quantum computing startup IonQ. But progress is being made on understanding other applications of quantum-powered optimization, such as machine learning, and there should be many more.

Svore of Microsoft thinks that machine-learning applications could come relatively quickly. Meanwhile, breaking encryption, although a genuine threat, is one of the most distant applications of the technology, because the algorithms involved would require an extremely large quantum processor.

Monroe likens the current moment in quantum computing to the earliest days of the transistor, which had its first breakthrough success in hearing aids before going on to larger things.

“They didn’t imagine you could get 50 billion onto a chip and do all this other stuff,” he says. “We’re sort of at the hearing aid stage, where we understand a few very specific applications and need to keep exploring.”