Quantum computing has captured imaginations for almost 50 years. The reason is simple: it offers a path to solving problems that could never be answered with classical machines. Examples include simulating chemistry exactly to develop new molecules and materials and solving complex optimization problems, which seek the best solution from among many possible alternatives. Every industry has a need for optimization, which is one reason this technology has so much disruptive potential. 

Until recently, access to nascent quantum computers was restricted to specialists in a few labs around the world. But progress over the past several years has enabled the construction of the world’s first prototype systems that can finally test out ideas, algorithms and other techniques that until now were strictly theoretical.

Quantum computers tackle problems by harnessing the power of quantum mechanics. Rather than considering each possible solution one at a time, as a classical machine would, they behave in ways that cannot be explained with classical analogies. They start out in a quantum superposition of all possible solutions, and then they use entanglement and quantum interference to home in on the correct answer—processes that we do not observe in our everyday lives. The promise they offer, however, comes at the cost of them being difficult to build. A popular design requires superconducting materials (kept 100 times colder than outer space), exquisite control over delicate quantum states and shielding for the processor to keep out even a single stray ray of light. 

Existing machines are still too small to fully solve problems more complex than supercomputers can handle today. Nevertheless, tremendous progress has been made. Algorithms have been developed that will run faster on a quantum machine. Techniques now exist that prolong coherence (the lifetime of quantum information) in superconducting quantum bits by a factor of more than 100 compared with 10 years ago. We can now measure the most important kinds of quantum errors. And in 2016 IBM provided the public access to the first quantum computer in the cloud—the IBM Q experience—with a graphical interface for programming it and now an interface based on the popular programming language Python. Opening this system to the world has fueled innovations that are vital for this technology to progress, and to date more than 20 academic papers have been published using this tool. The field is expanding dramatically. Academic research groups and more than 50 start-ups and large corporations worldwide are focused on making quantum computing a reality.

With these technological advancements and a machine at anyone’s fingertips, now is the time for getting “quantum ready.” People can begin to figure out what they would do if machines existed today that could solve new problems. And many quantum computing guides are available online to help them get started.

There are still many obstacles. Coherence times must improve, quantum error rates must decrease, and eventually, we must mitigate or correct the errors that do occur. Researchers will continue to drive innovations in both the hardware and software. Investigators disagree, however, over which criteria should determine when quantum computing has achieved technological maturity. Some have proposed a standard defined by the ability to perform a scientific measurement so obscure that it is not easily explained to a general audience. I and others disagree, arguing that quantum computing will not have emerged as a technology until it can solve problems that have commercial, intellectual and societal importance. The good news is, that day is finally within our sights.