First, accepted explanations of the subatomic world turned out to be incomplete. Electrons and other particles didn’t just neatly carom around like Newtonian billiard balls, for example. Sometimes they acted like waves instead. Quantum mechanics emerged to explain such quirks, but introduced troubling questions of its own. To take just one brow-wrinkling example, this new math implied that physical properties of the subatomic world, like the position of an electron, didn’t really exist until they were observed.

If you find that baffling, you’re in good company. A year before winning a Nobel for his contributions to quantum theory, Caltech’s Richard Feynman remarked that “nobody understands quantum mechanics.” The way we experience the world just isn’t compatible. But some people grasped it well enough to redefine our understanding of the universe. And in the 1980s a few of them—including Feynman—began to wonder if quantum phenomena like subatomic particles' “don’t look and I don’t exist” trick could be used to process information. The basic theory or blueprint for quantum computers that took shape in the 80s and 90s still guides Google and others working on the technology.

Before we belly flop into the murky shallows of quantum computing 0.101, we should refresh our understanding of regular old computers. As you know, smartwatches, iPhones, and the world’s fastest supercomputer all basically do the same thing: they perform calculations by encoding information as digital bits, aka 0s and 1s. A computer might flip the voltage in a circuit on and off to represent 1s and 0s for example.

Quantum computers do calculations using bits, too. After all, we want them to plug into our existing data and computers. But quantum bits, or qubits, have unique and powerful properties that allow a group of them to do much more than an equivalent number of conventional bits.

Qubits can be built in various ways, but they all represent digital 0s and 1s using the quantum properties of something that can be controlled electronically. Popular examples—at least among a very select slice of humanity—include superconducting circuits, or individual atoms levitated inside electromagnetic fields. The magic power of quantum computing is that this arrangement lets qubits do more than just flip between 0 and 1. Treat them right and they can flip into a mysterious extra mode called a superposition.

The looped cables connect the chip at the bottom of the structure to its control system. Amy Lombard

You may have heard that a qubit in superposition is both 0 and 1 at the same time. That’s not quite true and also not quite false—there’s just no equivalent in Homo sapiens’ humdrum classical reality. If you have a yearning to truly grok it, you must make a mathematical odyssey WIRED cannot equip you for. But in the simplified and dare we say perfect world of this explainer, the important thing to know is that the math of a superposition describes the probability of discovering either a 0 or 1 when a qubit is read out—an operation that crashes it out of a quantum superposition into classical reality. A quantum computer can use a collection of qubits in superpositions to play with different possible paths through a calculation. If done correctly, the pointers to incorrect paths cancel out, leaving the correct answer when the qubits are read out as 0s and 1s.

Jargon for the Quantum Qurious What's a qubit? A device that uses quantum mechanical effects to represent 0s and 1s of digital data, similar to the bits in a conventional computer. What's a superposition? It's the trick that makes quantum computers tick, and makes qubits more powerful than ordinary bits. A superposition is in an intuition-defying mathematical combination of both 0 and 1. Quantum algorithms can use a group of qubits in a superposition to shortcut through calculations. What's quantum entanglement? A quantum effect so unintuitive that Einstein dubbed it “spooky action at a distance.” When two qubits in a superposition are entangled, certain operations on one have instant effects on the other, a process that helps quantum algorithms be more powerful than conventional ones. What's quantum speedup? The holy grail of quantum computing—a measure of how much faster a quantum computer could crack a problem than a conventional computer could. Quantum computers aren’t well-suited to all kinds of problems, but for some they offer an exponential speedup, meaning their advantage over a conventional computer grows explosively with the size of the input problem.

For some problems that are very time consuming for conventional computers, this allows a quantum computer to find a solution in far fewer steps than a conventional computer would need. Grover’s algorithm, a famous quantum search algorithm, could find you in a phone book with 100 million names with just 10,000 operations. If a classical search algorithm just spooled through all the listings to find you, it would require 50 million operations, on average. For Grover’s and some other quantum algorithms, the bigger the initial problem—or phonebook—the further behind a conventional computer is left in the digital dust.

The reason we don’t have useful quantum computers today is that qubits are extremely finicky. The quantum effects they must control are very delicate, and stray heat or noise can flip 0s and 1s, or wipe out a crucial superposition. Qubits have to be carefully shielded, and operated at very cold temperatures, sometimes only fractions of a degree above absolute zero. Most plans for quantum computing depend on using a sizable chunk of a quantum processor’s power to correct its own errors, caused by misfiring qubits.

Recent excitement about quantum computing stems from progress in making qubits less flaky. That’s giving researchers the confidence to start bundling the devices into larger groups. Startup Rigetti Computing recently announced it has built a processor with 128 qubits made with aluminum circuits that are super-cooled to make them superconducting. Google and IBM have announced their own chips with 72 and 50 qubits, respectively. That’s still far fewer than would be needed to do useful work with a quantum computer—it would probably require at least thousands—but as recently as 2016 those companies’ best chips had qubits only in the single digits. After tantalizing computer scientists for 30 years, practical quantum computing may not exactly be close, but it has begun to feel a lot closer.

What the Future Holds for Quantum Computing

Some large companies and governments have started treating quantum computing research like a race—perhaps fittingly it’s one where both the distance to the finish line and the prize for getting there are unknown.

Google, IBM, Intel, and Microsoft have all expanded their teams working on the technology, with a growing swarm of startups such as Rigetti in hot pursuit. China and the European Union have each launched new programs measured in the billions of dollars to stimulate quantum R&D. And in the US, the Trump White House has created a new committee to coordinate government work on quantum information science. Several bills were introduced to Congress in 2018 proposing new funding for quantum research, totalling upwards of $1.3 billion. It’s not quite clear what the first killer apps of quantum computing will be, or when they will appear. But there’s a sense that whoever is first make these machines useful will gain big economic and national security advantages.