A grand experiment: Quantum theory and practice

A quantum computer’s basic building block is the quantum bit, or qubit. In a classical computer, a bit can store either a 0 or a 1. A qubit can store not only 0 or 1 but also an in-between state called a superposition—which can assume lots of different values. One analogy is that if information were color, then a classical bit could be either black or white. A qubit when it’s in superposition could be any color on the spectrum, and could also vary in brightness.

The upshot is that a qubit can store and process a vast quantity of information compared with a bit—and capacity increases exponentially as you connect qubits together. Storing all the information in the 53 qubits on Google’s Sycamore chip would take about 72 petabytes (72 billion gigabytes) of classical computer memory. It doesn’t take a lot more qubits before you’d need a classical computer the size of the planet.

But it’s not straightforward. Delicate and easily disturbed, qubits need to be almost perfectly isolated from heat, vibration, and stray atoms—hence the “chandelier” refrigerators in Google’s quantum lab. Even then, they can function for at most a few hundred microseconds before they “decohere” and lose their superposition.

And quantum computers aren’t always faster than classical ones. They’re just different, faster at some things and slower at others, and require different kinds of software. To compare their performance, you have to write a classical program that approximately simulates the quantum one.

For its experiment, Google chose a benchmarking test called “random quantum circuit sampling.” It generates millions of random numbers, but with slight statistical biases that are a hallmark of the quantum algorithm. If Sycamore were a pocket calculator, it would be the equivalent of pressing buttons at random and checking that the display showed the expected results.

Google simulated parts of this on its own massive server farms as well as on Summit, the world’s biggest supercomputer, at Oak Ridge National Laboratory. The researchers estimated that completing the whole job, which took Sycamore 200 seconds, would have taken Summit approximately 10,000 years. Voilà: quantum supremacy.

So what was IBM’s objection? Basically, that there are different ways to get a classical computer to simulate a quantum machine—and that the software you write, the way you chop up data and store it, and the hardware you use all make a big difference in how fast the simulation can run. IBM said Google assumed the simulation would need to be cut up into a lot of chunks, but Summit, with 280 petabytes of storage, is big enough to hold the complete state of Sycamore at once. (And IBM built Summit, so it should know.)

But over the decades, the company has gained a reputation for struggling to turn its research projects into commercial successes. Take, most recently, Watson, the Jeopardy!-playing AI that IBM tried to convert into a robot medical guru. It was meant to provide diagnoses and identify trends in oceans of medical data, but despite dozens of partnerships with health-care providers, there have been few commercial applications, and even the ones that did emerge have yielded mixed results.

The quantum computing team, in Gil’s telling, is trying to break that cycle by doing the research and business development in parallel. Almost as soon as it had working quantum computers, it started making them accessible to outsiders by putting them on the cloud, where they can be programmed by means of a simple drag-and-drop interface that works in a web browser. The “IBM Q Experience,” launched in 2016, now consists of 15 publicly available quantum computers ranging from five to 53 qubits in size. Some 12,000 people a month use them, ranging from academic researchers to school kids. Time on the smaller machines is free; IBM says it already has more than 100 clients paying (it won’t say how much) to use the bigger ones.