Here's an expanded version of my news story for Nature on Google's new quantum computer. It's a somewhat complicated story, so a bit more explanation might be useful.
Combining the best of two leading approaches might be the way to make a full-scale multipurpose quantum computer.
A universal quantum computer, which can any computational problem, has been a goal of research on quantum computing since its origins three decades ago. A team in California has now made an experimental prototype of such a device. It uses nine solid-state quantum bits (qubits), which can be configured to solve a wide range of problems and has the potential to be scaled up to larger systems.
The new device was made by Rami Barends and coworkers at Google’s research laboratories in Santa Barbara, collaborating with the group of physicist John Martinis at the University of California at Santa Barbara and with a team at the University of the Basque Country in Bilbao, Spain.
“It’s terrific work in many respects, and is filled with valuable lessons for the quantum computing community”, says Daniel Lidar, a quantum-computing expert at the University of Southern California in Los Angeles.
The Google circuit combines some of the advantages of the two main approaches to quantum computing so far. One is to build the computer’s circuits from qubits in particular arrangements geared to an algorithm for solving a specific problem. This is analogous to a tailor-made digital circuit in a conventional microprocessor made from classical bits. Much of the theory of quantum computing is based on this digital approach, which includes methods for the all-important problem of error correction to avoid errors accumulating and derailing a calculation. But so far practical implementations have been possible only with a handful of qubits.
The other approach is called adiabatic quantum computing (AQC). Here, instead of encoding an algorithm in a series of digital-logic operations between qubits, the computer encodes the problem of interest in the states of a pool of qubits, gradually evolving and adjusting the interactions between them to “shape” their collective quantum state. In principle just about any problem can be encoded into the same group of qubits.
This is an analog rather than a digital approach, and is limited by the effects of random noise, which introduces errors that can’t be corrected as systematically as in digital circuits. What’s more, there’s no guarantee that all problems can be solved efficiently this way, says Barends.
While most research on quantum computing uses the digital approach, adiabatic quantum computing has furnished the first commercial devices, made by D-Wave Systems in Burnaby, Canada, for about $15 million apiece. Google owns a D-Wave device, but its own researchers are searching for ways to improve the method.
In particular, they wanted to find some way of implementing error correction. Without it, scaling up AQC to more qubits will be difficult, since errors will accumulate more quickly in larger systems. With that in mind, Barends and colleagues decided to combine the AQC method with the digital approach, which has a well developed theory of error correction .
“Implementing adiabatic optimization on a universal quantum computer is not a new idea”, explains Andrew Childs of the University of Maryland. “But now the Google group has actually carried this out, which makes for a nice test of their system.”
To do that, the Google team uses a row of nine qubits, fashioned from cross-shaped films of aluminium about 400 micrometres across from tip to tip, deposited on a sapphire surface. The aluminium becomes superconducting when cooled to 1.1 degrees Kelvin, in which state its electrical resistance falls to zero. (The Google team actually operates the device at just 0.02 K to reduce the thermal noise.) . This is state-of-the-art technology for qubits, Lidar says.
Superconductivity is a quantum-mechanical effect, and a bit of information – a 1 or 0 – can be encoded in different states of the superconducting current. Crucially, these quantum bits can be placed in superposition states, simultaneously encoding a 1 and 0 – the key to the power of quantum computing.
The interactions between neighbouring qubits are controlled by linking them via logic gates. Using these gates, the nine qubits can be steered step by step into a state that encodes the solutions to a problem. As a demonstration, the researchers let their array simulate a system of coupled magnetic “spins”, like a row of magnetic atoms – a problem well explored in condensed-matter physics. They can then interrogate the states of the qubits to determine the lowest-energy state of the spins they represent.
That’s a fairly simple problem to solve on a classical computer too. But the researchers show that their device is also able to handle so-called “non-stoquastic” problems, which aren’t tractable on classical computers. These include simulations of the interactions between many electrons, needed to make exact calculations in quantum chemistry. The ability to simulate molecules and materials at the quantum level could be one of the most valuable applications of quantum computing.
A great advantage of this new approach is that it allows for the incorporation of quantum error correction, says Lidar. Although the researchers didn’t demonstrate that in this work, the Google team has previously shown how error correction might be achieved on their nine-qubit device .
“Quantum error correction is needed to allow for addressing really large problems, otherwise with each qubit and coupler you add a source of noise”, says Barends’ co-author Alireza Shabani at Google. “With error correction, our approach becomes a general-purpose algorithm that is in principle scalable to an arbitrarily large quantum computer.”
The Google device is still very much a prototype. “With early small-scale devices like this one, it’s not yet possible to tackle problems that cannot be solved on traditional classical hardware”, says Lidar.
But “in a couple of years it may be possible to work with devices having more than 40 qubits”, he adds. “At that point it will become possible to simulate quantum dynamics that is inaccessible on classical hardware, which will mark the advent of ‘quantum supremacy’.”
1. Barends, R. et al., Nature doi:10.1038/nature17658 (2016) here.
2 . Kelly, J. et al., Nature 519, 66-69 (2015) here.