Quantum-bits a thousand times more stable

April 11, 2016 // By Graham Prophet
In a step towards the goal of quantum computing, a group of researchers at MIT have devised a feedback technique to stabilise quantum bit (qubits) and extend the time over which superposition states can be maintained.

Mechanisms for controlling the media in which the trapped particles, whose quantum states are of interest, have had to be open-loop: because closed-loop implies measurement and measurement disrupts quantum states. The MIT group reports that it has found a way to implement feedback control that does not depend on making a measurement of the ntu state of the particles. This feedback technique used on diamond “qubits” could make quantum computing more practical.

Quantum computers are, as the statement from MIT expresses it, “largely hypothetical” devices that could in principle perform some calculations much more rapidly than conventional computers, exploiting quantum superposition, which describes a quantum particle’s counter-intuitive ability to, in some sense, inhabit more than one physical state at the same time.

But superposition is fragile, and finding ways to preserve it is one of the chief obstacles to developing large, general-purpose quantum computers. As reported in Nature, MIT researchers describe a new approach to preserving superposition in a class of quantum devices built from synthetic diamonds. The work could ultimately prove an important step toward reliable quantum computers.

In most engineering fields, the best way to maintain the stability of a physical system is feedback control. The problem with using this technique to stabilise a quantum system is that measurement destroys superposition. So quantum-computing researchers have traditionally had to do without feedback.

“Typically, what people do is to use what’s called open-loop control,” says Paola Cappellaro, the Esther and Harold Edgerton Associate Professor of Nuclear Science and Engineering at MIT and senior author on the new paper. “You decide a priori how to control your system and then apply your controller and hope for the best — that you knew enough about your system that the control you applied will do what you thought it should. Feedback should be more robust, because it lets you adapt to what’s going wrong.”