Keith Mordoff, director of Communications Information Systems & Global Solutions at Lockheed Martin, says, "Yes, we have a fully functioning quantum computer with 56 qubits, which is different from the classical methods. D-Wave uses an adiabatic or quantum annealing approach, which defines a complex system whose ground state (lowest energy state) represents the solution to the problem posed. It constructs a simple system and initializes it in its ground state (relatively straight forward for simple systems), then changes the simple system slowly until it becomes the complex system. As the system evolves, it remains in the ground state, then measures the state of the final system. And, this will be the answer to the problem posed. The change from simple system to complex system is induced by turning on a background magnetic field."
Some scientists are extremely skeptical about quantum computing and doubt that it will ever amount to anything tangible.
Artur Ekert, professor of Quantum Physics, Mathematical Institute at the University of Oxford, says physicists today can only control a handful of quantum bits, which is adequate for quantum communication and quantum cryptography, but nothing more. He notes that it will take a few more domesticated qubits to produce quantum repeaters and quantum memories, and even more to protect and correct quantum data.
"Add still a few more qubits, and we should be able to run quantum simulations of some quantum phenomena and so forth. But when this process arrives to 'a practical quantum computer' is very much a question of defining what 'a practical quantum computer' really is. The best outcome of our research in this field would be to discover that we cannot build a quantum computer for some very fundamental reason, then maybe we would learn something new and something profound about the laws of nature," Ekert says.
Gildert adds that the key area for quantum computing will be machine learning, which is strongly linked to the field of artificial intelligence (AI). This discipline is about constructing software programs that can learn from experience, as opposed to current software, which is static.
"This is radically different from how we use computing for most tasks today," Gildert says. "The reason that learning software is not ubiquitous is that there are some very difficult and core mathematical problems known as optimization problems under the hood when you look closely at machine learning software. D-Wave is building a hardware engine that is designed to tackle those hard problems, opening the door to an entirely new way of programming and creating useful pieces of code."
According to Gildert, one very important real-world application is in the field of medical diagnosis. It's possible to write a program that applies hand-coded rules to X-ray or MRI images to try and detect whether there is a tumor in the image. But current software can only perform as well as the expert doctors' knowledge regarding what to look for in those images. With learning software, the program is shown examples of X-rays or MRI scans with and without tumors, then it learns the differences itself without having to be told. With this technology, the computer can even detect anomalies that a doctor cannot see or might not even notice. And the more examples you show it, the better it gets at this task.