Building a practical quantum computer has proved hard. Really hard. Despite efforts by some of the world’s top physicists and engineers and the likes of IBM, HP, and NEC, progress has been slow. Ask the experts and they’ll tell you these systems are a decade—or five—away.
Lockheed Martin, maker of the F-22 Raptor fight jet and the Mars Odyssey orbiter, bought a quantum computer from D-Wave for $10 million (sale revealed two weeks ago, but actual sale occurred Nov 2010.)
Prediction – There will be a quantum computer with over 100 qubits of processing capability sold either as a hardware system or whose use is made available as a commercial service by Dec 31, 2010
Lockheed Martin is getting help with optimization and sampling problems.
Lockheed Martin wants to see if they can get verification and validation through Dwave’s quantum computer. Lockheed wants to ensure that systems and components perform to an acceptable level and they can use optimization to achieve that goal.
Dwave partners with clients. Provide the hardware and technical knowledge and work with them to develop the software and help them to crack their hard problems that are suited to the system.
Dwave has a roadmap to achieve larger systems. The current system is not enough to crack Lockheed Martin’s core problem. But over the next 3 to 7 years Lockheed and Dwave will learn how to apply the Dwave system to the problem so that when the larger system is ready then they can run the software and the approach that they develop for the problem at that time.
Why do we create models of things that we then can’t ever practically make for real? All approximate models break down at some point, but the difference is usually small enough to still be able to inform our practical building of things. But in this case the model seems useless for anything we try to build that is more complex than a few building blocks!
There is another way to build quantum computers that DO behave like their models. There is a type of quantum computing known as Natural Quantum Computing (NQC). This is a way of using quantum systems that we CAN build, in a way that is practical, and doesn’t go against what nature intended to happen to those circles [Geordie using an analogy of perfect circles in place of qubits].
I think that we are sometimes a little too biased towards the yin theoretical description of a perfect system, hypnotized by the beauty and simplicity of our mathematical models. And we become disheartened and frustrated when real systems don’t behave like this. But this is not necessarily a problem with nature; it can also be thought of as a problem with our models! Sure, we can make nature behave more like our models by polishing those bumpy circles. But we can also make our models behave more like nature too. We can meet half way, and have the best of both worlds. And personally I’d rather build problem solving things (and models of them!) with natural bumpiness than spend my entire life trying to polish circles to an unachievable smoothness.