HPCwire: Quantum Computing Steps Out of the Research Lab

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February 16, 2007

Quantum Computing Steps Out of the Research Lab

by Michael Feldman, HPCwire Editor

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On Tuesday at the Computer History Museum in Mountain View, California, a Canadian tech startup called D-Wave demonstrated a prototype of a commercial quantum computer. The company claims their 16-qubit system is by far the most powerful quantum computer ever built and the first ever to run commercial applications. The purpose of the demonstration was to provide "proof-of-concept" for upcoming commercial products.

While many researchers have estimated that quantum devices will not be commercially viable for another 20 to 50 years, D-Wave founder and CTO Geordie Rose has aggressively pursued his dream of developing a commercial device in a much shorter timeframe. In 1999, he formed D-Wave to begin his pursuit of superconductor-based quantum computing. A superconductor implementation was chosen because unlike other QC approaches, such as quantum dots or optical circuits, it does not rely on the development of future technologies.

Unlike bits in digital computers, quantum computers contain quantum bits (qubits), which can exist as 0, 1, or a superposition of both. The property of superposition is at the heart of quantum computing.

The D-Wave system relies on a technology called adiabatic quantum computing to do its work. The hardware consists of a 4x4 array of magnetic flux qubits, which are implemented as niobium rings. At temperatures close to absolute zero they become superconducting, enabling them to behave quantum mechanically. Because of the quantum mechanical behavior, the 16-qubit system is able to perform 64K calculations simultaneously.

The demonstration used the D-Wave prototype system, called Orion, running remotely at the company's headquarters in Burnaby, Canada. Three different applications were put through their paces. The first was a pattern matching application used to search a databases of molecules. The second was a seating plan application, where wedding seat assignments were subject to a number of constraints. The third application demonstrated solutions to the Suduko puzzle.

The algorithms were adapted such that they were recast as combinatorial graphs. A conventional digital preprocessor ran the applications, but the graphs were sent to the QC hardware, where they were distributed across the qubit array.

If this sounds like a lot of trouble for searching a database or assigning some seats, the real payoff comes when the system is scaled up to thousands of qubits. Quantum computers of this size should be able to solve problems that cannot be solved by any conventional computer, no matter how large powerful.

"There are problems out there that just don't scale polynomially, they scale exponentially," says D-Wave CEO Herb Martin.

He is referring to NP-complete problems, which require examining a very large number of possibilities. For these types of problems, computation time on a conventional digital computer goes up exponentially as the number of combinations increases. An example is the subset sum problem, which is important to cryptography. The problem may be stated as follows: for a given set of integers, does a subset of the numbers exist, which when added together, equals zero? For example, in the set {-7, -3, -2, 5, 8}, the subset {-3, -2, 5} is the solution. A digital computer would be able to determine this in a fraction of a second. However, if the given set of numbers grew to a couple of hundred elements, it would take billions of years for the computer to solve it. A quantum computer of reasonable size could solve it almost instantly.

Or could it? D-Wave's Geordie Rose admits that using quantum computers to achieve exact solutions to NP-complete problems is unproven. D-Wave's specific claim is that these systems will be able to derive very useful "approximate solutions" for such applications, where the problem does not require an exact solution.

Virtually any industry has applications that could make use of this capability. This applies to most real-world problems where the number of combinations limits how fast a conventional computer can generate a useful solution. Applications like protein folding, drug discovery, genomics, machine vision, security biometrics, quantitative finances, data mining, VLSI layout, nanoscale simulation, supply chain management, and many others can be re-cast as QC-native algorithms. All of these problems are currently being addressed with conventional computers, but the scale of the algorithm will always be limited by the digital nature of the computation.

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