We illustrate our discussion with data from tests run on a D-Wave Two device with up to 503 qubits.
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Here, we show how to define and measure quantum speedup and how to avoid pitfalls that might mask or fake such a speedup.
The development of small-scale quantum devices raises the question of how to fairly assess and detect quantum speedup. D-Wave has responded by saying that the wrong benchmark problem was used to evaluate its processor, while the US–Swiss team now intends to do more experiments using different benchmarks. While the researchers say that their results do not rule out the possibility that the processor can outperform conventional computers when solving other classes of problems, their work does suggest that evaluating the performance of a quantum computer could be a much trickier task than previously thought. The paper, which first appeared as a preprint earlier this year, concludes that the processor – built by the controversial Canadian firm D-Wave Systems – offers no advantage over a conventional computer when it is used to solve a benchmark computing problem. There is substantial skepticism expressed in some quarters, about what is happening inside the box, about whether the D-Wave system offers a performance advantage over conventional computers.Ī team of quantum-computing experts in the US and Switzerland has published a paper in Science that casts doubt over the ability of the D-Wave Two quantum processor to perform certain computational tasks. Quantum computing is still in its infancy. The following is the video of the interview Ī note of caution. It’s more complicated than I’m making out, of course, but it’s a good layman-like example. A D Wave system would consider all of the possible solutions at the same time, then collapse down to the optimal set of player. When you start adding variables like this, the problem gets exponentially more difficult to solve.īut it’s right up the alley of a quantum computer. But since hockey is a team sport, you also have to consider how well they work when combined with other specific players.
First there’s all the individual stats, like how well they score, pass, and defend. There are a lot of variables to consider. Your task is to select the very best starting line-up from that roster of guys.
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The first is a Monte Carlo type simulation, the second is machine learning, and the third is optimization problems that would drive a regular computer nuts – or, at least, take a long time for it to process.Īn example of this type of optimization problem is this: Consider the approximately 2,000 professional hockey players in North America. It turns out that there are three broad categories of problem where your best bet is a quantum computer. The Register has published a fascinating video about Quantum Computing, an interview with D-Wave, a company which manufactures what they claim are quantum computing systems.
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