Will quantum computing end traffic jams?

May 8, 2017

Quantum computers are slowly moving beyond the research stage as commercial companies take a look at ways they might exploit the technology. Sara Castellanos at The Wall Street Journal quotes Martin Hofmann, Volkswagen chief information officer, as saying, “This technology is not futuristic. It’s a question of years until it’s commercialized, and investing right now in the technology is a big competitive advantage.”

The expectation, reports Castellanos, is that within five years quantum computers from companies including D-Wave Systems and IBM could be solving new classes of problems now beyond the capabilities of today’s supercomputers. Already, she writes, Volkswagen has employed a $15 million D-Wave quantum computer over the cloud in a traffic-optimization project involving GPS data from 10,000 taxis in Beijing. The quantum approach could optimize routes in a fraction of a second, vs. 45 minutes for a traditional computer.

Next up is a project in Barcelona that will make use of mobile apps to harness quantum computing to predict and avoid traffic jams. Castellanos quotes Hofmann as saying, “If our project succeeds and in six to eight years the technology is where it should be, then traffic jams won’t happen anymore.”

In related news, D-Wave Systems in March announced that Google, NASA, and Universities Space Research Association (USRA) have elected to upgrade their D-Wave 2X quantum computer to the new D-Wave 2000Q system, under the terms of their multi-year agreement, to support research at the NASA Quantum Artificial Intelligence Lab (QuAIL) on how quantum computing can be applied to artificial intelligence, machine learning, and difficult optimization problems.

“The new system will be the third generation of D-Wave technology installed at Ames,” said D-Wave CEO Vern Brownell. “We are pleased that Google, NASA, and USRA value the increased performance embodied in our latest generation of technology, the D-Wave 2000Q system, for their critical applications.”

For its part, IBM is targeting its IBM Q initiative to build commercially available universal quantum computing systems for a variety of application areas, including medicine and materials, supply chain and logistics, and financial services. The company says IBM Q will make facets of artificial intelligence sufficiently powerful to handle very large data sets, such as those involved in searching images or video. Further, the technology could make cloud computing more secure. The company says, “Following Watson and blockchain, quantum computing will provide the next powerful set of services delivered via the IBM Cloud platform.”

From a test-and-measurement perspective, Tektronix is addressing quantum computing with the release last month of its AWG5200 Series arbitrary waveform generators. Kip Pettigrew, product marketing manager, said that as physicists race to build high Q-bit quantum computers, they are finding that existing solutions aren’t meeting their fidelity, latency, and scalability requirements for controlling Q-bits with precision pulsed microwave signals from multiple independent RF channels. Quantum computing and its associated industries are expected to reach $26 billion by 2020, he said.

The new AWG5200 Series represents Tektronix’s latest initiative to address the challenges. It includes a flexible waveform generation plug-in suite with comprehensive coverage for a variety of standards and digital modulation techniques. It also lowers the cost of ownership for complex multisignal environments starting at a list price of about $11,000 per channel for the eight-channel instrument.

Castellanos in the Journal notes that barriers remain to commercializing quantum computing. She quotes Dario Gil, vice president for science and solutions for IBM Research, as calling the commercialization of the technology “phenomenally difficult from a tech perspective from every aspect”—extending from materials to error detection.

However, she concludes by quoting Paul Warburton, a professor of nanoelectronics at University College London: “From the commercial point of view, it’s a question of not wanting to be left behind when the real machine arrives.”

About the Author

Rick Nelson | Contributing Editor

Rick is currently Contributing Technical Editor. He was Executive Editor for EE in 2011-2018. Previously he served on several publications, including EDN and Vision Systems Design, and has received awards for signed editorials from the American Society of Business Publication Editors. He began as a design engineer at General Electric and Litton Industries and earned a BSEE degree from Penn State.

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