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A D-Wave quantum processor learned to tell trees from rivers, fields, and roads.

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Quantum computer learns to ‘see’ trees

Scientists have trained a quantum computer to recognize trees. That may not seem like a big deal, but the result means that researchers are a step closer to using such computers for complicated machine learning problems like pattern recognition and computer vision.

The team used a D-Wave 2X computer, an advanced model from the Burnaby, Canada–based company that created the world’s first quantum computer in 2007. Conventional computers can already use sophisticated algorithms to recognize patterns in images, but it takes lots of memory and processor power. This is because classical computers store information in binary bits–either a 0 or a 1. Quantum computers, in contrast, run on a subatomic level using quantum bits (or qubits) that can represent a 0 and a 1 at the same time. A processor using qubits could theoretically solve problems exponentially more quickly than a traditional computer for a small set of specialized problems. The nature of quantum computing and the limitations of programming qubits has meant that complex problems like computer vision have been off-limits until now.

In the new study, physicist Edward Boyda of St. Mary’s College of California in Moraga and colleagues fed hundreds of NASA satellite images of California into the D-Wave 2X processor, which contains 1152 qubits. The researchers asked the computer to consider dozens of features—hue, saturation, even light reflectance—to determine whether clumps of pixels were trees as opposed to roads, buildings, or rivers. They then told the computer whether its classifications were right or wrong so that the computer could learn from its mistakes, tweaking the formula it uses to determine whether something is a tree.

“Classification is a tricky problem; there are short trees, tall trees, trees next to each other, next to buildings—all sorts of combinations,” says team member Ramakrishna Nemani, an earth scientist at NASA’s Advanced Supercomputer Division in Mountain View, California.

Satellite photos of California landscapes (top); in green, what the D-Wave recognized as trees (bottom).

E. Boyda, et. al., PLOS ONE 12, 2 (27 February 2017) PLOS

After it was trained, the D-Wave was 90% accurate in recognizing trees in aerial photographs of Mill Valley, California, the team reports in PLOS ONE. It was only slightly more accurate than a conventional computer would have been at the same problem. But the results demonstrate how scientists can program quantum computers to “look” at and analyze images, and opens up the possibility of using them to solve other complex problems that require heavy data crunching.

For example, Nemani says the study lays the groundwork for better climate forecasting. By poring over NASA’s satellite imagery, quantum processors could take a machine learning approach to uncover new patterns in how weather moves across the world over the course of weeks, months, or even years, he says. “Say you’re living in India—you might get an advance notice of a cyclone 6 months ahead of time because we see a pattern of weather in northern Canada.”

But it will take a great deal of work before quantum computing is the norm in solving complex computational problems. “There’s a popular belief that quantum computers do things that classical computers cannot, but the only difference is speed,” says Itay Hen, a computer scientist at the University of Southern California in Marina del Rey, who was not involved with the research. “This particular work hasn’t shown that the D-Wave device can beat standard computers in that.” Hen points out that in researchers’ search for ways to harness the power of quantum computing, some applications might be dead ends. “A machine learning application, like the one in the paper, is one direction” for quantum computers, Hen says. “But it’s unclear whether or not there’s hope there.”