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.
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