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Researchers at the University of Maryland (UMD) have trained a small hybrid quantum computer to reproduce the features in a particular set of images.
Computers based on quantum physics promise to solve certain problems much faster than their conventional counterparts. By utilizing qubits—which can have more than just the two values of ordinary bits—quantum computers of the future could perform complex simulations and may solve difficult problems in chemistry, optimization and pattern-recognition.
Large-scale quantum computers, which are an active pursuit of many university labs and tech giants, remain years away. But that hasn’t stopped some scientists from thinking ahead, to a time when quantum computers might be linked together in a network or a single quantum computer might be split up across many interconnected nodes.
The race to build larger and larger quantum computers is heating up, with several technologies competing for a role in future devices. Each potential platform has strengths and weaknesses, but little has been done to directly compare the performance of early prototypes. Now, researchers at the JQI have performed a first-of-its-kind benchmark test of two small quantum computers built from different technologies.
Consider, for a moment, the humble puddle of water. If you dive down to nearly the scale of molecules, it will be hard to tell one spot in the puddle from any other. You can shift your gaze to the left or right, or tilt your head, and the microscopic bustle will be identical—a situation that physicists call highly symmetric.