Robots Learn Faster with Quantum Technology
+ “We have shown, for the first time, a learning task — or more precisely a reinforcement learning technique — that exploits the advantages of quantum technology for the machine or robot that learns a task, and for the communication with the environment that provides the feedback for the robot’s action,” explained Philip Walther, a professor of physics at University of Vienna who was the principal investigator in this research, leading a team of researchers from University of Innsbruck, the Austrian Academy of Sciences, the Leiden University, and the German Aerospace Center. “Thus, we show a machine, where quantum technology is ‘maximally exploited.’ Our experiment shows that there is a speed-up compared to classical machine learning counterparts.”
“We used quantum light — a single photon — to be processed through a nanophotonic processor,” said Walther. “In a nutshell, the light enters from the ‘left side’ is then processed quantum mechanically, which means that this photon is superimposed and thus can travel through different paths simultaneously, before escaping the chip at ‘the right side.’”
+ In practical terms, the experiment was set up with the nanophotonic chip designated into different sections in order to more accurately monitor the experimental results, as the chip processed quantum algorithms and quantum light. “The first section is the robot, which can tune its processor part for guiding the photon. The next section is the environment that affects the photon with respect to its path, and as the last step — which we implemented as a new run — is the evaluation of the environment’s influence of the photon path.”
+ This last step was done using a classical computation that results in a “policy update”, or the process of acquiring new “knowledge” for the robot. According to the team, one can think of the experiment with the analogy of an intelligent robotic agent standing at an intersection and tasked with the learning of always turning left. When this left turn is correctly achieved, it receives a reward, which further reinforces the robot’s learning process. When performed under a classical computational system, the reward would only be obtained when the robot turns left. However, when done under a quantum mechanical paradigm, the quantum robot can prepare the plausible paths simultaneously, allowing it to learn much faster. In the case of the team’s experiments, the use of quantum computing technology reduced learning times for the robotic agent by 63%, down from 270 attempts to only 100.
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