While robots today have become more adapted, they’re still essentially stupid – limited to a particular pre-programmed series of tasks, slow to respond to complex environments and unable to learn from past experience. The future belongs to machine learning and cognitive computing, a new field that’s set to have a great impact on our lives, but before this can happen quantum computing needs to be introduced. These machines will vastly increase computing power, which will allow scientists to perform tasks currently thought impossible like extremely complex models and simulations, and … a new line of automatons called quantum robots.
A new model performed by researchers at the Complutense University of Madrid (UCM) and the University of Innsbruck (Austria) shows that such quantum robots would be able to perform creative tasks, adapt to complex stimuli and learn based on previous tasks, unlike classic robots that exploit quantum physical phenomena.
“In the case of very demanding … environments, the quantum robot can adapt itself and survive, while the classic robot is destined to collapse,” says G. Davide Paparo and Miguel A. Martín-Delgado, two researchers from UCM who have participated in the study. Their theoretical work has focused on using quantum computing for machine learning.
“Building a model is actually a creative act, but conventional computers are no good at it,” says Martín-Delgado. “The advances [quantum computing] brings are not only quantitative in terms of greater speed, but also qualitative: adapting better to environments where the classic agent does not survive. This means that quantum robots are [in models] more creative.”
Real life situations are complex and unpredictable. Put your robot out of your living room or production line and it will stumble, crumble and fall. The researchers used the theory of a quantum random walk to show how an agent can explore its episodic memory in superposition to dramatically speed up its active learning time. Utilizing quantum physics to promote artificial intelligence learning has the ability to provide a quadratic increase in speed in active learning—critical when the environment changes on time scales of the “thinking” time of the autonomous learning agent.
It’s worth noting that the findings published in the Physical Review Letters are only based on a model. We don’t yet have quantum robots, quantum computers or some other form of quantum agent, but the model sure does make me excited about the future.