Quantum physics has some spooky, anti-intuitive effects, but it could also be essential to how actual intuition works, at least in regards to artificial intelligence.
In a new study, researcher Vedran Dunjko and co-authors applied a quantum analysis to a field within artificial intelligence called reinforcement learning, which deals with how to program a machine to make appropriate choices to maximize a cumulative reward. The field is surprisingly complex and must take into account everything from game theory to information theory.
Dunjko and his team found that quantum effects, when applied to reinforcement learning in artificial intelligence systems, could provide quadratic improvements in learning efficiency, reports Phys.org. Exponential improvements might even be possible over short-term performance tasks. The study was published in the journal Physical Review Letters.
"This is, to our knowledge, the first work which shows that quantum improvements are possible in more general, interactive learning tasks," explained Dunjko. "Thus, it opens up a new frontier of research in quantum machine learning."
One of the key quantum effects in regards to learning is quantum superposition, which potentially allows a machine to perform many steps simultaneously. Such a system has vastly improved processing power, which allows it to compute more variables when making decisions.
The research is tantalizing, in part because it mirrors some theories about how biological brains might produce higher cognitive states, possibly even being related to consciousness. For instance, some scientists have proposed the idea that our brains pull off their complex calculations by making use of quantum computation.
Could quantum effects unlock consciousness in our machines? Quantum physics isn't likely to produce HAL from "2001: A Space Odyssey" right away; the most immediate improvements in artificial intelligence will likely come in complex fields such as climate modeling or automated cars. But eventually, who knows?
You probably won't want to be taking a joyride in an automated vehicle the moment it becomes conscious, if HAL is an example of what to expect.
"While the initial results are very encouraging, we have only begun to investigate the potential of quantum machine learning," said Dunjko. "We plan on furthering our understanding of how quantum effects can aid in aspects of machine learning in an increasingly more general learning setting. One of the open questions we are interested in is whether quantum effects can play an instrumental role in the design of true artificial intelligence."