A.I.’s Next Big Challenge? Playing a Quantum Version of Go


When Google DeepMind’s AlphaGo program defeated the world’s greatest Go player in March 2016, it represented a major tech breakthrough. Go, a Chinese board game in which the goal is to surround more territory than your opponent, is a game that’s notoriously easy to learn but next to impossible to master. The total number of allowable board positions exceeds the total number of atoms in the observable universe. However, an A.I. still learned to defeat one of humanity’s best players.

But while cutting-edge technology made this possible, cutting-edge technology could also make mastering Go even more difficult for future machines — thanks to the insertion of quantum computing concepts like entanglement to add a new element of randomness to the game.

“We [created] a prototype of a quantum Go machine,” Xian-Min Jin, a professor in the Center for Integrated Quantum Information Technologies (IQIT) at Shanghai Jiao Tong University in China, told Digital Trends. “We introduce the counter-intuitive effects of quantum mechanics into the game of Go and experimentally implement it. Regular Go is a deterministic and perfect information game, while Quantum Go can be a nondeterministic and imperfect information game.”

As New Scientist, which wrote about the project, explains, Quantum Go includes quantum states such as placing two stones at once, representing a superposition of two possible locations for one single stone. As the publication notes, “When a new stone is put next to either of those locations, the quantum state of a pair of entangled photons is measured to determine the original stone’s location, collapsing the superposition. And then the other stone is removed.”

Jin explained that nondeterministic and imperfect information games like Quantum Go are more like real-world problems than perfect information games like regular Go. Non-deterministic and imperfect information games are also more difficult to solve, meaning that the game will requirer a smart algorithm to figure it out. “Quantum Go can be adjusted from perfect information to imperfect information and from deterministic to non-deterministic, which can cover various kinds of difficulties of different games,” Jin said.

The idea that quantum computing could be used to make games harder, which could then presumably be cracked by still-better quantum machine learning algorithms, is a fascinating concept. While more difficult board games aren’t necessarily the ultimate goal of quantum computing (although there are some intriguing quantum games researchers are already making), this work could make quantum A.I. tools smarter for genuinely useful purposes such as stronger encryption. If Generative Adversarial Networks prove anything, it’s that pitting two incredibly smart pieces of tech against one another winds up elevating both. Quantum Go could also help introduce quantum principles to players.

A paper describing the research is available to read online.

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