Technology 3 min read

Google's AlphaZero Learns to Control Quantum Computers

John Williams RUS /

John Williams RUS /

In 2017, Google‘s AlphaZero destroyed the highest-rated chess engine, Stockfish, in a 100-game match-up. What’s more, the AI spent only four hours learning before demolishing the best chess program ever built.

Now a team of researchers at Aarhus University in Denmark is using the same algorithm to control a quantum computer.

Quantum computers are the future of computing. While classical computers can theoretically solve the same problems as quantum processors, they’ll require a ridiculous amount of time and energy.

So, it’s not surprising that research groups across the world are working to build these powerful systems. And the team at Aarhus University in Denmark happens to fall in this category.

Under the direction of Professor Jacob Sherson, the Aarhus researchers used the AlphaZero to learn to control a quantum system.

Here’s how that happened.

How AlphaZero Learned to Control Quantum Computers

AlphaZero can learn on its own without human intervention.

In other words, no one taught AlphaZero chess to beat Stockfish, at least not in the traditional sense. There was neither an endgame table nor a complicated algorithm analyzing the differences between the chess pieces.

Instead, the AI used a type of machine learning called reinforcement learning to teach itself. And it worked well.

Aside from beating humans and computer programs at Chess, AlphaZero has also excelled at other games such as Go and Shogi. The AI is so impressive that Danish grandmaster, Peter Heine Nielsen, described it as a superior alien species sent to beat us at Chess.

Using computer simulations, the researchers demonstrated the broad capability of AlphaZero in quantum computing. They used three different control problems that could each potentially play a role in quantum systems.

Although AlphaZero is good on its own, the AI proved to be significantly better with a specialized quantum optimization algorithm.

Professor Jacob Sherson noted:

“This indicates that we are still in need of human skill and expertise and that the goal of the future should be to understand and develop hybrid intelligence interfaces that optimally exploit the strengths of both.”

To speed up the development process in this field, the team has made the code openly available. And several major tech companies with quantum laboratories expressed interest within hours.

So it will probably not be long until these methods find use in practical experiments across the world,” Jacob concluded.

Read More: New AlphaGo Zero “Unsupervised”; AI is 100X Better While Using 10% Computing Power

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Sumbo Bello is a creative writer who enjoys creating data-driven content for news sites. In his spare time, he plays basketball and listens to Coldplay.

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