A team of researchers from Aarhus University in Denmark let DeepMind‘s AlphaZero algorithm loose on a few quantum computing optimization problems and, much to everyone’s surprise, the AI was able to solve the problems without any outside expert knowledge. Not bad for a machine learning paradigm designed to win at games like Chess and StarCraft.
You’ve probably heard of DeepMind and its AI systems. The UK-based Google sister-company is responsible for both AlphaZero and AlphaGo, the systems that beat the world’s most skilled humans at the games of Chess and Go. In essence, what both systems do is try to figure out what the optimal next set of moves is. Where humans can only think so many “moves” ahead, the AI can look a bit further using optimized search and planning methods.
Related: DeepMind’s AlphaZero AI is the new champion in chess, shogi, and Go
When the Aarhus team applied AlphaZero’s optimization abilities to a trio of problems associated with optimizing quantum functions – an open problem for the quantum computing world – they learned that its ability to learn new parameters unsupervised transferred over from games to applications quite well.
Per the study:
AlphaZero employs a deep neural network in conjunction with deep lookahead in a guided tree search, which allows for predictive hidden-variable approximation of the quantum parameter landscape. To emphasize transferability, we apply and benchmark the algorithm on three classes of control problems using only a single common set of algorithmic hyperparameters.
The implications for AlphaZero’s mastery over the quantum universe could be huge. Controlling a quantum computer requires an AI solution because operations at the quantum level quickly become incalculable by humans. The AI can find optimum paths between data clusters in order to emerge better solutions in tandem with computer processors. It works a lot like human heuristics, just scaled to the nth degree.
An example of this would be an algorithm that helps a quantum computer sort through near-infinite combinations of molecules to come up with chemical compounds that would be useful in the treatment of certain illnesses. The current paradigm would involve developing an algorithm that relies on human expertise and databases with previous findings to point it in the right direction.
But the kind of problems we’re looking at quantum computers to solve don’t always have a good starting point. Some of these, optimization problems like the Traveling Salesman Problem, need an algorithm that’s capable of figuring things out without the need for constant adjustment by developers.
DeepMind‘s algorithm and AI system may be the solution quantum computing’s been waiting for. The researchers effectively employ AlphaZero as a “Tabula Rasa” for quantum optimization: It doesn’t necessarily need human expertise to find the optimum solution to a problem at the quantum computing level.
Before we start getting too concerned about unsupervised AI accessing quantum computers, it’s worth mentioning that so far AlphaZero’s just solved a few problems in order to prove a concept. We know the algorithms can handle quantum optimization, now it’s time to figure out what we can do with it.
The researchers have already received interest from big tech and other academic institutions with queries related to collaborating on future research. Not for nothing, but DeepMind‘s sister-company Google has a little quantum computing program of its own. We’re betting this isn’t the last we’ve heard of AlphaZero’s adventures in the quantum computing world.
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