Using entangled photons to play “quantum Go”

A team of researchers affiliated with several institutions in China has developed a form of the board game Go using entanglement.

The researchers created a version of quantum Go using entangled photons and found that in continuously generating entangled photons as play progressed, they were able to introduce a random element to the game, which, they note, is required to build ever more powerful AI systems able to play sophisticated games with an element of randomness, such as poker.

Go has long been considered as a testbed for artificial intelligence. By introducing certain quantum features, such as superposition and collapse of wavefunction, they experimentally demonstrated a quantum version of Go by using correlated photon pairs entangled in polarization degree of freedom. The total dimension of Hilbert space of the generated states grows exponentially as two players take turns to place the stones in time series. As nondeterministic and imperfect information games are more difficult to solve using nowadays technology, they found that the inherent randomness in quantum physics can bring the game nondeterministic trait, which does not exist in the classical counterpart.

Some quantum resources, like coherence or entanglement, can also be encoded to represent the state of quantum stones. Adjusting the quantum resource may vary the average imperfect information (as comparison classical Go is a perfect information game) of a single game. They further verified its non-deterministic feature by showing the unpredictability of the time series data obtained from different classes of quantum state.

Finally, by comparing quantum Go with a few typical games that are widely studied in artificial intelligence, they found that quantum Go can cover a wide range of game difficulties rather than a single point. Their results establish a paradigm of inventing new games with quantum-enabled difficulties by harnessing inherent quantum features and resources, and provide a versatile platform for the test of new algorithms to both classical and quantum machine learning.

The paper can be read there.