Using entangled photons to play “quantum Go”

Sketch of quantum Go machine. a, Experimental setup of the quantum stone box. The generated photon pairs can be tuned to maximally entangled states, non-maximally entangled states and product states to behave as difierent quantum stones, see Methods. b, The collapse measurement module. After the photons come into this module, they will be measured by the polarizing beam splitter (PBS) then the quantum state collapses to path 1 and 3 (or path 2 and 4). Four single photon detectors transfer the photon signals to electronic signals. c, The time-of-flight storage module. Four output channels from the collapse measurement module will be guided into this module. The collapse result information of each pair of the entangled photons can be acquired a‰er seŠing a proper coincidence time window, and recorded as an effective stored state in the time series data. We encode the signals coincidence in Channel 1 and 3 as "1", and Channel 2 and 4 as "0". d, Sketch of playing quantum Go with the quantum stones from the time series data. Two robot arms represent the two agents who help to execute the game of quantum Go together. They pick the quantum stones from the quantum stone box alternately and put every stone onto two intersections of the virtual board. When a quantum stone is put on an intersection that has neighbors, the game will get the collapse results from the time series data with a backdated measurement in the collapse measurement module. Credit: arXiv:2007.12186 [quant-ph]

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.