Quantum Go Machine

TL;DR

This paper introduces a quantum version of the game Go, leveraging quantum superposition and entanglement to create a nondeterministic, complex game environment that can serve as a platform for testing new AI algorithms.

Contribution

The paper experimentally demonstrates a quantum Go game using entangled photons, highlighting how quantum resources influence game complexity and nondeterminism, offering a novel quantum game paradigm.

Findings

Quantum Go exhibits inherent nondeterminism due to quantum randomness.

Quantum resources can modulate the game's difficulty and information completeness.

Quantum Go covers a broad spectrum of game complexities, unlike classical Go.

Abstract

Go has long been considered as a testbed for artificial intelligence. By introducing certain quantum features, such as superposition and collapse of wavefunction, we experimentally demonstrate 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, we excitedly find 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. We further verify 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, we find that quantum Go can cover a wide range of game difficulties rather than a single point. Our 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.