量子围棋机
Quantum Go Machine
摘要：
长期以来，围棋一直被认为是人工智能的试验平台。通过引入某些量子特性，如波函数的叠加和坍缩，我们用偏振自由度纠缠的关联光子对实验演示了一个量子版的围棋。随着两个玩家轮流在时间序列中放置石头，生成状态的希尔伯特空间的总维度呈指数级增长。随着不确定性和不完全信息博弈越来越难以用现代技术解决，我们兴奋地发现量子物理中固有的随机性可以给博弈带来经典博弈中不存在的不确定性。一些量子资源，如相干或纠缠，也可以被编码来表示量子宝石的状态。调整量子资源可能会改变单个博弈的平均不完美信息（因为比较经典围棋是完美信息博弈）。我们进一步验证了它的非确定性的特点，显示从不同类别的量子态的时间序列数据的不可预测性。最后，通过将量子围棋与人工智能中广泛研究的几个典型博弈进行比较，我们发现量子围棋可以覆盖广泛的博弈难度，而不是单一的点。我们的研究结果建立了一个通过利用固有的量子特征和资源来发明具有量子困难的新游戏的范例，并为测试经典和量子机器学习的新算法提供了一个通用平台。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
