Li Zhaokai, Liu Xiaomei, Xu Nanyang, Du Jiangfeng
Hefei National Laboratory for Physical Sciences at the Microscale and Department of Modern Physics, University of Science and Technology of China, Hefei 230026, China.
Synergetic Innovation Center of Quantum Information and Quantum Physics, University of Science and Technology of China, Hefei 230026, China.
Phys Rev Lett. 2015 Apr 10;114(14):140504. doi: 10.1103/PhysRevLett.114.140504. Epub 2015 Apr 8.
The fundamental principle of artificial intelligence is the ability of machines to learn from previous experience and do future work accordingly. In the age of big data, classical learning machines often require huge computational resources in many practical cases. Quantum machine learning algorithms, on the other hand, could be exponentially faster than their classical counterparts by utilizing quantum parallelism. Here, we demonstrate a quantum machine learning algorithm to implement handwriting recognition on a four-qubit NMR test bench. The quantum machine learns standard character fonts and then recognizes handwritten characters from a set with two candidates. Because of the wide spread importance of artificial intelligence and its tremendous consumption of computational resources, quantum speedup would be extremely attractive against the challenges of big data.
人工智能的基本原理是机器能够从以往经验中学习并据此开展未来工作的能力。在大数据时代，经典学习机器在许多实际情况下往往需要巨大的计算资源。另一方面，量子机器学习算法通过利用量子并行性，其速度可能比经典算法快指数倍。在此，我们展示了一种量子机器学习算法，用于在一个四量子比特核磁共振测试平台上实现手写识别。该量子机器学习标准字符字体，然后从包含两个候选字符的集合中识别手写字符。鉴于人工智能的广泛重要性及其对计算资源的巨大消耗，量子加速对于应对大数据挑战将极具吸引力。
