arXiv 1808.034252018-08-10quant-phcs.LGstat.ML

Learning and Inference on Generative Adversarial Quantum Circuits

Institute of Physics, Chinese Academy of Sciences（中国科学院物理研究所）；University of Chinese Academy of Sciences（中国科学院大学）；College of Physics, Jilin University（吉林大学物理学院）；CAS Center for Excellence in Topological Quantum Computation, University of Chinese Academy of Sciences（中国科学院大学拓扑量子计算卓越创新中心）；Kavli Institute of Theoretical Sciences, University of Chinese Academy of Sciences（中国科学院大学卡弗里理论科学研究所）

Jinfeng Zeng, Yufeng Wu, Jin-Guo Liu, Lei Wang, Jiangping Hu

备注：: 7 pages, 6 figures
发表信息：: Phys. Rev. A 99, 052306 (2019)
DOI：: 10.1103/PhysRevA.99.052306

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Quantum mechanics is inherently probabilistic in light of Born's rule. Using quantum circuits as probabilistic generative models for classical data exploits their superior expressibility and efficient direct sampling ability. However, training of quantum circuits can be more challenging compared to classical neural networks due to lack of efficient differentiable learning algorithm. We devise an adversarial quantum-classical hybrid training scheme via coupling a quantum circuit generator and a classical neural network discriminator together. After training, the quantum circuit generative model can infer missing data with quadratic speed up via amplitude amplification. We numerically simulate the learning and inference of generative adversarial quantum circuit using the prototypical Bars-and-Stripes dataset. Generative adversarial quantum circuits is a fresh approach to machine learning which may enjoy the practically useful quantum advantage on near-term quantum devices.

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