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Many-body localized hidden generative models
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DOI：10.1103/PhysRevResearch.6.043041.png)
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Quantum generative models hold the promise of accelerating or improving machine learning tasks by leveraging the probabilistic nature of quantum states, but the successful optimization of these models remains a difficult challenge. To tackle this challenge, we present a new architecture for quantum generative modeling that combines insights from classical machine learning and quantum phases of matter. In particular, our model utilizes both many-body localized (MBL) dynamics and hidden units to improve the optimization of the model. We demonstrate the applicability of our model on a diverse set of classical and quantum tasks, including a toy version of MNIST handwritten digits, quantum data obtained from quantum many-body states, and nonlocal parity data. Our architecture and algorithm provide novel strategies of utilizing quantum many-body systems as learning resources and reveal a powerful connection between disorder, interaction, and learning in quantum many-body systems.
Keyword:
QUANTUM
SYSTEMS
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4.2
论文数:
7.6K
被引数:
2.7W
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Expressibility and trainability of parametrized analog quantum systems for machine learning applications用于机器学习应用的参数化模拟量子系统的可表达性和可训练性
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