论文标题
迈向自动QML：基于云的自动电路体系结构搜索框架
Towards AutoQML: A Cloud-Based Automated Circuit Architecture Search Framework
论文作者
论文摘要
经典机器学习算法的学习过程是由需要自定义的超参数调整的，以最好地从输入数据集学习和推广。近年来，量子机学习（QML）已成为量子计算的可能应用，这可能在将来提供量子优势。但是，经典机器学习算法的量子版本引入了许多其他参数和电路变化，它们在调整时具有自身的复杂性。 在这项工作中，我们迈出了自动化量子机学习（AUTOQML）的第一步。我们提出了对问题的具体描述，然后开发经典的Quantum混合云体系结构，该体系结构允许并行的超参数探索和模型训练。 作为应用程序用例，我们训练量子生成的对抗神经网络（QGAN），以产生遵循已知历史数据分布的能源价格。这种QML模型可用于能源经济学领域的各种应用。
The learning process of classical machine learning algorithms is tuned by hyperparameters that need to be customized to best learn and generalize from an input dataset. In recent years, Quantum Machine Learning (QML) has been gaining traction as a possible application of quantum computing which may provide quantum advantage in the future. However, quantum versions of classical machine learning algorithms introduce a plethora of additional parameters and circuit variations that have their own intricacies in being tuned. In this work, we take the first steps towards Automated Quantum Machine Learning (AutoQML). We propose a concrete description of the problem, and then develop a classical-quantum hybrid cloud architecture that allows for parallelized hyperparameter exploration and model training. As an application use-case, we train a quantum Generative Adversarial neural Network (qGAN) to generate energy prices that follow a known historic data distribution. Such a QML model can be used for various applications in the energy economics sector.
