Quantum machine learning has the potential to overcome problems that current classical machine learning algorithms face, such as large data requirements or long learning times. Sampling is one of the aspects of classical machine learning that might benefit from quantum machine learning, as quantum computers intrinsically excel at sampling. Current hybrid quantum-classical implementations provide ways to already use near-term quantum computers for practical applications. By expanding the horizon on hybrid quantum-classical approaches, this work proposes the first implementation of a gated quantum-classical hybrid Helmholtz machine, a gate-based quantum circuit approximation of a neural network for unsupervised tasks. Our approach focuses on parameterized shallow quantum circuits and effectively implements an approximate Bayesian network, overcoming the exponential complexity of exact networks. In addition, a new balanced cost function is introduced, preventing the need of millions of training samples. Using a bars and stripes data set, the model, implemented on the Quantum Inspire platform, is shown to outperform classical Helmholtz machines in terms of the Kullback–Leibler divergence.

中文翻译：

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混合亥姆霍兹机器：基于门的量子电路实现

量子机器学习具有克服当前经典机器学习算法所面临的问题的潜力，例如大数据需求或长学习时间。采样是经典机器学习的方面之一，它可能会受益于量子机器学习，因为量子计算机本质上擅长采样。当前的混合量子经典实现提供了已经将短期量子计算机用于实际应用的方式。通过扩大对混合量子经典方法的视野，这项工作提出了门控量子经典混合亥姆霍兹机的第一个实现，这是一种用于非监督任务的神经网络的基于门的量子电路近似。我们的方法着重于参数化的浅量子电路，并有效地实现了近似的贝叶斯网络，克服精确网络的指数复杂性。另外，引入了新的平衡成本函数，从而避免了数百万个训练样本的需求。通过使用条形数据集，在Kumback-Leibler散度方面，在Quantum Inspire平台上实现的模型表现出优于传统的Helmholtz机器。