In this work we present the Scaled QUantum IDentifier (SQUID), an open-source framework for exploring hybrid Quantum-Classical algorithms for classification problems. The classical infrastructure is based on PyTorch and we provide a standardized design to implement a variety of quantum models with the capability of back-propagation for efficient training. We present the structure of our framework and provide examples of using SQUID in a standard binary classification problem from the popular MNIST dataset. In particular, we highlight the implications for scalability for gradient-based optimization of quantum models on the choice of output for variational quantum models.

中文翻译：

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使用 SQUID 进行量子机器学习

在这项工作中，我们提出了 Scaled Quantum IDentifier (SQUID)，这是一个开源框架，用于探索分类问题的混合 Quantum-Classical 算法。经典基础设施基于 PyTorch，我们提供标准化设计来实现各种具有反向传播能力的量子模型，以实现高效训练。我们展示了我们框架的结构，并提供了在流行的 MNIST 数据集的标准二进制分类问题中使用 SQUID 的示例。特别是，我们强调了基于梯度的量子模型优化对变分量子模型的输出选择的可扩展性的影响。