Quantum Machine Learning with SQUID

Abstract

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.

Standard model physics and the digital quantum revolution: thoughts about the interface

“…This is similar to the use of variational Monte Carlo as a precursor to more accurate, and expensive, GFMC calculations of nuclei (see reference [300] for a review). In addition to such compounding applications, another possible use of VQA is as components for quantum machine learning [460] tasks (possibly coupled to classical neural networks e.g., reference [461]), such as characterizing the output states generated by a quantum algorithm or even directly as a way to perform inference on classical data (for a recent review of applications to SM problems, see reference [462]). Presently, it is not clear if an exponential advantage could be obtained for the latter task (see, e.g., references [463,464]).…”