Investigating a Quantum Cloud Paradigm with Quantum Neural Networks
Maxwell Yarter, Glen S. Uehara, Andreas Spanias · 2023
In this work, we examine the interactions between an embedded processing board and cloud server performing quantum computing simulations. We examined the trade-offs in performance and complexity between using classical neural networks and quantum-hybrid neural networks on the cloud server. More specifically, we propose the use of quantum hybrid neural network (QNN) for the classification of spoken commands on live audio data acquired at the networks edge. For this application, a compact embedded processor board handles simple operations like data acquisition and pre-processing while a quantum cloud server is used to perform quantum computing simulations. This experimental setup is well suited for quantum computing as it is not feasible to embed a quantum processor at the edge, but practical to interface embedded processor boards to a quantum cloud server for processing massive audio data for a variety of recognition tasks. We found that distributing the task between the embedded processor and quantum sever enabled the application of QNN's to live audio data at the expense of system response time.