QuClassi: A Hybrid Deep Neural Network Architecture based on Quantum State Fidelity

Part of Proceedings of Machine Learning and Systems 4 pre-proceedings (MLSys 2022)

Paper

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Authors

Samuel A. Stein, Betis Baheri, Daniel Chen, Ying Mao, Qiang Guan, Ang Li, Shuai Xu, Caiwen Ding

Abstract

In the past decade, remarkable progress has been achieved in deep learning related systems and applications. In the post Moore’s Law era, however, the limit of semiconductor fabrication technology along with the increasing data size has slowed down the development of learning algorithms. In parallel, the rapid development of quantum computing has pushed it into a new era. Google illustrated quantum supremacy by completing a specific task (random sampling problem), in 200 seconds, which continues to be impracticable for the largest classical computers. Due to the exponential potential of quantum computing, quantum based learning is an area of interest, in hopes that certain systems might offer a quantum speedup. In this work, we propose a novel architecture QuClassi, a quantum neural network for both binary and multi-class classification. Powered by a quantum differentiation function along with a hybrid quantum-classic design, QuClassi encodes the data with a reduced number of qubits and generates the quantum circuit, pushing it to the quantum platform for the best states, iteratively. We conduct intensive experiments on both quantum simulators, IBM-Q’s quantum platform as well as evaluate performance on IonQ. The evaluation results demonstrate that QuClassi is able to outperform the state-of-the-art quantum-based solutions, Tensorflow-Quantum and QuantumFlow by up to 53.75% and 203.00% for binary and multi-class classifications. When comparing to traditional deep neural networks, QuClassi achieves a comparable performance with 97.37% fewer parameters.