Can Quantum Computers Learn Like Classical Computers? A Co-Design Framework for Machine Learning and Quantum Circuits
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Despite the pursuit of quantum supremacy in various applications, the power of quantum computers in machine learning (such as neural network models) has mostly remained unknown, primarily due to a missing link that effectively designs a neural network model suitable for quantum circuit implementation. In this article, we present the first co-design framework, namely QuantumFlow, to fixed the missing link. QuantumFlow consists of a novel quantum-friendly neural network (QF-Net) design, an automatic tool (QF-Map) to generate the quantum circuit (QF-Circ) for QF-Net, and a theoretic-based execution engine (QF-FB) to efficiently support the training of QF-Net on a classical computer. We discover that, in order to make full use of the strength of quantum representation, data in QF-Net is best modeled as random variables rather than real numbers. Moreover, instead of using the classical batch normalization (which is key to achieve high accuracy for deep neural networks), a quantum-aware batch normalization method is proposed for QF-Net. Evaluation results show that QF-Net can achieve 97.01 widely used MNIST dataset, which is 14.55 quantum-aware implementation. A case study on a binary classification application is conducted. Running on IBM Quantum processor's "ibmq_essex" backend, a neural network designed by QuantumFlow can achieve 82 the best of our knowledge, QuantumFlow is the first framework that co-designs both the machine learning model and its quantum circuit.
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