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arXiv · 2012.00256

A Hybrid System for Learning Classical Data in Quantum States

Samuel A. Stein·Ryan L'Abbate·Wenrui Mu·Yue Liu·Betis Baheri·Ying Mao·Qiang Guan·Ang Li·Bo Fang
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Abstract

Deep neural network powered artificial intelligence has rapidly changed our daily life with various applications. However, as one of the essential steps of deep neural networks, training a heavily weighted network requires a tremendous amount of computing resources. Especially in the post-Moore's Law era, the limit of semiconductor fabrication technology has restricted the development of learning algorithms to cope with the increasing high-intensity training data. Meanwhile, quantum computing has demonstrated its significant potential in terms of speeding up the traditionally compute-intensive workloads. For example, Google illustrated quantum supremacy by completing a sampling calculation task in 200 seconds, which is otherwise impracticable on the world's largest supercomputers. To this end, quantum-based learning has become an area of interest, with the potential of a quantum speedup. In this paper, we propose GenQu, a hybrid and general-purpose quantum framework for learning classical data through quantum states. We evaluate GenQu with real datasets and conduct experiments on both simulations and real quantum computer IBM-Q. Our evaluation demonstrates that, compared with classical solutions, the proposed models running on GenQu framework achieve similar accuracy with a much smaller number of qubits, while significantly reducing the parameter size by up to 95.86% and converging speedup by 33.33% faster.

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Samuel A. Stein, Ryan L'Abbate, Wenrui Mu, Yue Liu, Betis Baheri, Ying Mao, Qiang Guan, Ang Li, Bo Fang. 2020-12-01. A Hybrid System for Learning Classical Data in Quantum States. https://arxiv.org/abs/2012.00256

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