A Hybrid System for Learning Classical Data in Quantum States

TL;DR

This paper introduces GenQu, a hybrid quantum framework that efficiently learns classical data using fewer qubits and parameters, achieving faster convergence and comparable accuracy to classical methods.

Contribution

The paper presents a novel hybrid quantum framework, GenQu, that reduces resource requirements and accelerates training for classical data learning tasks.

Findings

Achieves similar accuracy with fewer qubits.

Reduces parameter size by up to 95.86%.

Speeds up convergence by 33.33%.

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.