Resource saving via ensemble techniques for quantum neural networks

IF 4.1 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Massimiliano Incudini, Michele Grossi, Andrea Ceschini, Antonio Mandarino, Massimo Panella, Sofia Vallecorsa, David Windridge
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引用次数: 2

Abstract

Abstract Quantum neural networks hold significant promise for numerous applications, particularly as they can be executed on the current generation of quantum hardware. However, due to limited qubits or hardware noise, conducting large-scale experiments often requires significant resources. Moreover, the output of the model is susceptible to corruption by quantum hardware noise. To address this issue, we propose the use of ensemble techniques, which involve constructing a single machine learning model based on multiple instances of quantum neural networks. In particular, we implement bagging and AdaBoost techniques, with different data loading configurations, and evaluate their performance on both synthetic and real-world classification and regression tasks. To assess the potential performance improvement under different environments, we conducted experiments on both simulated, noiseless software and IBM superconducting-based QPUs, suggesting these techniques can mitigate the quantum hardware noise. Additionally, we quantify the amount of resources saved using these ensemble techniques. Our findings indicate that these methods enable the construction of large, powerful models even on relatively small quantum devices.
基于量子神经网络集成技术的资源节约
量子神经网络在许多应用中具有重要的前景,特别是因为它们可以在当前一代量子硬件上执行。然而,由于有限的量子比特或硬件噪声,进行大规模实验往往需要大量的资源。此外,该模型的输出容易受到量子硬件噪声的破坏。为了解决这个问题,我们建议使用集成技术,这涉及到基于多个量子神经网络实例构建单个机器学习模型。特别是,我们在不同的数据加载配置下实现了bagging和AdaBoost技术,并评估了它们在合成和现实世界分类和回归任务上的性能。为了评估在不同环境下潜在的性能改进,我们在模拟、无噪声软件和IBM超导qpu上进行了实验,表明这些技术可以减轻量子硬件噪声。此外,我们量化了使用这些集成技术节省的资源量。我们的研究结果表明,这些方法甚至可以在相对较小的量子设备上构建大型,强大的模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
7.60
自引率
4.20%
发文量
29
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