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arXiv 2304.03462quant-ph

单振荡器储层计算中的量子性与学习性能

Quantumness and Learning Performance in Reservoir Computing with a Single Oscillator

Arsalan Motamedi, Hadi Zadeh-Haghighi, Christoph Simon

AI总结:

本研究探索单振荡器储层计算中量子模型对混沌时间序列的学习优势,发现量子性可提升性能但需谨慎应用,并验证了Lee-Jeong度量作为非经典性度量的高效性。

摘要:

我们探索了使用单振荡器在量子和经典模型中学习时间序列的储层计算能力。我们证明了该方案能够学习Mackey-Glass(MG)混沌时间序列,这是延迟微分方程的一个解。我们的结果表明,与经典非线性振荡器相比,量子非线性模型在学习性能方面更为有效。我们开发了在此过程中测量储层量子性的方法,证明了Lee-Jeong宏观性度量是一种非经典性度量。我们注意到,Lee-Jeong度量的评估在计算上比Wigner负性更高效。通过考察广泛的初始状态和变化的超参数来探索量子性与性能之间的关系,我们观察到在某些情况下量子性提高了学习性能。然而,我们的研究揭示,量子性的无差别增加并不总是带来改进的结果,因此在其应用中需要谨慎。我们讨论了这一现象,并试图找出高量子性带来性能提升的条件。

英文摘要:

We explore the power of reservoir computing with a single oscillator in learning time series using quantum and classical models. We demonstrate that this scheme learns the Mackey--Glass (MG) chaotic time series, a solution to a delay differential equation. Our results suggest that the quantum nonlinear model is more effective in terms of learning performance compared to a classical non-linear oscillator. We develop approaches for measuring the quantumness of the reservoir during the process, proving that Lee-Jeong's measure of macroscopicity is a non-classicality measure. We note that the evaluation of the Lee-Jeong measure is computationally more efficient than the Wigner negativity. Exploring the relationship between quantumness and performance by examining a broad range of initial states and varying hyperparameters, we observe that quantumness in some cases improves the learning performance. However, our investigation reveals that an indiscriminate increase in quantumness does not consistently lead to improved outcomes, necessitating caution in its application. We discuss this phenomenon and attempt to identify conditions under which a high quantumness results in improved performance.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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