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arXiv 2408.09577cond-mat.quant-gas
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使用带阻尼的玻色-爱因斯坦凝聚的量子储备池计算

Quantum Reservoir Computing Using Bose-Einstein Condensate with Damping

Yuki Kurokawa, Junichi Takahashi, Yoshiya Yamanaka

AI总结:

本研究采用稀薄原子气体的玻色-爱因斯坦凝聚作为量子储备池,发现阻尼是系统发挥储备池作用的必要条件,动力学非线性可提升性能,而凝聚粒子数减少会降低性能。

摘要:

量子储备池计算是一种机器学习,其中量子系统的高维希尔伯特空间有助于提升性能。在本研究中,我们采用稀薄原子气体的玻色-爱因斯坦凝聚作为储备池,以考察凝聚粒子数减少、阻尼以及动力学非线性的影响。观察发现,要使凝聚体发挥储备池的作用,物理系统需要阻尼。动力学的非线性提高了储备池的性能,而凝聚粒子数的减少则降低了性能。

英文摘要:

Quantum reservoir computing is a type of machine learning in which the high-dimensional Hilbert space of quantum systems contributes to performance. In this study, we employ the Bose-Einstein condensate of dilute atomic gas as a reservoir to examine the effect of reduction in the number of condensed particles, damping, and the nonlinearity of the dynamics. It is observed that for the condensate to function as a reservoir, the physical system requires damping. The nonlinearity of the dynamics improves the performance of the reservoir, while the reduction in the number of condensed particles degrades the 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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