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arXiv 1901.01653quant-phcs.LGcs.SYeess.SY
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使用耗散量子系统学习非线性输入-输出映射

Learning Nonlinear Input-Output Maps with Dissipative Quantum Systems

School of Electrical Engineering and Telecommunications, The University of New South Wales (UNSW)(新南威尔士大学电气工程与电信学院)

Jiayin Chen, Hendra I. Nurdin

AI总结:

本文提出了利用耗散量子系统学习具有衰减记忆的非线性输入-输出映射的理论,证明了其普遍性,并展示了少量qubit即可媲美大规模经典学习方案,指数级Hilbert空间为其超越经典方案提供了潜在资源。

摘要:

在本文中,我们发展了一种利用耗散量子系统学习具有衰减记忆的非线性输入-输出映射的理论,作为使用经典动力系统近似此类映射理论的量子对应物。该理论确定了一类耗散量子系统具备普遍性所需的属性,即任何具有衰减记忆的输入-输出映射都可以被该类中的一个元素任意接近地近似。随后,我们引入了一个可证明具有普遍性的耗散量子系统示例类。数值实验表明,使用少量qubit,该类能够实现与具有大量可调参数的经典学习方案相当的性能。进一步的数值分析表明,呈指数增长的Hilbert空间为耗散量子系统在输入-输出映射上超越经典学习方案提供了潜在资源。

英文摘要:

In this paper, we develop a theory of learning nonlinear input-output maps with fading memory by dissipative quantum systems, as a quantum counterpart of the theory of approximating such maps using classical dynamical systems. The theory identifies the properties required for a class of dissipative quantum systems to be {\em universal}, in that any input-output map with fading memory can be approximated arbitrarily closely by an element of this class. We then introduce an example class of dissipative quantum systems that is provably universal. Numerical experiments illustrate that with a small number of qubits, this class can achieve comparable performance to classical learning schemes with a large number of tunable parameters. Further numerical analysis suggests that the exponentially increasing Hilbert space presents a potential resource for dissipative quantum systems to surpass classical learning schemes for input-output maps.

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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