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arXiv 2105.04867quant-ph
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实验量子忆阻器

Experimental quantum memristor

Michele Spagnolo, Joshua Morris, Simone Piacentini, Michael Antesberger, Francesco Massa, Francesco Ceccarelli, Andrea Crespi, Roberto Osellame, Philip Walther

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摘要：

量子计算机技术利用量子物理学的特征来彻底改变信息处理和计算。因此，量子计算机使用统一地处理信息的物理量子门，即使最终的计算步骤可能是基于测量的或非统一的。量子计算机的应用涵盖了不同的领域，从著名的量子算法到量子机器学习和量子神经网络。其中最后一个是特别感兴趣的，属于人工智能的有前途的领域。然而，量子神经网络在技术上具有挑战性，因为底层计算需要非幺正操作来模仿神经元的行为。经典神经网络的一个里程碑式的发展是记忆电阻器的实现。这些是无源电路元件，以电阻滞后的形式保持其过去状态的记忆，从而提供对非线性门操作的访问。对实现量子忆阻器的追求导致了一些建议，所有这些都面临着有限的技术实用性。在这里，我们介绍和实验证明了一种新的量子光学忆阻器，是基于集成光子学和单光子的行为。我们表征其忆阻行为，并强调我们的设备的实际潜力，通过数值模拟的量子水库计算的实例，在那里我们预测在使用我们的量子忆阻器的优势超过经典架构。鉴于最近在实现神经网络应用的光子电路方面取得的进展，我们的设备可能成为即时和短期量子神经形态架构的构建模块。

英文摘要：

Quantum computer technology harnesses the features of quantum physics for revolutionizing information processing and computing. As such, quantum computers use physical quantum gates that process information unitarily, even though the final computing steps might be measurement-based or non-unitary. The applications of quantum computers cover diverse areas, reaching from well-known quantum algorithms to quantum machine learning and quantum neural networks. The last of these is of particular interest by belonging to the promising field of artificial intelligence. However, quantum neural networks are technologically challenging as the underlying computation requires non-unitary operations for mimicking the behavior of neurons. A landmark development for classical neural networks was the realization of memory-resistors, or "memristors". These are passive circuit elements that keep a memory of their past states in the form of a resistive hysteresis and thus provide access to nonlinear gate operations. The quest for realising a quantum memristor led to a few proposals, all of which face limited technological practicality. Here we introduce and experimentally demonstrate a novel quantum-optical memristor that is based on integrated photonics and acts on single photons. We characterize its memristive behavior and underline the practical potential of our device by numerically simulating instances of quantum reservoir computing, where we predict an advantage in the use of our quantum memristor over classical architectures. Given recent progress in the realization of photonic circuits for neural networks applications, our device could become a building block of immediate and near-term quantum neuromorphic architectures.

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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