[论文解读] Experimental quantum memristor

Michele Spagnolo, Joshua Morris+7 more|arXiv (Cornell University)|May 11, 2021

Neural Networks and Reservoir Computing参考文献 54被引用 5

一句话总结

该论文通过集成光子电路实验演示了一种量子忆阻器，其通过单光子上的非幺正、测量诱导的动力学表现出忆阻特性。该器件实现了高保真度态重建（平均保真度达98.7%），并具备实现量子储层计算的潜力，相较于经典架构具有优势。

ABSTRACT

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.

研究动机与目标

• 为解决将非线性、耗散性操作集成到量子计算中的挑战，这些操作对量子神经网络至关重要。
• 实现在量子光学平台上模拟经典忆阻器的滞后性与记忆依赖行为的实用型量子忆阻器。
• 展示一种可行且可实验实现的量子忆阻器架构，基于集成光子学与单光子态。
• 评估该器件在近期量子神经形态计算应用中的潜力，特别是量子储层计算方面。

提出的方法

• 该量子忆阻器通过光子芯片中的可调Mach-Zehnder干涉仪实现，其输出态取决于输入光子态和干涉仪的反射率。
• 该器件利用测量诱导的反馈机制，实现非幺正、具有记忆依赖性的演化，类似于经典忆阻器的滞后行为。
• 对不同输入态和反射率设置下的输出密度矩阵进行量子态层析，将保真度与理论预测进行比较。
• 通过将密度矩阵的非对角项与实验数据拟合，提取全局相位，得到 φ_global = 5.6 rad。
• 在16种输入光子数 |β|² 与干涉仪反射率 R 的组合下对系统进行表征，覆盖了所有输入态的完整范围。
• 利用数值模拟评估器件在量子储层计算任务中的性能，预测其相较于经典架构具有优势。

实验结果

研究问题

• RQ1 能否在基于单光子与集成电路的光子平台上实验实现量子忆阻器？
• RQ2 该器件是否表现出与量子域忆阻行为一致的非幺正、具有记忆依赖性的动力学？
• RQ3 该量子忆阻器在不同输入态与系统参数下，对量子相干性与保真度的保持程度如何？
• RQ4 该量子忆阻器是否能在量子储层计算任务中超越经典架构？

主要发现

• 实验重建的输出密度矩阵与理论预测高度一致，所有测试配置下的平均保真度达到98.7%。
• 该器件通过非幺正、测量驱动的演化表现出忆阻特性，其输出态依赖于输入态以及通过反射率 R 体现的系统历史。
• 成功从实验数据中提取出全局相位 φ_global = 5.6 rad，证实了芯片中存在相干光程差。
• 该量子忆阻器在不同输入条件下仍保持高保真度，个体保真度在测试参数集范围内介于94.92%至99.71%之间。
• 数值模拟表明，与经典实现相比，该量子忆阻器在量子储层计算中可能提供性能优势。
• 该器件未表现出显著的额外退相干，理论与实验纯度值（Tr(ρ²) ≈ 0.5–1.0）高度吻合。