论文标题

量子神经形态方法有效感应重力诱导的纠缠

Quantum neuromorphic approach to efficient sensing of gravity-induced entanglement

论文作者

Krisnanda, Tanjung, Paterek, Tomasz, Paternostro, Mauro, Liew, Timothy C. H.

论文摘要

纠缠的检测提供了明确的量子证明。它的确定可能对热或宏观物体可能具有挑战性,那里的纠缠通常很弱,但仍然存在。在这里,我们提出了一个平台,通过将感兴趣的对象连接到不受控制的量子网络来测量纠缠的平台,该网络的排放(读数)经过培训,以学习并感知前者的纠缠。首先,我们通过通用量子系统演示了平台及其功能。随着网络有效地学会识别量子状态,只能在仅使用非输入状态进行训练后感觉到纠缠的数量。此外,通过考虑到测量误差,我们以精度证明了纠缠感超过标准量子限制的缩放,并且超过了直接在对象上执行的测量值。最后,我们利用我们的平台来感应重力引起的两个质量之间的纠缠,并预测与现有技术相比,纠缠估计的精确度上有两个数量级的改善。

The detection of entanglement provides a definitive proof of quantumness. Its ascertainment might be challenging for hot or macroscopic objects, where entanglement is typically weak, but nevertheless present. Here we propose a platform for measuring entanglement by connecting the objects of interest to an uncontrolled quantum network, whose emission (readout) is trained to learn and sense the entanglement of the former. First, we demonstrate the platform and its features with generic quantum systems. As the network effectively learns to recognise quantum states, it is possible to sense the amount of entanglement after training with only non-entangled states. Furthermore, by taking into account measurement errors, we demonstrate entanglement sensing with precision that scales beyond the standard quantum limit and outperforms measurements performed directly on the objects. Finally, we utilise our platform for sensing gravity-induced entanglement between two masses and predict an improvement of two orders of magnitude in the precision of entanglement estimation compared to existing techniques.

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