论文标题
最佳利用嘈杂测量的贝叶斯信息引擎
Bayesian information engine that optimally exploits noisy measurements
论文作者
论文摘要
我们通过实验意识到了一个信息引擎，该信息引擎由光学困在水中的沉重珠子组成。该设备在良好的“向上”热波动后提高了陷阱中心，从而增加了珠的平均重力势能。在存在测量噪声的情况下，反馈决策不佳会降低其性能。在关键的信噪比之下，发动机显示出相变，无法存储任何引力能。但是，使用贝叶斯对珠的位置做出反馈决策的估计可以在所有测量噪声强度下提取重力能量，并在关键的信噪比下具有最大的性能益处。
We have experimentally realized an information engine consisting of an optically trapped, heavy bead in water. The device raises the trap center after a favorable "up" thermal fluctuation, thereby increasing the bead's average gravitational potential energy. In the presence of measurement noise, poor feedback decisions degrade its performance; below a critical signal-to-noise ratio, the engine shows a phase transition and cannot store any gravitational energy. However, using Bayesian estimates of the bead's position to make feedback decisions can extract gravitational energy at all measurement noise strengths and has maximum performance benefit at the critical signal-to-noise ratio.
