Physical reservoir computing (RC) is a machine learning algorithm that
employs the dynamics of a physical system to forecast highly nonlinear and
chaotic phenomena. In this paper, we introduce a quantum RC system that employs
the dynamics of a probed atom in a cavity. The atom experiences coherent
driving at a particular rate, leading to a measurement-controlled quantum
evolution. The proposed quantum reservoir can make fast and reliable forecasts
using a small number of artificial neurons compared with the traditional RC
algorithm. We theoretically validate the operation of the reservoir,
demonstrating its potential to be used in error-tolerant applications, where
approximate computing approaches may be used to make feasible forecasts in
conditions of limited computational and energy resources.

我们引入了一种量子 RC 系统，利用共振腔中的受探测原子的动力学，提出的量子储层可以使用较少的人工神经元进行快速可靠的预测，理论上验证了储层的操作并展示了在计算和能源资源有限的条件下使用近似计算方法进行可行预测的潜力。

使用测量控制的量子动力学进行储备计算

Reservoir Computing Using Measurement-Controlled Quantum Dynamics

The recent advances in autonomous systems have prompted a strong demand for
the next generation of adaptive structures and materials to possess more
built-in intelligence in their mechanical domain, the so-called
mechano-intelligence (MI). Previous MI attempts mainly focused on specific
designs and case studies to realize limited aspects of MI, and there is a lack
of a systematic foundation in constructing and integrating the different
elements of intelligence in an effective and efficient manner. Here, we propose
a new approach to create the needed foundation in realizing integrated
multifunctional MI via a physical reservoir computing (PRC) framework. That is,
to concurrently embody computing power and the various elements of
intelligence, namely perception, decision-making, and commanding, directly in
the mechanical domain, advancing from conventional adaptive structures that
rely solely on add-on digital computers and massive electronics to achieve
intelligence. As an exemplar platform, we construct a mechanically intelligent
phononic metastructure with the integrated elements of MI by harnessing the PRC
power hidden in their high-degree-of-freedom nonlinear dynamics. Through
analyses and experimental investigations, we uncover multiple adaptive
structural functions ranging from self-tuning wave controls to wave-based logic
gates. This research will provide the basis for creating future new structures
that would greatly surpass the state of the art - such as lower power
consumption, more direct interactions, and much better survivability in harsh
environment or under cyberattacks. Moreover, it will enable the addition of new
functions and autonomy to systems without overburdening the onboard computers.

该研究采用物理储备计算 (PRC) 框架，直接在机械领域实现了感知、决策和命令等智能元素，构建了一个具有多功能机械智能的声子超结构，并通过实验验证了其自适应结构功能，为未来创造更具生存能力、低功耗和其他附加功能的多功能自适应结构提供了基础。

利用声子超材料挖掘和通过物理储备计算揭示多功能的机械智能

Uncovering multifunctional mechano-intelligence in and through phononic  metastructures harnessing physical reservoir computing

This study demonstrates that the soft biological tissues of humans can be
used as a type of soft body in physical reservoir computing. Soft biological
tissues possess characteristics such as stress-strain nonlinearity and
viscoelasticity that satisfy the requirements for physical reservoir computing,
including nonlinearity and memory. The aim of this study was to utilize the
dynamics of human soft tissues as a physical reservoir for the emulation of
nonlinear dynamical systems. To demonstrate this concept, joint angle data
during motion in the flexion-extension direction of the wrist joint, and
ultrasound images of the muscles associated with that motion, were acquired
from human participants. The input to the system was the angle of the wrist
joint, while the deformation field within the muscle (obtained from ultrasound
images) represented the state of the reservoir. The results indicate that the
dynamics of soft tissue have a positive impact on the computational task of
emulating nonlinear dynamical systems. This research suggests that the soft
tissue of humans can be used as a potential computational resource.

本研究发现人类柔软的生物组织可以作为一种物理谐振器的类型，在模拟非线性动力学系统时具有积极作用，这表明人类柔软的组织可以被用作一种潜在的计算资源。