Developments in quantum computing and, more in general, non-standard
computing systems, represent a clear indication that the very notion of what a
physical computing device is and does should be recast in a rigorous and sound
framework. Physical computing has opened a whole stream of new research aimed
to understand and control how information is processed by several types of
physical devices. Therefore, classical definitions and entire frameworks need
to be adapted in order to fit a broader notion of what physical computing
systems really are. Recent studies have proposed a formalism that can be used
to carve out a more proper notion of physical computing. In this paper we
present a framework which capture such results in a very natural way via some
basic constructions in Category Theory. Furthermore, we show that, within our
framework, the compositional nature of physical computing systems is naturally
formalized, and that it can be organized in coherent structures by the means of
their relational nature.

本文研究量子计算、非标准计算系统、物理计算等内容，通过分类学基本概念提出一种适用于物理计算的规范框架，以此建立物理计算系统的组合性和关联性。

物理计算和组合性

Physical computation and compositionality

A ''technology lottery'' describes a research idea or technology succeeding
over others because it is suited to the available software and hardware, not
necessarily because it is superior to alternative directions--examples abound,
from the synergies of deep learning and GPUs to the disconnect of urban design
and autonomous vehicles. The nascent field of Self-Driving Laboratories (SDL),
particularly those implemented as Materials Acceleration Platforms (MAPs), is
at risk of an analogous pitfall: the next logical step for building MAPs is to
take existing lab equipment and workflows and mix in some AI and automation. In
this whitepaper, we argue that the same simulation and AI tools that will
accelerate the search for new materials, as part of the MAPs research program,
also make possible the design of fundamentally new computing mediums. We need
not be constrained by existing biases in science, mechatronics, and
general-purpose computing, but rather we can pursue new vectors of engineering
physics with advances in cyber-physical learning and closed-loop,
self-optimizing systems. Here we outline a simulation-based MAP program to
design computers that use physics itself to solve optimization problems. Such
systems mitigate the hardware-software-substrate-user information losses
present in every other class of MAPs and they perfect alignment between
computing problems and computing mediums eliminating any technology lottery. We
offer concrete steps toward early ''Physical Computing (PC) -MAP'' advances and
the longer term cyber-physical R&D which we expect to introduce a new era of
innovative collaboration between materials researchers and computer scientists.

本文阐述了自动驾驶实验室的物质加速平台以及利用模拟技术和人工智能设计计算机新媒介，解决软硬件信息丢失问题和建立计算问题与计算媒介间的完美对齐，为新一代的材料和计算机科学研究提供了创新合作方向。