25,374 research outputs found
Machine Learning Quantum Systems with Magnetic p-bits
The slowing down of Moore's Law has led to a crisis as the computing
workloads of Artificial Intelligence (AI) algorithms continue skyrocketing.
There is an urgent need for scalable and energy-efficient hardware catering to
the unique requirements of AI algorithms and applications. In this environment,
probabilistic computing with p-bits emerged as a scalable, domain-specific, and
energy-efficient computing paradigm, particularly useful for probabilistic
applications and algorithms. In particular, spintronic devices such as
stochastic magnetic tunnel junctions (sMTJ) show great promise in designing
integrated p-computers. Here, we examine how a scalable probabilistic computer
with such magnetic p-bits can be useful for an emerging field combining machine
learning and quantum physics
A Projective Simulation Scheme for Partially-Observable Multi-Agent Systems
We introduce a kind of partial observability to the projective simulation
(PS) learning method. It is done by adding a belief projection operator and an
observability parameter to the original framework of the efficiency of the PS
model. I provide theoretical formulations, network representations, and
situated scenarios derived from the invasion toy problem as a starting point
for some multi-agent PS models.Comment: 28 pages, 21 figure
Big-Data-Driven Materials Science and its FAIR Data Infrastructure
This chapter addresses the forth paradigm of materials research -- big-data
driven materials science. Its concepts and state-of-the-art are described, and
its challenges and chances are discussed. For furthering the field, Open Data
and an all-embracing sharing, an efficient data infrastructure, and the rich
ecosystem of computer codes used in the community are of critical importance.
For shaping this forth paradigm and contributing to the development or
discovery of improved and novel materials, data must be what is now called FAIR
-- Findable, Accessible, Interoperable and Re-purposable/Re-usable. This sets
the stage for advances of methods from artificial intelligence that operate on
large data sets to find trends and patterns that cannot be obtained from
individual calculations and not even directly from high-throughput studies.
Recent progress is reviewed and demonstrated, and the chapter is concluded by a
forward-looking perspective, addressing important not yet solved challenges.Comment: submitted to the Handbook of Materials Modeling (eds. S. Yip and W.
Andreoni), Springer 2018/201
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