67 XRDS • WINTER 2019 • VOL. 26 • NO. 2
computational complexity theory
SM: The Quantum Information
Center is only involved in the theory of
quantum computing or are there real-
world applications to your work?
DL: Well building a quantum computer that will be able to exact out the
theoretical models that we are using is
going to take a really long time as far
as we know, and we are not anywhere
close to being factor numbers large
enough to break modern cryptosys-tems using RSA.
SM: Didn’t Google just demonstrate
a quantum computer?
DL: What they are doing is basically
showing that the classical computers
cannot efficiently simulate a quantum computer in a specific regime
and that they have created a quantum
computer that is still pretty noisy and
impractical of doing anything useful,
but is just quantum enough to show
that some quantum stuff is happening. That is a high-level explanation of
what is going on.
SM: What is the research project you
are currently involved in?
DL: I have been looking into quantum learning theory and more specifically what things we can learn using
quantum computers. That is what I
have been doing recently.
SM: So what is an example of
things we can learn using quantum
DL: One of the first things that was
shown in quantum PAC learning is
that under a uniform distribution you
can learn DNFs, which is not known
classically how to do.
For more information on the Quantum Information Center at the University of Texas at Austin, you can
visit Dr. Scott Aaronson’s webpage at
— Sepideh Maleki
DOI: 10.1145/3368081 Copyright held by author.
CS + CS =
When one brings up the phrase “farming tools,” most probably
one would not immediately conjure the imagery of a computer
in their mind. For the majority of history, farming has been
associated with manual labor rather than cubicles filled with
software engineers and data scientists. However, the earliest use
of computers in farming most likely began with the digital storage
of accounting information before extending to other applications
such as yield analysis. Computational power aided the
development of Green Revolution technologies, which changed the
face of agriculture for many. While countless debate the positive
impact of this movement, the technological shift produced has
only continued to grow.
Added in 2018, Computer Science + Crop Sciences is one of
the newest undergraduate majors at the University of Illinois
at Urbana-Champaign, reflecting the broader demand for the
intersection between these fields. Today agricultural tools
are growing to accommodate hardware such as Io T sensors,
robotic tools, satellites, and drones. Emerging developments
in bioinformatics and genomic sequencing are also advancing
farming technologies, in addition to machine learning and
GIS. Today, Ag Tech startups across the globe are thriving, and
innovation from different agricultural philosophies will be
necessary to battle the challenges associated with the world’s
increasing population and resource demands.
— Daniela Zieba
DOI: 10.1145/3368083 Copyright held by the author. I m
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