This diagram
demonstrates the simplified results that can be obtained by using quantum
analysis
on enormous,
complex sets of data. Shown here are the connections between different regions
of the brain in a
control subject (left) and a subject under the influence of the psychedelic
compound
psilocybin
(right). This demonstrates a dramatic increase in connectivity, which explains
some of the
drug’s effects
(such as “hearing” colors or “seeing” smells). Such an analysis, involving
billions of brain
cells, would be
too complex for conventional techniques, but could be handled easily by the new
quantum approach,
the researchers say. Courtesy of the researchers
(January 25, 2016) System
for handling massive digital datasets could make impossibly complex problems
solvable.
From gene mapping to space exploration, humanity continues
to generate ever-larger sets of data — far more information than people can
actually process, manage, or understand.
Machine learning systems can help researchers deal with this
ever-growing flood of information. Some of the most powerful of these
analytical tools are based on a strange branch of geometry called topology,
which deals with properties that stay the same even when something is bent and
stretched every which way.
Such topological systems are especially useful for analyzing
the connections in complex networks, such as the internal wiring of the brain,
the U.S. power grid, or the global interconnections of the Internet. But even
with the most powerful modern supercomputers, such problems remain daunting and
impractical to solve. Now, a new approach that would use quantum computers to
streamline these problems has been developed by researchers at MIT, the
University of Waterloo, and the University of Southern California.
The team describes their theoretical proposal this week in
the journal Nature Communications. Seth Lloyd, the paper’s lead author and the
Nam P. Suh Professor of Mechanical Engineering, explains that algebraic
topology is key to the new method. This approach, he says, helps to reduce the
impact of the inevitable distortions that arise every time someone collects
data about the real world.
In a topological description, basic features of the data
(How many holes does it have? How are the different parts connected?) are
considered the same no matter how much they are stretched, compressed, or
distorted. Lloyd explains that it is often these fundamental topological
attributes “that are important in trying to reconstruct the underlying patterns
in the real world that the data are supposed to represent.”