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allied
academies
WORLD CONGRESS ON SMART MATERIALS AND STRUCTURES
&
3
rd
International Conference on
POLYMER CHEMISTRY AND MATERIALS ENGINEERING
November 21-22, 2019 | Singapore
Joint event on
Materials Science and Nanotechnology | Volume: 03
Algorithmicallydiscoveringhigh temperaturesuperconductorswithquantumcomputers
Deep Prasad
ReactiveQ, Canada
S
uperconductors play an integral role in magnetic
resonance imaging (MRI), nuclear magnetic resonance
(NMR) and fusion reactors for magnetic confinement.
When they were first discovered in the early 20th century,
it was unclear what physics went behind making them
work. Since then, we have come a long way in describing
at least one class of superconductors: low temperature,
Type I and Type II superconductors. The mechanism giving
rise to this class of low temperature superconductors is
quantummechanical in nature. Therefore, it is conceivable
that such processes can be modelled easier and with more
robustness on quantum computers as opposed to classical
computers. This modelling ability can then be exploited to
explore a broader search space of other superconductors
that may have not been discovered yet.
ReactiveQ has created a computational engineering
platform that allows for multi-physics simulations to
be run on classical supercomputers as well as quantum
computers. In lieu of accelerating the discovery of new
materials, namely superconductors, ReactiveQ looked at
the viability of both near-term, Noisy Intermediate Scale
Quantum (NISQ) algorithms as well as long-term Universal
Gate model algorithms that could be used to automate the
discovery of high temperature superconductors.
e:
Deep@reactiveq.ioMater Sci Nanotechnol, Volume: 03
Notes: