arXiv (Cornell University),
Год журнала:
2023,
Номер
unknown
Опубликована: Янв. 1, 2023
Computational
models
are
an
essential
tool
for
the
design,
characterization,
and
discovery
of
novel
materials.
Hard
computational
tasks
in
materials
science
stretch
limits
existing
high-performance
supercomputing
centers,
consuming
much
their
simulation,
analysis,
data
resources.
Quantum
computing,
on
other
hand,
is
emerging
technology
with
potential
to
accelerate
many
needed
science.
In
order
do
that,
quantum
must
interact
conventional
computing
several
ways:
approximate
results
validation,
identification
hard
problems,
synergies
quantum-centric
supercomputing.
this
paper,
we
provide
a
perspective
how
can
help
address
critical
problems
science,
challenges
face
solve
representative
use
cases,
new
suggested
directions.
Physical review. A/Physical review, A,
Год журнала:
2024,
Номер
110(3)
Опубликована: Сен. 25, 2024
We
introduce
a
hybrid
quantum-classical
algorithm
to
compute
the
Green
function
for
strongly
correlated
electrons
on
noisy
intermediate-scale
quantum
(NISQ)
devices.The
technique
consists
in
construction
of
non-orthogonal
excitation
basis
composed
set
single-particle
excitations
generated
by
occupation
number
operators.The
excited
sectors
Hamiltonian
this
can
then
be
measured
device
and
classical
post-processing
procedure
yields
Lehmann
representation.The
allows
noise
filtering,
useful
feature
NISQ
devices.To
validate
approach,
we
carry
out
proof-of-principle
calculations
single-band
Hubbard
model
IBM
hardware.For
2
site
system
find
good
agreement
between
results
simulations
exact
result
local
spectral
function.This
also
shows
that
filtering
provides
reliable
way
get
rid
satellite
peaks
present
weight
obtained
from
device.A
simulation
4
carried
hardware
suggests
approach
achieve
similar
accuracy
larger
systems.
arXiv (Cornell University),
Год журнала:
2023,
Номер
unknown
Опубликована: Янв. 1, 2023
Computational
models
are
an
essential
tool
for
the
design,
characterization,
and
discovery
of
novel
materials.
Hard
computational
tasks
in
materials
science
stretch
limits
existing
high-performance
supercomputing
centers,
consuming
much
their
simulation,
analysis,
data
resources.
Quantum
computing,
on
other
hand,
is
emerging
technology
with
potential
to
accelerate
many
needed
science.
In
order
do
that,
quantum
must
interact
conventional
computing
several
ways:
approximate
results
validation,
identification
hard
problems,
synergies
quantum-centric
supercomputing.
this
paper,
we
provide
a
perspective
how
can
help
address
critical
problems
science,
challenges
face
solve
representative
use
cases,
new
suggested
directions.
