Quantum-Mechanical Modelling of Asymmetric Opinion Polarisation in Social Networks
Information,
Год журнала:
2024,
Номер
15(3), С. 170 - 170
Опубликована: Март 20, 2024
We
propose
a
quantum-mechanical
model
that
represents
human
system
of
beliefs
as
the
quantised
energy
levels
physical
system.
This
novel
perspective
on
opinion
dynamics,
recreating
broad
range
experimental
and
real-world
data
exhibit
an
asymmetry
radicalisation.
In
particular,
demonstrates
phenomena
pronounced
conservatism
versus
mild
liberalism
when
individuals
are
exposed
to
opposing
views,
mirroring
recent
findings
polarisation
via
social
media
exposure.
Advancing
this
model,
we
establish
robust
framework
integrates
elements
from
physics,
psychology,
behavioural
science,
decision-making
theory,
philosophy.
also
emphasise
inherent
advantages
quantum
approach
over
traditional
models,
suggesting
number
new
directions
for
future
research
work
models
cognition
decision-making.
Язык: Английский
Reservoir Computing Using Measurement-Controlled Quantum Dynamics
Electronics,
Год журнала:
2024,
Номер
13(6), С. 1164 - 1164
Опубликована: Март 21, 2024
Physical
reservoir
computing
(RC)
is
a
machine
learning
algorithm
that
employs
the
dynamics
of
physical
system
to
forecast
highly
nonlinear
and
chaotic
phenomena.
In
this
paper,
we
introduce
quantum
RC
probed
atom
in
cavity.
The
experiences
coherent
driving
at
particular
rate,
leading
measurement-controlled
evolution.
proposed
can
make
fast
reliable
forecasts
using
small
number
artificial
neurons
compared
with
traditional
algorithm.
We
theoretically
validate
operation
reservoir,
demonstrating
its
potential
be
used
error-tolerant
applications,
where
approximate
approaches
may
feasible
conditions
limited
computational
energy
resources.
Язык: Английский
The Physics of Preference: Unravelling Imprecision of Human Preferences through Magnetisation Dynamics
Information,
Год журнала:
2024,
Номер
15(7), С. 413 - 413
Опубликована: Июль 18, 2024
Paradoxical
decision-making
behaviours
such
as
preference
reversal
often
arise
from
imprecise
or
noisy
human
preferences.
Harnessing
the
physical
principle
of
magnetisation
in
ferromagnetic
nanostructures,
we
developed
a
model
that
closely
reflects
dynamics.
Tested
against
spectrum
psychological
data,
our
adeptly
captures
complexities
inherent
individual
choices.
This
blend
physics
and
psychology
paves
way
for
fresh
perspectives
on
understanding
imprecision
processes,
extending
reach
current
classical
quantum
models
behaviour
decision
making.
Язык: Английский
Classical and Quantum Physical Reservoir Computing for Onboard Artificial Intelligence Systems: A Perspective
Dynamics,
Год журнала:
2024,
Номер
4(3), С. 643 - 670
Опубликована: Авг. 12, 2024
Artificial
intelligence
(AI)
systems
of
autonomous
such
as
drones,
robots
and
self-driving
cars
may
consume
up
to
50%
the
total
power
available
onboard,
thereby
limiting
vehicle’s
range
functions
considerably
reducing
distance
vehicle
can
travel
on
a
single
charge.
Next-generation
onboard
AI
need
an
even
higher
since
they
collect
process
larger
amounts
data
in
real
time.
This
problem
cannot
be
solved
using
traditional
computing
devices
become
more
power-consuming.
In
this
review
article,
we
discuss
perspectives
development
neuromorphic
computers
that
mimic
operation
biological
brain
nonlinear–dynamical
properties
natural
physical
environments
surrounding
vehicles.
Previous
research
also
demonstrated
quantum
processors
(QNPs)
conduct
computations
with
efficiency
standard
computer
while
consuming
less
than
1%
battery
power.
Since
QNPs
are
semi-classical
technology,
their
technical
simplicity
low
cost
compared
make
them
ideally
suited
for
applications
systems.
Providing
perspective
future
progress
unconventional
reservoir
surveying
outcomes
200
interdisciplinary
works,
article
will
interest
broad
readership,
including
both
students
experts
fields
physics,
engineering,
technologies
computing.
Язык: Английский
Quantum-tunneling deep neural network for optical illusion recognition
APL Machine Learning,
Год журнала:
2024,
Номер
2(3)
Опубликована: Авг. 22, 2024
The
discovery
of
the
quantum
tunneling
(QT)
effect—the
transmission
particles
through
a
high
potential
barrier—was
one
most
impressive
achievements
mechanics
made
in
1920s.
Responding
to
contemporary
challenges,
I
introduce
deep
neural
network
(DNN)
architecture
that
processes
information
using
effect
QT.
demonstrate
ability
QT-DNN
recognize
optical
illusions
like
human.
Tasking
simulate
human
perception
Necker
cube
and
Rubin’s
vase,
provide
arguments
favor
superiority
QT-based
activation
functions
over
optimized
for
modern
applications
machine
vision,
also
showing
that,
at
fundamental
level,
is
closely
related
biology-inspired
DNNs
models
based
on
principles
processing.
Язык: Английский
Magnetism-Inspired Quantum-Mechanical Model of Gender Fluidity
Опубликована: Янв. 27, 2024
Quantum-mechanical
models
of
human
cognition,
opinion
formation
and
decision-making
have
changed
the
way
we
understand
predict
behaviour
in
many
practical
situations,
including
political
elections,
financial
decisions
international
affairs.
Yet,
at
present,
such
overlook
certain
essential
social
aspects
self-identification.
In
this
paper,
introduce
a
magnetism-inspired
quantum-mechanical
model
gender
fluidity,
concept
that
challenges
norms
across
globe.
Addressing
number
independent
suggestions
made
by
members
general
public
concerning
potential
analogy
between
quantum
superposition
non-binary
self-identification,
explore
new
territories,
demonstrating
physic
magnetism
can
help
explain
fluidity
similar
phenomena
better
than
traditional
cognition
perception.
We
anticipate
proposed
be
used
to
analyse
experimental
datasets
aimed
develop
sexual
orientation
identity
legal
definitions
as
well
create
artificial
intelligence
systems
sensibly
identify
both
binary
genders.
Язык: Английский
Optical Illusions Recognition Intelligence
Опубликована: Июль 20, 2024
Quantum Machine Learning, Leveraging AI, and Semiconductor Technology
Advances in mechatronics and mechanical engineering (AMME) book series,
Год журнала:
2024,
Номер
unknown, С. 57 - 78
Опубликована: Окт. 11, 2024
This
chapter
explores
the
intersection
of
quantum
computing,
artificial
intelligence
(AI),
and
semiconductor
technology,
focusing
specifically
on
emerging
field
machine
learning
(QML).
Quantum
computing
promises
to
revolutionize
traditional
algorithms
by
leveraging
principles
mechanics
perform
computations
at
exponentially
faster
speeds.
will
delve
into
fundamentals
technologies
relevant
QML,
highlighting
challenges
opportunities
in
scaling
up
integrated
AI-quantum
systems.
It
discuss
convergence
AI
exploring
development
tailored
for
information
processing
hardware
implementations
acceleration.
Case
studies
industry
applications
illustrate
potential
QML
cybersecurity,
drug
discovery,
material
science,
other
domains,
while
addressing
ethical
societal
implications
future
trends
challenges.
Язык: Английский