The real-time data processing framework for blockchain and edge computing
Alexandria Engineering Journal,
Год журнала:
2025,
Номер
120, С. 50 - 61
Опубликована: Фев. 11, 2025
Язык: Английский
Quantum Machine Learning: Exploring the Role of Data Encoding Techniques, Challenges, and Future Directions
Mathematics,
Год журнала:
2024,
Номер
12(21), С. 3318 - 3318
Опубликована: Окт. 23, 2024
Quantum
computing
and
machine
learning
(ML)
have
received
significant
developments
which
set
the
stage
for
next
frontier
of
creative
work
usefulness.
This
paper
aims
at
reviewing
various
data-encoding
techniques
in
Machine
Learning
(QML)
while
highlighting
their
significance
transforming
classical
data
into
quantum
systems.
We
analyze
basis,
amplitude,
angle,
other
high-level
encodings
depth
to
demonstrate
how
strategies
affect
encoding
improvements
algorithms.
However,
they
identify
major
problems
with
framework
QML,
including
scalability,
computational
burden,
noise.
Future
directions
research
outline
these
challenges,
aiming
enhance
excellence
constantly
evolving
technology
setting.
review
shall
enable
researcher
gain
an
enhanced
understanding
it
also
suggests
solutions
current
limitations
this
area.
Язык: Английский
A blockchain-assisted privacy-preserving signature scheme using quantum teleportation for metaverse environment in Web 3.0
Future Generation Computer Systems,
Год журнала:
2024,
Номер
164, С. 107581 - 107581
Опубликована: Ноя. 4, 2024
Язык: Английский
A decentralized authentication scheme for smart factory based on blockchain
Scientific Reports,
Год журнала:
2024,
Номер
14(1)
Опубликована: Окт. 20, 2024
Язык: Английский
Security Analysis and Improvement of Authenticated Key Agreement Protocol for Remote Patient Monitoring IoMT
Опубликована: Окт. 16, 2024
Язык: Английский
Blockchain-Assisted Cross-Platform Authentication Protocol with Conditional Traceability for Metaverse Environment in Web 3.0
IEEE Open Journal of the Communications Society,
Год журнала:
2024,
Номер
5, С. 7244 - 7261
Опубликована: Янв. 1, 2024
Язык: Английский
Hybrid Quantum–Classical Neural Networks for Efficient MNIST Binary Image Classification
Mathematics,
Год журнала:
2024,
Номер
12(23), С. 3684 - 3684
Опубликована: Ноя. 24, 2024
Image
classification
is
a
fundamental
task
in
deep
learning,
and
recent
advances
quantum
computing
have
generated
significant
interest
neural
networks.
Traditionally,
Convolutional
Neural
Networks
(CNNs)
are
employed
to
extract
image
features,
while
Multilayer
Perceptrons
(MLPs)
handle
decision
making.
However,
parameterized
circuits
offer
the
potential
capture
complex
features
define
sophisticated
boundaries.
In
this
paper,
we
present
novel
Hybrid
Quantum–Classical
Network
(H-QNN)
for
classification,
demonstrate
its
effectiveness
using
MNIST
dataset.
Our
model
combines
with
classical
supervised
learning
enhance
accuracy
computational
efficiency.
study,
detail
architecture
of
H-QNN,
emphasizing
capability
feature
classification.
Experimental
results
that
proposed
H-QNN
outperforms
conventional
methods
various
training
scenarios,
showcasing
high-dimensional
tasks.
Additionally,
explore
broader
applicability
hybrid
quantum–classical
approaches
other
domains.
findings
contribute
growing
body
work
machine
underscore
quantum-enhanced
models
recognition
Язык: Английский