SHENZHEN, China, May 1, 2025
/PRNewswire/ -- MicroAlgo Inc. (the "Company" or "MicroAlgo")
(NASDAQ: MLGO), announced today that their newly
developed quantum edge detection algorithm has broken through the
limitations of classical methods. This technology optimizes the
feature extraction process through quantum circuits, reducing
computational complexity from O(N²) to O(N) while maintaining
detection accuracy, thereby providing new solutions for real-time
image processing and edge intelligence devices.
The quantum image edge detection algorithm is based on quantum
state encoding and quantum convolution principles. It maps image
pixel information into quantum state vectors and performs feature
enhancement and edge extraction through quantum gate operations.
The core idea is to leverage quantum parallelism to simultaneously
process multiple pixel neighborhoods, using quantum superposition
states to simulate the weighted summation process of classical
convolution kernels. For example, the quantum Sobel operator
enhances gradient responses in edge regions through quantum
amplitude amplification techniques, while the quantum Canny
algorithm utilizes quantum state entanglement to achieve
collaborative multi-scale edge detection. Compared to classical
algorithms, quantum methods demonstrate significant advantages in
noise robustness, multi-scale feature fusion, and computational
energy efficiency.
MicroAlgo's quantum edge detection technology follows a hybrid
architecture of "quantum preprocessing - quantum feature extraction
- classical post-processing."
Image Quantum Encoding: A two-dimensional image matrix is
converted into a quantum state input. Using amplitude encoding
techniques, pixel grayscale values are mapped to the probability
amplitudes of quantum states, and spatial domain information is
transformed into a frequency domain representation via the quantum
Fourier transform. For instance, for an 8-bit grayscale image, 3
qubits are used to encode each pixel, with quantum superposition
states simultaneously representing the feature information of
multiple pixels.
Quantum Edge Detection Operations: A quantum convolution circuit
is constructed to simulate an edge detection kernel. Parameterized
quantum gates (such as RY gates and CNOT gates) are used to design
trainable quantum filters, dynamically adjusting the sensitivity
and directionality of edge detection. For example, a quantum
directional gradient operator achieves multi-directional edge
responses by rotating the phase of quantum states, while a quantum
noise suppression circuit leverages quantum error correction codes
to reduce the impact of salt-and-pepper noise.
Quantum Measurement and Result Decoding: Projective measurements
are performed on the quantum states, converting quantum probability
amplitudes into classical probability distributions. Edge images
are reconstructed using maximum likelihood estimation or Bayesian
inference, followed by binarization processing with adaptive
thresholding algorithms (e.g., Otsu).
Hybrid Optimization Framework: A variational quantum algorithm
(VQA) is employed to optimize the parameters of the quantum
circuit. A classical optimizer (e.g., Adam) adjusts the quantum
gate parameters based on edge detection performance metrics (such
as recall and accuracy), achieving algorithm adaptability through a
quantum-classical feedback loop.
MicroAlgo's quantum machine learning algorithms leverage quantum
state superposition and parallel processing capabilities to achieve
groundbreaking improvements in computational efficiency, resource
consumption, model generalization, and hardware compatibility. Its
Quantum Principal Component Analysis (QPCA) reduces the time
complexity of high-dimensional data feature extraction from O(N²)
in classical algorithms to O(N), with energy consumption only
1/100th that of traditional GPU clusters. The quantum state
superposition property significantly expands the feature
exploration space, effectively avoiding local optima issues. A
cross-platform quantum programming framework supports various types
of quantum computers, such as superconducting and ion-trap systems,
lowering the barriers to technological implementation and providing
revolutionary solutions for fields like drug development, financial
risk control, and image recognition.
The quantum edge detection algorithm has already been applied in
practical scenarios across medical imaging analysis, remote sensing
image processing, industrial quality inspection, and autonomous
driving. In the medical field, it precisely locates brain tumor
boundaries in MRI scans, enhancing detection speed. In remote
sensing, it rapidly extracts waterlines under complex sea
conditions, reducing false detection rates. In industrial quality
inspection, it enables sub-pixel-level crack detection in precision
components, lowering miss rates. In autonomous driving, combined
with LiDAR data, it improves lane line recognition accuracy in
heavy rain, extending effective recognition distance.
Looking ahead, MicroAlgo's quantum edge detection algorithm will
further expand into areas such as multimodal image fusion,
encrypted image analysis, and photonic quantum chip integration,
reshaping image processing paradigms in fields like intelligent
security and biomedical research.
About MicroAlgo Inc.
MicroAlgo Inc. (the "MicroAlgo"), a Cayman
Islands exempted company, is dedicated to the development and
application of bespoke central processing algorithms. MicroAlgo
provides comprehensive solutions to customers by integrating
central processing algorithms with software or hardware, or both,
thereby helping them to increase the number of customers, improve
end-user satisfaction, achieve direct cost savings, reduce power
consumption, and achieve technical goals. The range of
MicroAlgo's services includes algorithm optimization,
accelerating computing power without the need for hardware
upgrades, lightweight data processing, and data intelligence
services. MicroAlgo's ability to efficiently deliver software and
hardware optimization to customers through bespoke central
processing algorithms serves as a driving force for MicroAlgo's
long-term development.
Forward-Looking Statements
This press release contains statements that may constitute
"forward-looking statements." Forward-looking statements are
subject to numerous conditions, many of which are beyond the
control of MicroAlgo, including those set forth in the Risk Factors
section of MicroAlgo's periodic reports on
Forms 10-K and 8-K filed with the SEC. Copies are
available on the SEC's website, www.sec.gov. Words such as
"expect," "estimate," "project," "budget," "forecast,"
"anticipate," "intend," "plan," "may," "will," "could," "should,"
"believes," "predicts," "potential," "continue," and similar
expressions are intended to identify such forward-looking
statements. These forward-looking statements include, without
limitation, MicroAlgo's expectations with respect to future
performance and anticipated financial impacts of the business
transaction.
MicroAlgo undertakes no obligation to update these statements
for revisions or changes after the date of this release, except as
may be required by law.
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SOURCE Microalgo.INC