MicroAlgo Inc. Develops Quantum Edge Detection Algorithm, Offering New Solutions for Real-Time Image Processing and Edge Intelligence Devices

Rhea-AI Summary

MicroAlgo (NASDAQ: MLGO) has announced a breakthrough in quantum edge detection algorithm development. The new technology reduces computational complexity from O(N²) to O(N) while maintaining accuracy for real-time image processing. The algorithm uses quantum state encoding and quantum convolution principles, leveraging quantum parallelism for simultaneous pixel processing. The technology follows a hybrid architecture combining quantum preprocessing, feature extraction, and classical post-processing. Notable achievements include energy consumption at 1/100th of traditional GPU clusters and successful applications in medical imaging, remote sensing, industrial inspection, and autonomous driving. Key implementations include precise brain tumor boundary detection in MRI scans, waterline extraction in complex sea conditions, sub-pixel-level crack detection in industrial components, and improved lane line recognition in autonomous vehicles during adverse weather conditions.

Positive

• Significant reduction in computational complexity from O(N²) to O(N)
• Energy consumption reduced to 1/100th of traditional GPU clusters
• Successfully implemented in multiple high-value applications (medical imaging, autonomous driving, industrial inspection)
• Cross-platform quantum programming framework supporting various quantum computer types

Negative

• None.

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

FAQ

What is MLGO's new quantum edge detection algorithm and how does it improve performance?

MLGO's quantum edge detection algorithm uses quantum state encoding and convolution principles to reduce computational complexity from O(N²) to O(N) while maintaining accuracy. It processes multiple pixels simultaneously through quantum parallelism.

How much energy does MLGO's quantum algorithm save compared to traditional methods?

The algorithm consumes only 1/100th of the energy compared to traditional GPU clusters, offering significant efficiency improvements.

What are the main applications of MLGO's quantum edge detection technology?

The technology is applied in medical imaging (tumor detection), remote sensing (waterline extraction), industrial quality inspection (crack detection), and autonomous driving (lane recognition in poor weather).

How does MLGO's quantum edge detection algorithm work in autonomous driving?

Combined with LiDAR data, the algorithm improves lane line recognition accuracy in heavy rain conditions and extends effective recognition distance for autonomous vehicles.

What is the architecture of MLGO's quantum edge detection system?

The system uses a hybrid architecture combining quantum preprocessing, quantum feature extraction, and classical post-processing, with quantum state encoding for image processing.