Latest news with #MicroAlgoInc
Business Upturn
09-06-2025
- Business
- Business Upturn
MicroAlgo Inc. Integrates Quantum Image LSQb Algorithm with Quantum Encryption Technology to Build a More Secure Quantum Information Hiding and Transmission System
By GlobeNewswire Published on June 9, 2025, 18:30 IST shenzhen, June 09, 2025 (GLOBE NEWSWIRE) — MicroAlgo Inc. Integrates Quantum Image LSQb Algorithm with Quantum Encryption Technology to Build a More Secure Quantum Information Hiding and Transmission System Shenzhen, Jun. 09, 2025––MicroAlgo Inc. (the 'Company' or 'MicroAlgo') (NASDAQ: MLGO), today announced that by integrating the quantum image LSQb algorithm with quantum encryption technology, they have proposed a brand-new information hiding and transmission scheme, aiming to build a more secure and efficient data protection LSQb algorithm, namely the Least Significant Quantum Bit algorithm for quantum images, is an innovative quantum image processing technology. It achieves secure information hiding by embedding secret information into the least significant quantum bits of a quantum image. Building on this foundation, MicroAlgo further integrates relevant theories from quantum information theory and cryptography, comprehensively expanding the application scope and functionality of the LSQb algorithm. This integration not only enhances the security of information hiding but also improves the efficiency and reliability of information transmission in quantum core of MicroAlgo's technological innovation lies in utilizing the Least Significant Quantum Bit (LSQb) algorithm for efficient information encoding and decoding, combined with quantum encryption technologies such as Quantum Key Distribution (QKD) to ensure data security during transmission. The LSQb algorithm can identify and select key quantum bits critical to image representation, reducing the number of quantum gate operations by optimizing the embedding and extraction processes, thereby lowering algorithm complexity. Meanwhile, quantum encryption technology provides unconditional security for information transmission, ensuring that information leakage is prevented even in a quantum computing Image Preprocessing: First, the original image undergoes compressed sensing and sparse representation to extract key features and convert them into quantum bit form. Further analysis is conducted using machine learning or deep learning models to ensure the retention of important visual elements of the image, reduce the amount of encoded information, and lower algorithm Bit Selection and Embedding: An improved Least Significant Quantum Bit (LSQb) algorithm is employed to embed selected key quantum bits into quantum states. Each quantum bit generates a corresponding quantum state and is embedded into a larger quantum state structure through quantum gate operations. Quantum error correction codes and quantum entanglement properties are introduced to enhance the system's robustness and stability, reducing unnecessary quantum gate Key Distribution and Encryption: Quantum Key Distribution (QKD) technology is utilized to generate a shared key, ensuring the security of data transmission. The sender and receiver exchange correlated quantum states to generate the key, and any attempt to read the states will alter them and be detected, preventing information Transmission and Protection: The encrypted quantum state information is transmitted through a quantum channel, and even if eavesdropping occurs, attackers cannot obtain useful information. By combining protocols such as quantum teleportation, the system's security and flexibility are further Decryption and Recovery: The receiver uses the shared key to decrypt the quantum state information and applies inverse quantum gate operations to restore the original quantum bit sequence. Key feature information is extracted through a decoding algorithm and reassembled into a complete image, with error correction mechanisms introduced to ensure high-fidelity recovery. The entire process validates the effectiveness and accuracy of information hiding and transmission, establishing an efficient and secure quantum information processing integrates the Least Significant Quantum Bit (LSQb) algorithm for quantum images with other related theories, such as quantum information theory and cryptography, to further expand its application scope and functionality. Combined with quantum encryption technology, it constructs a more secure quantum information hiding and transmission system, ensuring the secure transmission of information in quantum networks. On one hand, it significantly reduces the demand for quantum resources, minimizing the involvement of unnecessary quantum bits and the number of quantum gate operations, thereby increasing the algorithm's execution speed. On the other hand, leveraging the unconditional security provided by quantum encryption technology ensures a high level of confidentiality for data during transmission. This not only enhances the efficiency of information processing but also greatly improves the system's resilience to interference, maintaining high information fidelity even in noisy environments. Additionally, by simplifying quantum circuit design, it reduces the cost and technical complexity of hardware implementation, making large-scale commercial applications practical applications, MicroAlgo's novel information hiding and transmission system has already been applied in multiple fields. For example, in medical image encryption, patient privacy data receives a higher level of protection; in financial transaction systems, customers' sensitive financial information is similarly safeguarded effectively. Through this approach, not only is information security enhanced, but processing efficiency is also improved, meeting the modern society's demand for high-speed and efficient data processing. In the future, with continuous advancements in quantum computing and quantum encryption technologies, MicroAlgo's novel information hiding and transmission system is expected to expand beyond its current application scenarios to more emerging fields, such as artificial intelligence and big data analysis. For instance, in the field of artificial intelligence, leveraging the advantages of quantum computing can accelerate the training process of machine learning models; in big data analysis, quantum image processing technology can help extract valuable information from massive datasets more quickly. Through ongoing exploration and practice, quantum image processing technology will become more mature and refined, contributing to the construction of a more secure and efficient information society. 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, 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. Contact MicroAlgo Inc. Investor Relations Email: [email protected] Disclaimer: The above press release comes to you under an arrangement with GlobeNewswire. Business Upturn takes no editorial responsibility for the same. GlobeNewswire provides press release distribution services globally, with substantial operations in North America and Europe.
Yahoo
09-06-2025
- Business
- Yahoo
MicroAlgo Inc. Integrates Quantum Image LSQb Algorithm with Quantum Encryption Technology to Build a More Secure Quantum Information Hiding and Transmission System
shenzhen, June 09, 2025 (GLOBE NEWSWIRE) -- MicroAlgo Inc. Integrates Quantum Image LSQb Algorithm with Quantum Encryption Technology to Build a More Secure Quantum Information Hiding and Transmission System Shenzhen, Jun. 09, 2025––MicroAlgo Inc. (the "Company" or "MicroAlgo") (NASDAQ: MLGO), today announced that by integrating the quantum image LSQb algorithm with quantum encryption technology, they have proposed a brand-new information hiding and transmission scheme, aiming to build a more secure and efficient data protection LSQb algorithm, namely the Least Significant Quantum Bit algorithm for quantum images, is an innovative quantum image processing technology. It achieves secure information hiding by embedding secret information into the least significant quantum bits of a quantum image. Building on this foundation, MicroAlgo further integrates relevant theories from quantum information theory and cryptography, comprehensively expanding the application scope and functionality of the LSQb algorithm. This integration not only enhances the security of information hiding but also improves the efficiency and reliability of information transmission in quantum core of MicroAlgo's technological innovation lies in utilizing the Least Significant Quantum Bit (LSQb) algorithm for efficient information encoding and decoding, combined with quantum encryption technologies such as Quantum Key Distribution (QKD) to ensure data security during transmission. The LSQb algorithm can identify and select key quantum bits critical to image representation, reducing the number of quantum gate operations by optimizing the embedding and extraction processes, thereby lowering algorithm complexity. Meanwhile, quantum encryption technology provides unconditional security for information transmission, ensuring that information leakage is prevented even in a quantum computing Image Preprocessing: First, the original image undergoes compressed sensing and sparse representation to extract key features and convert them into quantum bit form. Further analysis is conducted using machine learning or deep learning models to ensure the retention of important visual elements of the image, reduce the amount of encoded information, and lower algorithm Bit Selection and Embedding: An improved Least Significant Quantum Bit (LSQb) algorithm is employed to embed selected key quantum bits into quantum states. Each quantum bit generates a corresponding quantum state and is embedded into a larger quantum state structure through quantum gate operations. Quantum error correction codes and quantum entanglement properties are introduced to enhance the system's robustness and stability, reducing unnecessary quantum gate Key Distribution and Encryption: Quantum Key Distribution (QKD) technology is utilized to generate a shared key, ensuring the security of data transmission. The sender and receiver exchange correlated quantum states to generate the key, and any attempt to read the states will alter them and be detected, preventing information Transmission and Protection: The encrypted quantum state information is transmitted through a quantum channel, and even if eavesdropping occurs, attackers cannot obtain useful information. By combining protocols such as quantum teleportation, the system's security and flexibility are further Decryption and Recovery: The receiver uses the shared key to decrypt the quantum state information and applies inverse quantum gate operations to restore the original quantum bit sequence. Key feature information is extracted through a decoding algorithm and reassembled into a complete image, with error correction mechanisms introduced to ensure high-fidelity recovery. The entire process validates the effectiveness and accuracy of information hiding and transmission, establishing an efficient and secure quantum information processing integrates the Least Significant Quantum Bit (LSQb) algorithm for quantum images with other related theories, such as quantum information theory and cryptography, to further expand its application scope and functionality. Combined with quantum encryption technology, it constructs a more secure quantum information hiding and transmission system, ensuring the secure transmission of information in quantum networks. On one hand, it significantly reduces the demand for quantum resources, minimizing the involvement of unnecessary quantum bits and the number of quantum gate operations, thereby increasing the algorithm's execution speed. On the other hand, leveraging the unconditional security provided by quantum encryption technology ensures a high level of confidentiality for data during transmission. This not only enhances the efficiency of information processing but also greatly improves the system's resilience to interference, maintaining high information fidelity even in noisy environments. Additionally, by simplifying quantum circuit design, it reduces the cost and technical complexity of hardware implementation, making large-scale commercial applications practical applications, MicroAlgo's novel information hiding and transmission system has already been applied in multiple fields. For example, in medical image encryption, patient privacy data receives a higher level of protection; in financial transaction systems, customers' sensitive financial information is similarly safeguarded effectively. Through this approach, not only is information security enhanced, but processing efficiency is also improved, meeting the modern society's demand for high-speed and efficient data the future, with continuous advancements in quantum computing and quantum encryption technologies, MicroAlgo's novel information hiding and transmission system is expected to expand beyond its current application scenarios to more emerging fields, such as artificial intelligence and big data analysis. For instance, in the field of artificial intelligence, leveraging the advantages of quantum computing can accelerate the training process of machine learning models; in big data analysis, quantum image processing technology can help extract valuable information from massive datasets more quickly. Through ongoing exploration and practice, quantum image processing technology will become more mature and refined, contributing to the construction of a more secure and efficient information society. 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, 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. Contact MicroAlgo Inc. Investor Relations Email: ir@
Yahoo
06-06-2025
- Business
- Yahoo
MicroAlgo Inc. Adopts Quantum Phase Estimation (QPE) Method to Enhance Quantum Neural Network Training
SHENZHEN, China, June 6, 2025 /PRNewswire/ -- MicroAlgo Inc. (the "Company" or "MicroAlgo") (NASDAQ: MLGO), explored the possibilities of quantum technology in various application scenarios, particularly in the training of Quantum Neural Networks (QNNs). Quantum Neural Networks combine the advantages of quantum computing and machine learning, promising revolutionary breakthroughs in fields such as data processing and pattern recognition. Quantum Phase Estimation (QPE) is a key technique in quantum computing that leverages quantum superposition and interference principles to efficiently estimate the phase information of quantum states. In quantum neural network training, QPE is used to optimize network parameters. By precisely estimating the phase of quantum states, QPE can accelerate the convergence process of the network, improving training efficiency. This approach fully exploits the parallelism of quantum computing, enabling the processing of more information in the same amount of time, thereby significantly enhancing the training speed and accuracy of neural networks. Quantum Circuit Construction: A quantum circuit with multiple qubits is constructed, mapping the structure and functionality of the neural network to provide the foundation for the training process. The circuit design must be precise to ensure that qubits accurately represent the parameters of the neural network. Quantum State Initialization: A series of quantum gate operations are applied to initialize the qubits, placing them in specific quantum states. These quantum states correspond to the initial parameters of the neural network, serving as the starting point and foundation for the training process. Execution of Controlled Unitary Operations: Controlled unitary operations are applied to entangle the neural network's parameters with auxiliary qubits, accumulating phase information. By repeatedly applying controlled unitary operations with different powers, phase information is gradually accumulated onto the auxiliary qubits. Application of Inverse Quantum Fourier Transform: The inverse Quantum Fourier Transform is applied to the auxiliary qubits, converting the quantum state from the Fourier basis to the computational basis. Phase information is extracted and converted into classical bit values for subsequent parameter optimization. Parameter Optimization and Iteration: Based on the estimated phase information, the neural network's parameters are optimized to make the network output closer to the desired results. Through multiple iterations, parameters are continuously adjusted until the network achieves the expected training performance. Error Correction and Stability Enhancement: Advanced quantum error correction techniques are employed to reduce disturbances affecting qubits during operations. This improves the precision of phase estimation and the training stability of the neural network, ensuring the reliability of training results. Quantum phase estimation has brought revolutionary changes to various fields through its application in MicroAlgo's quantum neural network training. In image processing, quantum phase estimation enables quantum neural networks to classify and recognize images more efficiently, significantly outperforming traditional methods in both speed and accuracy. This technology makes the processing of large-scale image datasets faster and more precise, opening new possibilities for applications in image recognition and medical image analysis. In natural language processing, by optimizing network parameters, quantum neural networks can better understand and generate natural language text, demonstrating significant advantages in tasks such as machine translation, intelligent customer service, and text classification. The introduction of this technology not only enhances the efficiency of natural language processing but also improves its accuracy and fluency. The application of quantum phase estimation in MicroAlgo's quantum neural network training not only fully utilizes the parallelism of quantum computing, greatly accelerating the training process of neural networks, enabling more information to be processed in the same amount of time, and significantly improving training efficiency. At the same time, by precisely estimating the phase of quantum states, quantum phase estimation also optimizes the parameters of the neural network, enhancing the network's accuracy, making the neural network perform more outstandingly in various tasks. In addition, this technology has good scalability, capable of adapting to the continuous development of quantum computing technology and the increase in the number of qubits, providing strong support for larger-scale quantum neural network training. In the future, with the continuous advancement of quantum computing technology and the increasing number of qubits, the application of quantum phase estimation in quantum neural network training will become more extensive and in-depth. About 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 StatementsThis 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, 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. View original content: SOURCE
Yahoo
14-05-2025
- Business
- Yahoo
MicroAlgo Inc. Announces Research on Quantum Information Recursive Optimization (QIRO) Algorithm, for Combinatorial Optimization Problems to Expand and Solve New Ideas
Shenzhen, May 14, 2025 (GLOBE NEWSWIRE) -- MicroAlgo Inc. Announces Research on Quantum Information Recursive Optimization (QIRO) Algorithm, for Combinatorial Optimization Problems to Expand and Solve New Ideas Shenzhen, May. 14, 2025––MicroAlgo Inc. (the "Company" or "MicroAlgo") (NASDAQ: MLGO), today announced the research of the Quantum Information Recursive Optimization (QIRO) algorithm, which aims to provide a new approach to combinatorial optimization problems by leveraging the power of quantum computing. The Quantum Information Recursive Optimization (QIRO) algorithm is an optimization algorithm based on quantum computers, designed to tackle complex combinatorial optimization problems. This algorithm combines the concepts of quantum computing and recursive algorithms, utilizing the parallel computing capabilities of quantum computers along with the properties of quantum state superposition and interference to rapidly find optimal or near-optimal solutions within the search space. Recursive algorithms solve problems by repeatedly breaking them down into similar subproblems, while quantum computing exploits the characteristics of qubits and quantum states to achieve exponential acceleration. The QIRO algorithm integrates these two approaches by recursively invoking the quantum optimization process, progressively reducing the problem size until the optimal solution is Modeling: the first step involves modeling the combinatorial optimization problem by clearly defining the objective function, constraints, and candidate elements. This step forms the foundation of the algorithm and is a prerequisite for the subsequent State Initialization: in a quantum computer, quantum states are initialized through quantum gate operations. Due to the superposition property of quantum states, the quantum computer can process multiple computational paths simultaneously, thereby enabling parallel Invocation of the Quantum Optimization Process: the core of the QIRO algorithm lies in its recursive invocation of the quantum optimization process. In each recursion, the quantum state is evolved using quantum gate operations, leveraging quantum interference to search for the optimal solution within the search space. Depending on the problem's size and complexity, the depth and number of recursive calls are set to ensure that the algorithm can find an optimal solution within a reasonable time and Result Extraction: when the recursion reaches its boundary conditions, quantum measurement is performed to extract the optimal or near-optimal solution. The measurement collapses the quantum state into a definite state, from which the solution to the problem can be Verification and Optimization: the extracted solution is then verified and further optimized. By comparing the objective function values of different solutions, the optimal one is identified. Additionally, according to the actual needs of the problem, the solution can be further adjusted and refined to meet the problem's specific constraints and objective Quantum Information Recursive Optimization (QIRO) algorithm developed by MicroAlgo demonstrates significant technical advantages in solving combinatorial optimization problems. By fully leveraging the parallelism and interference principles of quantum computing, this algorithm achieves exponential improvements in computational efficiency, enabling it to handle large-scale and highly complex optimization problems in a short time. Compared to traditional algorithms, the QIRO algorithm possesses stronger global search capabilities, effectively avoiding local optima and instead identifying global or near-global optimal solutions. Moreover, the QIRO algorithm is highly flexible in design and can be tailored and optimized to meet the specific requirements of different problems, ensuring its effectiveness and accuracy across various application scenarios. At the same time, the algorithm exhibits a degree of robustness, allowing it to mitigate the impact of noise and errors on computational outcomes, thereby enhancing reliability and stability. These technical strengths position the QIRO algorithm as a powerful tool with broad application prospects and significant development potential in areas such as logistics and distribution, financial investment, artificial intelligence, and scientific terms of practical applications, the QIRO algorithm has already shown wide-ranging potential. It holds great significance for real-world scenarios requiring combinatorial optimization, such as resource allocation and network planning. For instance, in the field of logistics and transportation, tasks like planning optimal delivery routes and allocating cargo resources often involve complex combinatorial optimization. The QIRO algorithm can assist enterprises in identifying more efficient and cost-effective solutions. Additionally, in graph theory-related problems—such as finding large independent sets—the deployment of the QIRO algorithm on neutral atom quantum processors can enable efficient search operations. This supports efforts to study graph structures and analyze network characteristics, further proving the algorithm's practical value across different quantum computing platforms and its capacity to advance research in related academic ahead, MicroAlgo's Quantum Information Recursive Optimization (QIRO) algorithm holds immense growth potential. As quantum technology continues to progress, the quality and accessibility of quantum resources will steadily improve, providing greater support for the QIRO algorithm to tackle even more complex and large-scale combinatorial optimization problems. Furthermore, the QIRO algorithm may serve as a model for the development of additional hybrid quantum-classical algorithms, expanding the scope of quantum computing applications across various industries. This could offer new hope for solving more challenging real-world optimization problems, making QIRO a vital technological force in future scientific and technological development and a key driver of progress across multiple domains. 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, 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. Contact MicroAlgo Inc. Investor Relations Email: ir@ in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data
Yahoo
12-05-2025
- Business
- Yahoo
MicroAlgo Inc. Develops Quantum Convolutional Neural Network (QCNN) Architecture to Enhance the Performance of Traditional Computer Vision Tasks Using Quantum Mechanics Principles
SHENZHEN, China, May 12, 2025 /PRNewswire/ -- MicroAlgo Inc. (the "Company" or "MicroAlgo") (NASDAQ: MLGO), they announced today their research on quantum visual computing, exploring the integration of quantum computing with classical convolutional neural networks. They are developing a Quantum Convolutional Neural Network (QCNN) architecture to enhance the performance of traditional computer vision tasks using quantum mechanics principles. The Quantum Convolutional Neural Network (QCNN) architecture is an innovative computational model that cleverly combines the parallelism of quantum computing with the feature extraction capabilities of classical convolutional neural networks. In QCNN, quantum bits (qubits) serve as the basic carrier of information, utilizing the properties of quantum superposition and entanglement to achieve parallel processing of multiple computational tasks. At the same time, drawing inspiration from the structure of classical convolutional neural networks—such as convolution layers, pooling layers, and fully connected layers—QCNN extracts features, reduces dimensions, and classifies image data, thereby enhancing both computational speed and image recognition accuracy. Computer vision aims to enable computers to understand and analyze visual data, such as images or videos, much like the human visual system, involving tasks such as image recognition, object detection, and image segmentation. Quantum computing, with its unique quantum properties like superposition and entanglement, possesses powerful parallel computing capabilities and specialized methods of information processing. Data Preparation: Image or video data is collected from multiple channels, then screened and organized to remove low-quality or non-compliant data. The remaining data is preprocessed, including normalizing pixel values, resizing images, and correcting and enhancing colors to meet the specifications for subsequent processing. Quantum State Encoding: Following specific rules, the preprocessed image features are mapped onto quantum bits and converted into quantum states. By utilizing the properties of quantum superposition and entanglement, relationships between features are established, forming a complex network of feature associations. Quantum Convolutional Neural Network (QCNN) Processing: The quantum convolutional layer takes advantage of quantum parallelism, using multiple convolutional kernels to extract features represented by quantum states and uncover deeper features. The quantum pooling layer performs dimensionality reduction on the extracted features, retaining key features while alleviating the computational burden in subsequent stages. The quantum fully connected layer analyzes the reduced features and classifies them based on quantum state correlations. Quantum Measurement and Output: Through appropriate quantum measurement operations, the quantum state results are converted into classical data forms. Outputs such as target categories, locations, and other relevant information are provided, while the entire process is optimized based on application feedback. MicroAlgo's QCNN architecture has broad application prospects in the field of computer vision. In autonomous driving, QCNN can enable fast and accurate recognition of key elements such as road signs, vehicles, and pedestrians, enhancing the safety and reliability of autonomous driving systems. In medical imaging analysis, QCNN can achieve rapid and accurate diagnosis of medical images, assisting doctors in disease diagnosis and treatment planning. In security surveillance, QCNN can enable real-time detection and early warning of abnormal behavior in surveillance videos, improving the efficiency and accuracy of security measures. Additionally, QCNN can be widely applied in various fields such as smart manufacturing, aerospace, and smart cities, driving technological upgrades and intelligent transformations in related industries. 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, 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. View original content: SOURCE Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data
