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 eff ...

Core Insights - MicroAlgo Inc. is exploring the potential of quantum technology, particularly in training Quantum Neural Networks (QNNs), which could lead to significant advancements in data processing and pattern recognition [1] Quantum Neural Network Training - Quantum Phase Estimation (QPE) is a crucial technique in quantum computing that enhances the training efficiency of neural networks by optimizing network parameters through precise phase estimation [2][10] - The construction of quantum circuits is essential for mapping the neural network's structure, ensuring accurate representation of parameters [3] - Quantum state initialization involves applying quantum gate operations to set qubits in specific states that correspond to the neural network's initial parameters [4] - Controlled unitary operations are utilized to entangle the neural network's parameters with auxiliary qubits, gradually accumulating phase information [5] - The inverse Quantum Fourier Transform is applied to convert quantum states into classical bit values for parameter optimization [6] Parameter Optimization and Error Correction - Parameter optimization involves adjusting the neural network's parameters based on estimated phase information to improve output accuracy through iterative processes [7] - Advanced quantum error correction techniques are implemented to enhance training stability and precision of phase estimation [8] Applications and Future Prospects - The application of QPE in QNN training has shown to significantly improve image processing capabilities, outperforming traditional methods in speed and accuracy [9] - In natural language processing, optimized network parameters allow for better understanding and generation of text, enhancing efficiency and fluency in various tasks [9] - The scalability of this technology supports the ongoing development of quantum computing and the increasing number of qubits, indicating a promising future for larger-scale QNN training [10][11][12] Company Overview - MicroAlgo Inc. specializes in developing bespoke central processing algorithms, providing comprehensive solutions that integrate these algorithms with software and hardware to enhance customer satisfaction and achieve technical goals [13]

Core Viewpoint - MicroAlgo Inc. is focused on enhancing the accuracy and reliability of quantum algorithms by optimizing quantum error correction algorithms, which are essential for detecting and correcting errors in qubits during quantum computation [1][2]. Group 1: Quantum Error Correction Algorithms - The company introduces redundant qubits and specific measurement operations to detect and correct errors in qubits, ensuring the accuracy of quantum computation [2][7]. - The first step in quantum error correction involves encoding quantum information using redundant qubits to form a quantum codeword, which helps retain data despite noise [3]. - Error detection is performed through specific measurement operations on auxiliary qubits, allowing for the identification of errors in the quantum codeword [4]. Group 2: Error Correction Process - Once errors are detected, a series of complex quantum operations are executed to restore erroneous qubits to their correct states, emphasizing the need for efficient error correction algorithms [5]. - The algorithm undergoes iterative optimization, continuously repeating encoding, detection, and correction processes to reduce error rates and improve algorithm accuracy [6]. Group 3: Applications and Prospects - MicroAlgo's quantum error correction algorithm has broad application prospects in quantum communication, enhancing security and anti-interference capabilities, particularly for quantum key distribution [8]. - In quantum computing, the algorithm reduces qubit error rates, improving the reliability of quantum algorithms and supporting their practical applications [8]. - The algorithm can also be applied in quantum simulation and optimization, providing new tools for research and development in these areas [8]. Group 4: Company Overview - MicroAlgo Inc. is dedicated to developing bespoke central processing algorithms, offering solutions that integrate these algorithms with software or hardware to enhance customer satisfaction and achieve technical goals [9][10].

Core Viewpoint - MicroAlgo Inc. is integrating quantum algorithms with machine learning to explore practical applications for quantum acceleration [1] Group 1: Quantum Machine Learning Technology - Quantum machine learning algorithms utilize quantum computing principles to enhance machine learning, offering advantages in feature extraction, model training, and predictive inference [2] - These algorithms excel in processing high-dimensional data, optimizing combinatorial problems, and solving large-scale linear equations, resulting in faster model training and improved prediction accuracy [2][6] - MicroAlgo employs a closed-loop process for developing quantum machine learning technology, which includes problem modeling, quantum circuit design, experimental validation, and optimization iteration [3] Group 2: Technical Aspects - Quantum feature mapping techniques enhance data distinguishability, while quantum circuit optimization employs adaptive variational algorithms to balance computational resources and model expressiveness [4] - The hybrid quantum-classical architecture combines the strengths of both computing paradigms for efficient collaborative training [5] - Noise suppression techniques are introduced to address current quantum hardware limitations, improving computational accuracy [5] Group 3: Applications and Prospects - Quantum machine learning algorithms have broad application prospects in various sectors, including finance for analyzing time-series data, healthcare for personalized treatment plans, and logistics for supply chain optimization [7] - These algorithms can also be applied in cybersecurity, smart manufacturing, and energy management, providing efficient data analysis and optimization solutions [7] - As quantum computing technology advances, it is expected to address challenges that classical computers cannot, leading to disruptive innovations across industries [8] Group 4: Company Overview - MicroAlgo Inc. is focused on developing bespoke central processing algorithms and offers comprehensive solutions by integrating these algorithms with software and hardware [9][10] - The company aims to enhance customer satisfaction, achieve cost savings, and reduce power consumption through its services, which include algorithm optimization and data intelligence [10]

Core Viewpoint - MicroAlgo Inc. has developed a novel quantum entanglement-based training algorithm for supervised quantum classifiers, which significantly enhances training efficiency and classification accuracy compared to traditional algorithms [1][12]. Group 1: Algorithm Development - The new algorithm, named the Entanglement-Assisted Training Algorithm, utilizes quantum entanglement to process multiple training samples simultaneously, improving training efficiency [2][9]. - It represents training samples as qubit vectors and encodes label information into quantum states, allowing for parallel processing of data [3][5]. Group 2: Cost Function and Optimization - A cost function based on Bell inequalities is introduced, enabling the simultaneous encoding of errors from multiple samples, which enhances classification accuracy and overcomes local optimization issues common in traditional methods [4][10]. - This cost function allows the optimization process to focus on the collective performance of multiple samples rather than individual sample errors [8][10]. Group 3: Implementation and Components - The algorithm relies on core components of quantum computing technology, including qubits, quantum gate operations, and quantum measurement, to efficiently process input data [5][6]. - Quantum entanglement is utilized to arrange training samples into an entangled state, improving data processing efficiency and accelerating convergence during training [7][9]. Group 4: Future Implications - The advancements in quantum machine learning, particularly through MicroAlgo's algorithm, are expected to revolutionize the field, potentially allowing quantum classifiers to excel in complex classification tasks beyond traditional binary classifications [12]. - Continuous progress in quantum computing technology is anticipated to address existing challenges, paving the way for broader applications of quantum classifiers [11][12].

Core Viewpoint - MicroAlgo Inc. has announced the development of the Quantum Information Recursive Optimization (QIRO) algorithm, which aims to enhance combinatorial optimization problems by utilizing quantum computing capabilities [1][7]. Group 1: Algorithm Overview - The QIRO algorithm is designed to tackle complex combinatorial optimization problems by integrating quantum computing and recursive algorithms, leveraging parallel computing and quantum state properties [1][7]. - The algorithm recursively invokes quantum optimization processes, progressively reducing problem size to find optimal solutions [4][7]. Group 2: Technical Process - The first step involves modeling the combinatorial optimization problem by defining the objective function, constraints, and candidate elements [2]. - Quantum states are initialized through quantum gate operations, allowing for simultaneous processing of multiple computational paths [3]. - Quantum measurement is performed at the recursion's boundary conditions to extract optimal or near-optimal solutions [5]. - The extracted solution is verified and optimized by comparing objective function values to identify the best solution [6]. Group 3: Advantages and Applications - The QIRO algorithm demonstrates significant technical advantages, achieving exponential improvements in computational efficiency and stronger global search capabilities compared to traditional algorithms [7]. - It is flexible and can be tailored to meet specific problem requirements, enhancing its effectiveness across various applications [7]. - The algorithm has practical applications in logistics, resource allocation, network planning, and graph theory-related problems, proving its value in real-world scenarios [8]. Group 4: Future Potential - The QIRO algorithm holds immense growth potential as quantum technology advances, improving the quality and accessibility of quantum resources [9][10]. - It may serve as a model for developing additional hybrid quantum-classical algorithms, expanding quantum computing applications across various industries [10]. Group 5: Company Background - MicroAlgo Inc. is dedicated to developing and applying bespoke central processing algorithms, providing comprehensive solutions that enhance customer satisfaction and achieve technical goals [11].

Core Insights - MicroAlgo Inc. is developing a Quantum Convolutional Neural Network (QCNN) architecture that integrates quantum computing with classical convolutional neural networks to enhance computer vision tasks [1][2] Group 1: Quantum Convolutional Neural Network (QCNN) Overview - QCNN combines the parallelism of quantum computing with the feature extraction capabilities of classical convolutional neural networks, utilizing quantum bits (qubits) for information processing [2] - The architecture includes convolution layers, pooling layers, and fully connected layers, which improve computational speed and image recognition accuracy [2][3] Group 2: Data Processing Steps - Data preparation involves collecting, screening, and preprocessing image or video data to ensure quality and compliance [4] - Quantum state encoding maps preprocessed image features onto quantum bits, establishing complex feature associations through quantum properties [5] Group 3: QCNN Processing Mechanism - The quantum convolutional layer uses quantum parallelism to extract features, while the quantum pooling layer reduces dimensions to retain key features [6] - The quantum fully connected layer analyzes reduced features and classifies them based on quantum state correlations [6] Group 4: Applications of QCNN - QCNN has potential applications in autonomous driving for recognizing road signs, vehicles, and pedestrians, thereby enhancing safety [8] - In medical imaging, QCNN can facilitate rapid and accurate diagnoses, assisting in disease treatment planning [8] - The architecture can also improve security surveillance by enabling real-time detection of abnormal behavior [8] - Additional applications include smart manufacturing, aerospace, and smart cities, driving technological upgrades in these sectors [8] Group 5: Company Overview - MicroAlgo Inc. focuses on developing bespoke central processing algorithms and provides comprehensive solutions that integrate these algorithms with software and hardware [9] - The company aims to enhance customer satisfaction, reduce costs, and achieve technical goals through algorithm optimization and efficient data processing [9][10]

Core Viewpoint - MicroAlgo Inc. has developed an innovative quantum image encryption algorithm that utilizes quantum key images for efficient image protection, leveraging quantum mechanics principles to enhance security and performance [1][7]. Group 1: Quantum Image Encryption Algorithm - The algorithm employs a quantum key image to store encryption keys, utilizing quantum entanglement and parallelism for effective image encryption [1]. - A quantum key map is prepared using the GQIR method, which is crucial for storing the encryption key securely [2]. - The encryption process involves generating random key sequences through a quantum random number generator (QRNG), ensuring high-security keys [3]. Group 2: Preprocessing and Encryption Process - Prior to encryption, the plaintext image undergoes preprocessing to optimize its state for effective encryption, including format conversion and pixel value adjustments [4]. - The core encryption operation uses a bitwise XOR between the plaintext image and the quantum key map, resulting in a highly random and secure encrypted image [5]. Group 3: Storage and Transmission Security - After encryption, the secure storage and transmission of the encrypted image are essential to prevent unauthorized access, employing advanced Quantum Key Distribution (QKD) technology for secure transmission [6]. Group 4: Applications and Market Potential - The algorithm has significant potential in various sectors, including financial transactions, government institutions, and the medical field, providing robust encryption to protect sensitive information [8][9]. - With the rise of the Internet of Things (IoT), the algorithm offers secure communication channels to protect smart devices from hacking, enhancing the overall security of the digital ecosystem [9]. Group 5: Future Outlook - As quantum computing and encryption technologies evolve, the security and efficiency of MicroAlgo's algorithm are expected to improve, positioning it for widespread adoption across diverse fields [10].

Core Viewpoint - MicroAlgo Inc. has developed a blockchain storage optimization solution utilizing the Archimedes Optimization Algorithm (AOA) to enhance efficiency in blockchain storage systems, addressing key challenges in large-scale applications [1][10]. Company Overview - MicroAlgo Inc. is a Cayman Islands exempted company focused on developing bespoke central processing algorithms, providing solutions that integrate these algorithms with software and hardware to improve customer satisfaction and reduce costs [13]. Technology and Algorithm - The Archimedes Optimization Algorithm (AOA) is a metaheuristic algorithm inspired by Archimedean buoyancy principles, designed to optimize data storage and node collaboration in blockchain environments [2][10]. - AOA constructs a multi-objective optimization model targeting data sharding, resource allocation, and consensus efficiency, allowing for dynamic adjustments in storage systems [2][10]. Technical Workflow - The optimization solution encompasses five key stages: data preprocessing, sharding strategy optimization, node resource allocation, consensus mechanism enhancement, and security strategy tuning [3]. Data Preprocessing - Multi-dimensional feature extraction is performed on blockchain data, applying differentiated strategies based on data characteristics, such as lightweight serialization for structured data and homomorphic encryption for privacy-sensitive data [4]. Sharding Strategy Optimization - AOA models data sharding as an optimal partitioning task, dynamically adjusting storage node allocation based on data access frequency, ensuring efficient storage and access performance [5]. Node Load Balancing - AOA implements a real-time load monitoring model to balance node loads, guiding new data to underloaded nodes and migrating low-frequency data from overloaded nodes based on network conditions [6]. Consensus Efficiency - AOA enhances consensus mechanisms by optimizing block generation and validation, dynamically adjusting transaction sorting and prioritizing high-trust nodes to reduce malicious interference [7][8]. Security Strategy Optimization - AOA builds an encryption parameter optimization model to enhance data security, monitoring anomalies in on-chain data and facilitating rapid recovery from node failures [9]. Competitive Advantage - Compared to traditional storage strategies, MicroAlgo's AOA-based solution significantly improves efficiency, outperforming Genetic Algorithms by 40% and reducing iterations needed by 25% compared to Particle Swarm Optimization [10]. Future Directions - MicroAlgo plans to evolve AOA technology by introducing quantum computing acceleration, exploring algorithm-hardware co-design, and promoting integration with cross-chain protocols to enhance blockchain storage capabilities [12].

Core Viewpoint - MicroAlgo Inc. has launched a new classifier auto-optimization technology based on Variational Quantum Algorithms (VQA), which significantly enhances computational efficiency and reduces complexity in parameter updates during training [1][11]. Group 1: Technology Overview - The new technology improves upon traditional quantum classifiers by reducing the complexity of parameter updates through deep optimization of the core circuit, leading to enhanced computational efficiency [1][3]. - MicroAlgo's approach includes a streamlined quantum circuit structure that minimizes the number of quantum gates, thereby lowering computational resource consumption [3][6]. - The classifier auto-optimization model employs an innovative parameter update strategy that accelerates training speed and improves efficiency [3][11]. Group 2: Challenges in Traditional Quantum Classifiers - Traditional quantum classifiers face challenges such as high optimization complexity due to deep quantum circuits, which complicates parameter updates and prolongs training times [2][4]. - The increasing volume of training data exacerbates the computational load for parameter updates, impacting the practicality of these models [2][5]. Group 3: Key Innovations - MicroAlgo's technology features Depth Optimization of Quantum Circuits, which uses Adaptive Circuit Pruning (ACP) to dynamically adjust circuit structures, reducing the number of parameters and computational complexity [6][7]. - The introduction of Hamiltonian Transformation Optimization (HTO) shortens the search path within the parameter space, improving optimization efficiency and reducing computational complexity by at least an order of magnitude [7][11]. - A novel regularization strategy, Quantum Entanglement Regularization (QER), dynamically adjusts quantum entanglement strength during training to prevent overfitting and enhance generalization capability [9][10]. Group 4: Noise Robustness - To address the challenges posed by Noisy Intermediate-Scale Quantum (NISQ) devices, MicroAlgo proposes Variational Quantum Error Correction (VQEC) to improve the classifier's robustness against noise, enhancing stability in real quantum environments [10][11]. Group 5: Future Implications - As quantum computing hardware advances, MicroAlgo's technology is expected to expand its application domains, facilitating the practical implementation of quantum intelligent computing and marking a significant milestone in the convergence of quantum computing and artificial intelligence [12].