shenzhen, May 20, 2025 (GLOBE NEWSWIRE) -- Shenzhen, May. 20, 2025/––MicroAlgo Inc. (the "Company" or "MicroAlgo") (
NASDAQ: MLGO), announced that quantum algorithms will be deeply integrated with machine learning to explore practical application scenarios for quantum acceleration.Quantum machine learning algorithms represent an innovative approach that applies the principles of quantum computing to the field of machine learning. By leveraging the unique properties of quantum bits, such as superposition and entanglement, these algorithms enable parallel data processing and efficient computation. Compared to classical algorithms, quantum machine learning demonstrates significant advantages in feature extraction, model training, and predictive inference. It is particularly well-suited for handling high-dimensional data, optimizing combinatorial problems, and solving large-scale linear equations. Quantum machine learning algorithms can process more complex datasets in a shorter time, enhancing both the speed of model training and the accuracy of predictions.MicroAlgo's development of quantum machine learning technology follows a closed-loop process of "problem modeling - quantum circuit design - experimental validation - optimization iteration." For specific machine learning tasks (such as classification, regression, or clustering), the team preprocesses classical data into quantum state inputs, mapping feature vectors into a quantum system using techniques like amplitude encoding or density matrix encoding. Quantum circuits are designed based on task requirements, for instance, by employing variational quantum algorithms (VQA) to construct trainable parameterized quantum gate sequences, with a classical optimizer adjusting the quantum circuit parameters to minimize the target function. During the quantum computing execution phase, the circuits are run on a quantum computer or cloud platform, and quantum measurement results are obtained and converted into classical data outputs.Validate model performance through classical post-processing, analyze error sources, and reverse optimize quantum circuit structure and parameters.Quantum Feature Mapping: Embedding classical data into a quantum state space, enhancing data distinguishability through techniques such as quantum Fourier transform or amplitude amplification.Quantum Circuit Optimization: Employing adaptive variational algorithms to dynamically adjust circuit depth, balancing computational resources with model expressiveness.Hybrid Quantum-Classical Architecture: Combining the parallel advantages of quantum computing with the flexibility of classical computing to achieve efficient collaborative training.Noise Suppression Techniques: Addressing the noise issues in current quantum hardware by introducing quantum error correction codes and error mitigation strategies to improve computational accuracy.MicroAlgo's quantum machine learning algorithms leverage the parallelism and efficiency of quantum computing to accelerate the execution of machine learning tasks, enabling the processing of more complex datasets in shorter timeframes while improving model training speed and prediction accuracy. These quantum machine learning algorithms can handle high-dimensional data and complex patterns that traditional machine learning algorithms struggle to address. The unique properties of quantum bits, such as superposition and entanglement, allow quantum machine learning algorithms to efficiently represent and process data in high-dimensional spaces, uncovering complex patterns that conventional algorithms cannot capture. Additionally, MicroAlgo's quantum machine learning algorithms offer strong scalability and flexibility, making them adaptable to datasets of varying sizes and types as well as diverse machine learning task requirements.The quantum machine learning algorithms researched by MicroAlgo hold broad application prospects across multiple domains. In the financial sector, these algorithms can be used for predicting and analyzing financial time-series data, enhancing the accuracy and efficiency of trading decisions. In the medical field, quantum machine learning algorithms can support the development and implementation of personalized healthcare plans by analyzing patients’ genetic information and clinical data, accurately predicting treatment outcomes and providing tailored medical solutions. In the logistics sector, these algorithms can be applied to supply chain management and logistics optimization tasks, offering analytical and decision-making support to help businesses improve operational efficiency and reduce costs. Furthermore, quantum machine learning algorithms can also be utilized in areas such as cybersecurity, smart manufacturing, and energy management, delivering efficient data analysis and optimization solutions for these fields.As quantum computing technology continues to advance and research into quantum machine learning algorithms deepens, quantum algorithms are poised to address challenges that classical computers cannot solve, bringing disruptive innovations to various industries in the future.
