MicroAlgo, Inc. - Ordinary Shares (MLGO)
1.2700
-0.0700 (-5.22%)
NASDAQ · Last Trade: May 20th, 4:56 PM EDT
Detailed Quote
| Previous Close | 1.340 |
|---|---|
| Open | 1.570 |
| Bid | 1.280 |
| Ask | 1.290 |
| Day's Range | 1.170 - 1.670 |
| 52 Week Range | 1.110 - 509.60 |
| Volume | 67,710,896 |
| Market Cap | - |
| PE Ratio (TTM) | - |
| EPS (TTM) | - |
| Dividend & Yield | N/A (N/A) |
| 1 Month Average Volume | 22,709,055 |
Chart
About MicroAlgo, Inc. - Ordinary Shares (MLGO)
MicroAlgo, Inc. is a technology company that specializes in developing advanced software solutions and algorithms aimed at enhancing operational efficiencies for businesses. The company focuses on leveraging artificial intelligence and machine learning technologies to create innovative tools that streamline processes, analyze data, and optimize decision-making for various industries. By providing sophisticated analytics and automation capabilities, MicroAlgo empowers organizations to harness the power of their data, improve productivity, and drive growth in an increasingly competitive landscape. Read More
News & Press Releases
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.
By MicroAlgo.Inc · Via GlobeNewswire · May 20, 2025
shenzhen, May 16, 2025 (GLOBE NEWSWIRE) -- Shenzhen, May. 16, 2025––MicroAlgo Inc. (the "Company" or "MicroAlgo") (NASDAQ: MLGO), today announced the development of a novel quantum entanglement-based training algorithm — the Entanglement-Assisted Training Algorithm for Supervised Quantum Classifiers. They also introduced a cost function based on Bell inequalities, enabling the simultaneous encoding of errors from multiple training samples. This breakthrough surpasses the capability limits of traditional algorithms, offering an efficient and widely applicable solution for supervised quantum classifiers.The core of MicroAlgo's entanglement-assisted training algorithm for supervised quantum classifiers lies in leveraging quantum entanglement to construct a model capable of simultaneously operating on multiple training samples and their corresponding labels. Unlike traditional machine learning methods, quantum classifiers can not only process information from individual samples but also perform parallel processing of multiple samples in quantum states, thereby significantly enhancing training efficiency.The algorithm represents multiple training samples as qubit vectors using quantum superposition, and encodes their label information into quantum states through quantum gate operations. Due to the entangled relationships between qubits, the classifier can simultaneously operate on multiple samples at once. This characteristic breaks away from the conventional sample-by-sample processing paradigm, greatly improving both training speed and classification performance.Furthermore, the algorithm introduces a cost function based on Bell inequalities—an important theorem in quantum mechanics that highlights the distinction between quantum entanglement and classical information processing. By encoding classification errors of multiple samples simultaneously into the cost function, the optimization process is no longer limited to individual sample errors but instead considers the collective performance of multiple samples. This approach overcomes the local optimization issues common in traditional algorithms and significantly enhances classification accuracy.The implementation of MicroAlgo's entanglement-assisted training algorithm for supervised quantum classifiers relies on several core components of current quantum computing technology: qubits, quantum gate operations, and quantum measurement. With these fundamental building blocks, the algorithm can efficiently process input data on a quantum computer.Representation and Initialization of Qubits: at the initial stage of the algorithm, the input training samples are transformed into qubits. Each training sample corresponds to one or more qubits, which are initialized into specific quantum states. To enable entanglement, entangling operations are performed between multiple qubits so that they can collaboratively process sample data in the subsequent steps.Construction of Quantum Entanglement: quantum entanglement is one of the core features of quantum computing. In this algorithm, training samples are arranged into an entangled state, meaning that information between samples is shared and processed through entanglement. This not only improves data processing efficiency but also accelerates convergence during the training process.Application of Bell Inequalities and Cost Function Optimization: a key application of quantum entanglement is in the use of Bell inequalities. In the algorithm, Bell inequalities are employed to construct the cost function, with the objective of minimizing classification errors. Unlike traditional methods, this cost function simultaneously accounts for errors from multiple samples, allowing the optimization process to focus on the collective performance of all samples rather than optimizing on a per-sample basis. Through rapid quantum algorithmic computation, the cost function can be efficiently minimized to achieve optimal classification results.Interpretation and Output of Classification Results: finally, the algorithm outputs the classification results through quantum measurement. In binary classification tasks, the input training samples are divided into two categories, while in multi-class tasks, they are assigned to multiple classes. The advantage of quantum computing lies in its parallel processing capability, enabling the system to complete complex classification tasks in a significantly shorter amount of time.The greatest advantage of this technology lies in its ability to leverage the unique properties of quantum entanglement to parallelize the training process across multiple training samples. This not only accelerates the training speed but also effectively enhances classification accuracy. Especially in problems involving large datasets, traditional methods often face computational bottlenecks, whereas quantum computing can easily overcome these limitations.In addition, the cost function based on Bell's inequality is theoretically more robust than traditional error minimization methods. It can simultaneously handle the errors of multiple training samples, thereby avoiding the local optimum problems that may occur in conventional approaches. This makes the supervised quantum classifier particularly effective in complex classification tasks.However, quantum computing still faces many challenges. For instance, the stability and computational scale of quantum computers remain limiting factors. The number of qubits and their error rates can both impact the practical performance of the algorithms. Therefore, how to implement efficient algorithms on existing quantum computing platforms remains a technical hurdle that needs further breakthroughs.With the continuous advancement of quantum computing technology, quantum machine learning is bound to become a key direction for future technological innovation. The entanglement-assisted training algorithm of the MicroAlgo supervised quantum classifier opens up new possibilities in this field. By integrating quantum entanglement with traditional classification algorithms, this technology demonstrates great potential in improving training efficiency and enhancing classification accuracy. Although quantum computing still faces numerous challenges, with ongoing progress in hardware and deepening theoretical research, we have every reason to believe that quantum computing will bring about a revolution in the field of machine learning. In the future, quantum classifiers may not be limited to traditional binary classification tasks—they could potentially exhibit unparalleled advantages in even more complex domains.
By MicroAlgo.Inc · Via GlobeNewswire · May 16, 2025
Keep an eye on the top gainers and losers in Monday's session, as they reflect the most notable price movements.
Via Chartmill · May 19, 2025
Via Benzinga · May 19, 2025
Wondering how the US markets performed in the middle of the day on Monday? Discover the movers and shakers of today's session in our comprehensive analysis.
Via Chartmill · May 19, 2025
Via Benzinga · May 16, 2025
Via Benzinga · May 15, 2025
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
By MicroAlgo.Inc · Via GlobeNewswire · May 14, 2025
Via Benzinga · May 13, 2025
Via Benzinga · May 13, 2025
Via Benzinga · May 13, 2025
shenzhen, May 13, 2025 (GLOBE NEWSWIRE) -- MicroAlgo Inc. Develops Blockchain-Based Traceable IP Rights Protection Algorithm
By MicroAlgo.Inc · Via GlobeNewswire · May 13, 2025
Curious to know what's happening on the US markets one hour before the close of the markets on Monday? Join us as we explore the top gainers and losers in today's session.
Via Chartmill · May 12, 2025
Via Benzinga · May 12, 2025
SHENZHEN, May 08, 2025 (GLOBE NEWSWIRE) -- MicroAlgo Inc. Develops a Blockchain Storage Optimization Solution Based on the Archimedes Optimization Algorithm (AOA)
By MicroAlgo.Inc · Via GlobeNewswire · May 8, 2025
Curious about what's happening in today's session? Check out the latest stock movements and price changes.
Via Chartmill · May 7, 2025
Via Benzinga · May 7, 2025
Via Benzinga · May 7, 2025
Via Benzinga · May 5, 2025
Via Benzinga · May 2, 2025
The company said the return to profitability was largely due to the strategic shift away from its intelligent chips and services segment, and dedication of resources to its central processing algorithm services.
Via Stocktwits · April 29, 2025
Today's session on Monday is marked by notable gaps in various stocks. Stay informed with the gap up and gap down stocks in today's session.
Via Chartmill · April 28, 2025
MicroAlgo shares are trading higher by 19% during Monday's session. The company earlier reported a return to profitability for 2024.
Via Benzinga · April 28, 2025
Shenzhen, April 28, 2025 (GLOBE NEWSWIRE) -- Shenzhen, China, April 28, 2025 – MicroAlgo Inc. (NASDAQ: MLGO), (the “Company”), a leading developer and application provider of bespoke central processing algorithms, today announced its financial results for the year ended December 31, 2024. The Company reported total revenues of RMB 541.5 million (USD 75.3 million) and net income of RMB 53.4 million (USD 7.3 million), marking a significant turnaround from the previous year's net loss of RMB 266.2 million and net loss of RMB 46.54 million in 2022. This return to profitability is largely attributed to the company's strategic shift away from its intelligent chips and services segment, and dedication of resources resulting in strong performance in its central processing algorithm services, which accounted for 100% of revenues in 2024.
By MicroAlgo.Inc · Via GlobeNewswire · April 28, 2025