MicroAlgo Inc. Researches Quantum Machine Learning Algorithms to Accelerate Machine Learning Tasks

Rhea-AI Summary

MicroAlgo Inc. (NASDAQ: MLGO) announced its research into integrating quantum algorithms with machine learning for quantum acceleration applications. The company is developing quantum machine learning technology through a closed-loop process involving problem modeling, quantum circuit design, experimental validation, and optimization iteration.

The technology leverages quantum bits' properties like superposition and entanglement for parallel data processing, focusing on key areas including quantum feature mapping, circuit optimization, hybrid quantum-classical architecture, and noise suppression techniques. MicroAlgo's approach aims to process complex datasets faster while improving model training speed and prediction accuracy.

The company identifies potential applications across multiple sectors including financial services (time-series analysis), healthcare (personalized treatment plans), logistics (supply chain optimization), cybersecurity, smart manufacturing, and energy management.

Positive

• Development of advanced quantum machine learning algorithms that can handle high-dimensional data more efficiently
• Potential applications across multiple high-value sectors (finance, healthcare, logistics)
• Implementation of hybrid quantum-classical architecture for efficient collaborative training
• Integration of noise suppression techniques and error correction to improve computational accuracy

Negative

• Research is still in early stages with no concrete implementation timeline
• Technology depends on advancement of quantum computing hardware
• No specific financial metrics or business impact details provided
• Faces technical challenges related to quantum hardware noise and error rates

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.

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.

Contact

MicroAlgo Inc.

Investor Relations

Email: ir@microalgor.com

FAQ

What is MicroAlgo's (MLGO) approach to quantum machine learning?

MicroAlgo uses a closed-loop process including problem modeling, quantum circuit design, experimental validation, and optimization iteration, leveraging quantum bits' properties for parallel data processing and efficient computation.

What are the key features of MLGO's quantum machine learning technology?

The key features include quantum feature mapping, quantum circuit optimization, hybrid quantum-classical architecture, and noise suppression techniques with error correction codes.

Which industries could benefit from MicroAlgo's quantum machine learning algorithms?

The technology can benefit financial services (time-series analysis), healthcare (personalized treatment), logistics (supply chain optimization), cybersecurity, smart manufacturing, and energy management sectors.

How does MicroAlgo (MLGO) address quantum computing noise issues?

MicroAlgo implements noise suppression techniques including quantum error correction codes and error mitigation strategies to improve computational accuracy.

What advantages does MLGO's quantum machine learning offer over classical algorithms?

The technology offers faster processing of complex datasets, improved model training speed, better prediction accuracy, and superior handling of high-dimensional data through quantum properties like superposition and entanglement.