MicroAlgo Inc. Develops Optimal Precise Quantum Query Algorithm Based on Sum-of-Squares Representation Form of Boolean Functions
Rhea-AI Summary
MicroAlgo (NASDAQ: MLGO) on April 30, 2026 announced a new technical framework for designing optimal exact quantum query algorithms built from the sum-of-squares representation form of Boolean functions. The three-step approach covers SOS representation, construction of final quantum states, and selection of unitary operators.
The company says the method offers theoretical advances and potential practical applications in quantum decision trees, quantum communication, security, and quantum machine learning, while noting feasibility limits for some practical cases.
AI-generated analysis. Not financial advice.
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News Market Reaction – MLGO
On the day this news was published, MLGO gained 5.66%, reflecting a notable positive market reaction. Argus tracked a peak move of +2.4% during that session. Our momentum scanner triggered 5 alerts that day, indicating moderate trading interest and price volatility. This price movement added approximately $3M to the company's valuation, bringing the market cap to $52.73M at that time.
Data tracked by StockTitan Argus on the day of publication.
Key Figures
Market Reality Check
Peers on Argus
MLGO was down 2.75% while peers were mixed: LIDR down 2.84%, XBP up 6.41%, VHC up 1.79%, ALAR slightly up 0.15%, KPLT flat. Momentum scanner only flagged ZENA up 12.12%, indicating today’s setup looked stock-specific rather than a broad sector move.
Historical Context
| Date | Event | Sentiment | Move | Catalyst |
|---|---|---|---|---|
| Apr 24 | Quantum AI update | Positive | +4.4% | Announced quantum algorithms for neural networks using QRAM and inner-product routines. |
| Apr 01 | Full-year earnings | Positive | +14.9% | Reported strong 2025 results with sharply higher net profit and EPS. |
Recent positive technology and earnings announcements have both seen positive next-day price reactions, suggesting the stock has previously rewarded upbeat news.
Over the last few months, MicroAlgo highlighted both financial and technical progress. On Apr 1, 2026, it reported strong 2025 results, with net profit and EPS rising sharply and the stock up 14.94% the next day. On Apr 24, 2026, it announced new quantum algorithms for neural networks, followed by a 4.42% gain. Today’s quantum query framework fits this ongoing narrative of quantum-computing innovation built on recent profitability gains.
Market Pulse Summary
The stock moved +5.7% in the session following this news. A strong positive reaction aligns with MicroAlgo’s recent pattern, where upbeat technology and earnings updates saw gains of 4.42% and 14.94%. The new Boolean-function quantum query framework extends its quantum-computing story, but investors must balance enthusiasm against prior capital-structure risks such as the $35 million deeply discounted convertible note. Past behavior suggests news-driven spikes could be sensitive to perceptions of dilution and execution progress.
Key Terms
boolean functions technical
sum-of-squares representation technical
multilinear polynomials technical
quantum query algorithms technical
quantum state technical
unitary operator technical
cnot gates technical
quantum decision tree algorithms technical
AI-generated analysis. Not financial advice.
In quantum computing, the query complexity of Boolean functions directly affects the performance of quantum algorithms. Traditional classical algorithms face limitations in time and space when processing Boolean functions, whereas quantum computing, by leveraging the characteristics of superposition and entanglement, has the potential to significantly improve query efficiency. However, the challenge of designing optimal exact quantum query algorithms for arbitrarily small-input Boolean functions still remains, and there is a lack of general methods.
Boolean functions can be represented as a sum of squares of multilinear polynomials, and this property provides an important mathematical foundation for designing quantum algorithms. By performing sum-of-squares representations of Boolean functions and their negations, it is possible to reveal their internal structure, thereby enabling the construction of corresponding quantum query algorithms.
MicroAlgo's technical framework consists of three fundamental steps:
Step One: Finding the Sum-of-Squares Representations of the Boolean Function and Its Negative Function
First, it is necessary to analyze the target Boolean function and find its sum-of-squares representation. The key to this step lies in identifying the structure of the Boolean function and using the properties of multilinear polynomials to express it in the form of a sum of squares. Through this representation, the characteristics of the Boolean function can be obtained, which facilitates the subsequent construction of the algorithm.
In practical operation, algebraic tools and computer algebra systems can be effectively used to achieve this goal. Various algorithms (such as the Lagrange interpolation method) can be used to derive the sum-of-squares representations of the Boolean function and its negation.
Step Two: Constructing the Final State of the Optimal Exact Quantum Query Algorithm
After obtaining the sum-of-squares representation of the Boolean function, the next step is to construct a quantum state. The goal of this process is to determine a state that is assumed to be the final state of the optimal exact quantum query algorithm. The superposition property of quantum states must be used to explore multiple paths simultaneously during the query process, thereby improving efficiency.
The construction of the quantum state involves the initialization of qubits, phase modulation, and gate operations. This process can be implemented using basic quantum gates such as rotation gates and CNOT gates, so that the required quantum state can be realized in the quantum circuit.
Step Three: Finding Each Unitary Operator in the Uncertainty Algorithm
Finally, each unitary operator must be found within the uncertainty algorithm. This step is crucial because the selection of unitary operators directly affects the effectiveness of the quantum query. By reasonably selecting and designing unitary operators, efficient quantum querying can be achieved.
In this process, it may be necessary to utilize methods such as mathematical optimization and machine learning to find the optimal combination of unitary operators. In addition, for specific Boolean functions, customized algorithms may be required to ensure query efficiency and accuracy.
The implementation logic of MicroAlgo's entire technical framework can be summarized as: the use of multilinear polynomials, the construction of quantum states, and the selection of unitary operators. Through the sum-of-squares representation, the properties of Boolean functions can be effectively analyzed, providing a theoretical foundation for the subsequent design of quantum algorithms.
The constructed quantum states not only need to meet the basic requirements of querying but must also fully leverage the characteristics of quantum superposition and entanglement to enhance the parallelism of queries. Finally, by carefully selecting and designing unitary operators, efficient querying of Boolean functions can be achieved, thereby maximizing the performance of quantum algorithms.
MicroAlgo's development of this technology is based on the sum-of-squares representation of Boolean functions and has successfully designed a technical framework for optimal exact quantum query algorithms, bringing a brand-new perspective and implementation path to the field of quantum computing.
Through in-depth analysis of the structure of Boolean functions and with the aid of quantum state construction and precise design of unitary operators, this framework demonstrates outstanding query efficiency and theoretical superiority. The sum-of-squares representation of Boolean functions not only provides a solid mathematical foundation for the design of quantum decision tree algorithms but also effectively reveals the intrinsic relationships between functions, helping us better understand the complexity issues in quantum algorithms. This method, which combines algebraic techniques with quantum physics, offers new research directions for quantum computing and lays the groundwork for the further optimization of exact quantum query algorithms.
Although the current technical framework faces challenges in dealing with certain practical problems—for example, the algorithm may be infeasible in specific situations—the algorithmic framework based on sum-of-squares representations has already demonstrated its powerful potential in solving problems with low complexity. This optimization of the quantum query model can significantly reduce the consumption of computational resources while increasing the query speed of the algorithm, thereby further improving the overall performance of quantum computing. This has great application prospects and practicality across multiple fields within quantum information science, including quantum communication, quantum security, and quantum machine learning.
As a disruptive technology, quantum computing's potential impact will far exceed the scope of traditional computing. The optimal exact quantum query algorithm technical framework developed by MicroAlgo, though currently focused mainly on the exact querying of Boolean functions, possesses a highly scalable philosophy and methodology. By further exploring more complex Boolean functions and their quantum representations, it is expected that MicroAlgo's technology will be applied to a broader range of fields, including large-scale quantum data processing, complex system optimization, and future AI enhancement. As quantum computing technology continues to evolve and improve, more and more difficult problems will find new solutions through this algorithmic framework.
Whether in academia or industry, the potential value of this technical framework is immeasurable. It will drive quantum computing to take a solid step from theoretical research toward practical application and inject continuous new momentum into global scientific and technological innovation.
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.
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SOURCE MicroAlgo Inc.