MicroAlgo Inc (MLGO) Quote

Be very careful. 😗

moving

great

ya cool but I'm not interested as Lover, I went hard on PLUG / MLGO can wait for now

MLGO going from one high to the new high today, still $1.56 - but soon $2+ could easily come back
imho

plug is gonna rock MLGO

ok cool thanks for informing me. MLGO

New $80 million CD issued on top of others. I'm no longer interested.https://www.otcmarkets.com/filing/html?id=18485417&guid=kTc-kKRj3vwFdth

Everything sucks, I get out and the stock goes up 🤮🤦🤮🤦🤮🤦🤮🤦🤮🤮🤮🤮

Everything sucks, I get out and the stock goes up 🤮🤦🤮🤦🤮🤦🤮🤦🤮🤮🤮🤮🤮🤦🤦

news was good today

in the book there are important orders, at 1.15-1.16..

Oh ok. Yeah I agree with them😇

social forum Stocktwits for example, but also others...

What people?♥️

Wait for the CD to get bought out🫢

I would like it again under 1.00 somewhere in the $0.75 to $0.82 range🤭

bet I'm out now and it's starting to rise - lol

too many people expect 0.80...
I wouldn't want it to start early, leaving everyone out...it wouldn't be the first time for Wall Street !!!

no - i`m out now - dirty stock on good news
imo

Too soon :(

That reminds me. It's time for espresso! Talk soon, Spuds buddy!

MINT MLGO/PLUGGER

Hey, good luck on your buy-ins. I hope she explodes for you!🤩

oversold now , nice dip - MLGO should going the days

imho

ty , i`ve a some add.

this is really a great news

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

read my second last post , best joke ever MLGO aaand I guess pertaining to who wooo

.80 warrants but so many see you as low as 20 cents 

ya 79 now cool lover

nice lover

Yes, corvatsch, for sure! 🫢

Subslover,
on MLGO, are you still waiting to enter below the dollar ? 0.80 ?

thanks.

OT: Spuds, yes, I got in on PLUG yesterday at $0.71♥️

plus another trillion in investment into the us from trump with his 4 day a broad like come on PLUG , buy take a stab , did u?

gm lover MLGO

MicroAlgo Inc. Announces a Quantum Entanglement-Based Novel Training Algorithm — Entanglement-Assisted Training Algorithm for Supervised Quantum Classifiers
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.

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

get PLUG now MLGO next week

ok MLGO

Spuds, just as long as they don't do another CD. MLGO under $1.00 is where she is heading, but thinking $0.75 is very possible. Without another CD we will run again, not to $34.00 but $15.00 bones is possible😁

Short it down to $1.15 very little support at 1.80 1.60 then 1.20

NO sublover is saying a bone , I say a bone50 MLGO

Are you back in yet?

Gotcha.

Just a joke from $20s but hint hint sometimes best to short when convertibles involved 

PLUG worth another stab lover MLGO

What an embarrassment that quote is! If Einstein weren't cremated, he would be rolling in his grave🤓

Hey! You were given ample warning last winter, well under $2.00 to get in this. You said that Trump is against the Chinese ( you need me to cut and paste several of your posts). You don't have the class in saying you were wrong and congratulating the longs on this thread! MLGO only ran to $34.00, and with that said, you purposely lost contact with me out of jealousy and disrespect. Now you come back and bash it. It never ceases to amaze me how ignorant people like you are. You can't be schooled with your down 2,000% quote. Remedial arithmetic in Romper room knows that's impossible. They say you're nothing but a paper trader. Is that true?

How does one have more than 100% to the downside?
Math ain't' mathing!
<another 2000% to the downside>

$2 and going much much lower .Conversion at .20 cents ..get out some more timber demolition derby ..Plenty more pain to come another  2,000 thousand percent to the downside for investors coming lol...Pennyland scams at its best..Zero revenues at best and keep the fluff quantum news coming ..