MicroAlgo Inc. Announces a Quantum Entanglement-Based Novel Training Algorithm
Entanglement-Assisted Training Algorithm for Supervised Quantum Classifiers
Shenzhen, May 16, 2025 (GLOBE NEWSWIRE) — MicroAlgo Inc. (“the Company” or “MicroAlgo”) (NASDAQ: MLGO) has unveiled a groundbreaking quantum entanglement-based training algorithm: the Entanglement-Assisted Training Algorithm for Supervised Quantum Classifiers. With a cost function grounded in Bell inequalities, this innovation encodes errors from multiple training samples simultaneously, breaking past the conventional limits of algorithms. This development provides an efficient, highly adaptable solution for optimizing supervised quantum classifiers.
The core innovation of MicroAlgo’s entanglement-assisted training algorithm lies in its use of quantum entanglement to construct a model that operates concurrently on multiple training samples and their corresponding labels. Unlike traditional machine learning models, quantum classifiers are capable of processing information not only from individual samples but also in parallel across multiple samples encapsulated in quantum states, thereby significantly improving training efficiency.
Within this algorithm, multiple training samples are represented as qubit vectors using the principle of quantum superposition. Quantum gate operations then encode their label information into quantum states. Due to the entangled state of the qubits, the classifier can process multiple samples simultaneously. This capability shifts away from the traditional sample-by-sample processing model, markedly enhancing both training speed and performance in classification tasks.
Moreover, the algorithm incorporates a cost function based on the principles of Bell inequalities, a crucial component of quantum mechanics that underscores the differences between quantum entanglement and classical information processing. By encoding the classification errors of multiple samples directly into the cost function, the optimization moves from correcting individual sample errors towards considering the collective efficacy of all samples. This shift effectively overcomes the local optimization challenges prevalent in traditional algorithms, significantly boosting classification accuracy.
The development of MicroAlgo’s entanglement-assisted training algorithm for supervised quantum classifiers hinges on vital components of today’s quantum computing technology: qubits, quantum gate operations, and quantum measurement. With these fundamental units, the algorithm is poised to process input data proficiently on a quantum computer.
Key Components of the Algorithm
Representation and Initialization of Qubits: At the algorithm’s outset, training samples are transposed into qubits, with each embodying one or more qubits that are initialized into specific states. Entangling operations are conducted between multiple qubits to enable collaborative processing of sample data in following steps.
Construction of Quantum Entanglement: A hallmark of quantum computing, entanglement arranges training samples into an interconnected state, where shared information is processed collectively. This setup not only improves processing efficiency but also accelerates convergence during training.
Application of Bell Inequalities and Cost Function Optimization: Central to the power of quantum entanglement, Bell inequalities are employed to forge the cost function intended to minimize classification errors. Unlike traditional methods that focus on individual errors, this innovative approach evaluates simultaneous collective sample performance, allowing optimization processes to focus on synthesizing the performance of multiple samples. Through the rapid computations of quantum algorithms, this cost function is minimized effectively to deliver optimal classification outcomes.
Interpretation and Output of Classification Results: The algorithm concludes by broadcasting classification results through quantum measurement. Depending on the task, training samples find their assignment into binary or multiple categories, leveraging quantum computing’s parallel processing to resolve complex classification tasks within a compressed timeframe.
Significantly, this technology capitalizes on quantum entanglement to parallelize training across numerous samples, heightening training speed and enhancing classification precision. Particularly when addressing immense datasets where traditional methods contend with computational constraints, quantum computing surmounts these obstacles effortlessly.
In addition, the cost function based on Bell’s inequality promises greater robustness than traditional error minimization protocols, adeptly managing errors from multiple training samples and avoiding local optimization pitfalls common in conventional methodologies. This enhancement renders the supervised quantum classifier remarkably effective for tackling intricate classification challenges.
Nevertheless, quantum computing still confronts myriad challenges, such as stabilizing qubit count and reducing error rates, both influencing algorithm performance in practical scenarios. Overcoming these technical hurdles by implementing efficient algorithms on present quantum platforms remains an ongoing necessity.
With quantum computing technology rapidly advancing, the entanglement-assisted training algorithm by MicroAlgo for supervised quantum classifiers signifies a pivotal potential for propelling future innovations in quantum machine learning. By ingeniously blending quantum entanglement with established classification algorithms, this approach shows extraordinary promise for amplifying training efficiencies and enhancing accuracy. Though hurdles persist, continued strides in quantum hardware and theoretical research reinforce the belief that quantum computing will revolutionize machine learning, pushing beyond binary solutions into domains of far greater complexity.
About MicroAlgo Inc.
MicroAlgo Inc. is a Cayman Islands exempted company devoted to crafting and employing custom central processing algorithms. By syncretizing these algorithms with software, hardware, or a combination of both, MicroAlgo supplies comprehensive solutions designed to expand customer bases, boost end-user satisfaction, actualize cost savings, curtail power usage, and accomplish technological goals. Its services encompass optimizing algorithms, amplifying computational power sans hardware updates, lightweight processing of data, and sophisticated data intelligence services. MicroAlgo’s prowess in efficient software and hardware optimization through bespoke central processing algorithms propels the company’s long-term trajectory.
Forward-Looking Statements
This release contains statements that could be viewed as “forward-looking statements,” subject to numerous conditions, many beyond MicroAlgo’s control, as outlined in the Risk Factors section of the company’s periodic reports on Forms 10-K and 8-K, available on the SEC’s website, www.sec.gov. Terms like “expect,” “estimate,” “project,” “anticipate,” “plan,” and similar expressions intend to identify forward-looking statements. These encompass expectations regarding future performance and the prospective financial impacts from business transactions.
MicroAlgo disclaims any obligation to update these statements in light of any changes following this release, unless mandated by law.
