SHENZHEN, China, June 6, 2025 /PRNewswire/ — MicroAlgo Inc. (the “Company” or “MicroAlgo”) (NASDAQ: MLGO), explored the possibilities of quantum technology in various application scenarios, particularly in the training of Quantum Neural Networks (QNNs). Quantum Neural Networks combine the advantages of quantum computing and machine learning, promising revolutionary breakthroughs in fields such as data processing and pattern recognition.
Quantum Phase Estimation (QPE) is a key technique in quantum computing that leverages quantum superposition and interference principles to efficiently estimate the phase information of quantum states. In quantum neural network training, QPE is used to optimize network parameters. By precisely estimating the phase of quantum states, QPE can accelerate the convergence process of the network, improving training efficiency. This approach fully exploits the parallelism of quantum computing, enabling the processing of more information in the same amount of time, thereby significantly enhancing the training speed and accuracy of neural networks.
Quantum Circuit Construction: A quantum circuit with multiple qubits is constructed, mapping the structure and functionality of the neural network to provide the foundation for the training process. The circuit design must be precise to ensure that qubits accurately represent the parameters of the neural network.
Quantum State Initialization: A series of quantum gate operations are applied to initialize the qubits, placing them in specific quantum states. These quantum states correspond to the initial parameters of the neural network, serving as the starting point and foundation for the training process.
Execution of Controlled Unitary Operations: Controlled unitary operations are applied to entangle the neural network’s parameters with auxiliary qubits, accumulating phase information. By repeatedly applying controlled unitary operations with different powers, phase information is gradually accumulated onto the auxiliary qubits.
Application of Inverse Quantum Fourier Transform: The inverse Quantum Fourier Transform is applied to the auxiliary qubits, converting the quantum state from the Fourier basis to the computational basis. Phase information is extracted and converted into classical bit values for subsequent parameter optimization.
Parameter Optimization and Iteration: Based on the estimated phase information, the neural network’s parameters are optimized to make the network output closer to the desired results. Through multiple iterations, parameters are continuously adjusted until the network achieves the expected training performance.
Error Correction and Stability Enhancement: Advanced quantum error correction techniques are employed to reduce disturbances affecting qubits during operations. This improves the precision of phase estimation and the training stability of the neural network, ensuring the reliability of training results.
Quantum phase estimation has brought revolutionary changes to various fields through its application in MicroAlgo’s quantum neural network training. In image processing, quantum phase estimation enables quantum neural networks to classify and recognize images more efficiently, significantly outperforming traditional methods in both speed and accuracy. This technology makes the processing of large-scale image datasets faster and more precise, opening new possibilities for applications in image recognition and medical image analysis. In natural language processing, by optimizing network parameters, quantum neural networks can better understand and generate natural language text, demonstrating significant advantages in tasks such as machine translation, intelligent customer service, and text classification. The introduction of this technology not only enhances the efficiency of natural language processing but also improves its accuracy and fluency.
The application of quantum phase estimation in MicroAlgo’s quantum neural network training not only fully utilizes the parallelism of quantum computing, greatly accelerating the training process of neural networks, enabling more information to be processed in the same amount of time, and significantly improving training efficiency. At the same time, by precisely estimating the phase of quantum states, quantum phase estimation also optimizes the parameters of the neural network, enhancing the network’s accuracy, making the neural network perform more outstandingly in various tasks. In addition, this technology has good scalability, capable of adapting to the continuous development of quantum computing technology and the increase in the number of qubits, providing strong support for larger-scale quantum neural network training.
In the future, with the continuous advancement of quantum computing technology and the increasing number of qubits, the application of quantum phase estimation in quantum neural network training will become more extensive and in-depth.
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.
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