MicroCloud Hologram Inc. Quantum Computing-Driven Multi-Class Classification Model Demonstrates Superior Performance

SHENZHEN, China, Oct. 2, 2025 /PRNewswire/ -- MicroCloud Hologram Inc. (NASDAQ:HOLO), ("HOLO" or the "Company"), a technology service provider, introduced a significant development—Multi-Class Quantum Convolutional Neural Network (Quantum Convolutional Neural Network, QCNN). The core objective of this technology is to leverage the unique advantages of quantum computing to propel multi-class classification of classical data into a new dimension. By integrating quantum algorithms with the structure of convolutional neural networks, it not only achieves efficient processing of classical data but also demonstrates performance potential surpassing traditional neural networks in complex classification tasks with an increasing number of categories. This achievement marks a shift in the application of quantum computing in large-scale machine learning, moving from theoretical exploration toward practical and feasible industrialization.

For a long time, multi-class classification problems have played a critical role in various application scenarios such as information retrieval, image recognition, speech processing, and natural language processing. Whether in the sub-task processes of search engines or in high-precision scenarios like autonomous driving and medical image analysis, the capability of classifiers directly determines the reliability and efficiency of the system. Classical convolutional neural networks have driven tremendous leaps in artificial intelligence over the past decade, but as data dimensions and the number of categories continue to increase, issues such as computational cost, energy consumption, and generalization performance bottlenecks have become increasingly prominent. The multi-class QCNN technology developed by HOLO aims to leverage the inherent advantages of quantum computing in parallelism and high-dimensional space representation to break through the limitations of classical CNNs, paving a new path for multi-class classification.

At the technical implementation level, HOLO's QCNN design does not simply quantize convolutional layers but instead simulates the core operations of convolutional neural networks by constructing parameterized quantum circuits. It utilizes the tensor product structure of quantum states to encode input data, thereby unfolding feature representations in an exponentially large Hilbert space. Unlike classical CNNs, which rely on filters to extract features from local regions, QCNN's quantum convolutional layer forms through quantum gate operations and entangled states of qubits, extracting cross-regional correlations during parallel quantum evolution. This design enables QCNN to more efficiently model complex feature distributions in multi-class classification tasks, particularly demonstrating performance far superior to classical CNNs in cases with a large number of categories.

Additionally, in classical neural networks, backpropagation algorithms and their gradient descent mechanisms form the core of training, whereas in QCNN, this logic is transferred to the optimization of parameterized quantum circuits. HOLO employs the cross-entropy loss function as the target function and utilizes the PennyLane framework for automatic differentiation of circuit parameters. HOLO's optimization methods are divided into two categories: the first is based on polynomial approximations derived from exact higher-order derivative calculations, obtaining high-order derivatives of the circuit output with respect to parameters through mathematical derivation, thereby achieving high-precision gradient estimation; the second is based on finite difference methods, sampling approximations at multiple parameter points to estimate higher-order gradients. Each method has its advantages: the former ensures training accuracy, while the latter enhances computational flexibility. The combination of the two not only accelerates training convergence but also effectively avoids the gradient vanishing problem in quantum circuit optimization.

The advantage of QCNN in computational efficiency is remarkable. Classical CNNs often face memory and computational power bottlenecks when processing large-scale datasets, whereas QCNN, by leveraging quantum superposition and parallel evolution, mitigates this issue to a certain extent. Particularly in cases with relatively fewer parameters, QCNN demonstrates higher efficiency in convergence speed, which not only implies shorter training times but also suggests that, as large-scale quantum hardware becomes available in the future, this method will possess inherent advantages in energy consumption and cost control.

HOLO's development of multi-class QCNN technology is a significant strategic move toward the industrialization of quantum computing, and quantum machine learning is poised to become the next technological revolution following deep learning. With continuous advancements in quantum hardware, including increases in qubit counts and improvements in error correction capabilities, quantum models like QCNN will play a substantial role in fields such as speech recognition, medical diagnostics, financial risk control, and autonomous driving. Particularly in tasks involving high-dimensional complex data, numerous categories, and highly nonlinear features, the advantages demonstrated by QCNN will directly translate into competitive edges in practical applications.

In the long term, HOLO's multi-class QCNN technology serves as a critical cornerstone in its quantum intelligence strategy. Through sustained research and development investments, HOLO aims to push this technology toward industrialized applications, building an intelligent computing platform for the future. Compared to classical artificial intelligence, quantum artificial intelligence not only holds potential in performance but also opens a new paradigm in theoretical frameworks. It is not merely an acceleration of traditional algorithms but potentially a fundamental restructuring of the logic of intelligent computing.

HOLO's development of multi-class Quantum Convolutional Neural Networks represents a significant technological breakthrough and a key step in its journey toward integrating quantum computing with artificial intelligence. It showcases the unique advantages of quantum computing in complex classification tasks and foreshadows the vast prospects of quantum machine learning in future applications. As research deepens and hardware continues to improve, QCNN technology will unleash its potential on a broader scale, injecting new momentum into intelligent computing and opening a new chapter for industrial development.

About MicroCloud Hologram Inc.

MicroCloud is committed to providing leading holographic technology services to its customers worldwide. MicroCloud's holographic technology services include high-precision holographic light detection and ranging ("LiDAR") solutions, based on holographic technology, exclusive holographic LiDAR point cloud algorithms architecture design, breakthrough technical holographic imaging solutions, holographic LiDAR sensor chip design and holographic vehicle intelligent vision technology to service customers that provide reliable holographic advanced driver assistance systems ("ADAS"). MicroCloud also provides holographic digital twin technology services for customers and has built a proprietary holographic digital twin technology resource library. MicroCloud's holographic digital twin technology resource library captures shapes and objects in 3D holographic form by utilizing a combination of MicroCloud's holographic digital twin software, digital content, spatial data-driven data science, holographic digital cloud algorithm, and holographic 3D capture technology. MicroCloud focuses on the development of quantum computing and quantum holography, and plans to invest over $400 million in cutting-edge technology sectors, including Bitcoin-related blockchain development, quantum computing technology development, quantum holography development, and the development of derivatives and technologies in artificial intelligence and augmented reality (AR).

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Safe Harbor Statement

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