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

Rhea-AI Summary

MicroCloud Hologram (NASDAQ: HOLO) has unveiled its groundbreaking Multi-Class Quantum Convolutional Neural Network (QCNN) technology. This innovation leverages quantum computing advantages to enhance multi-class classification of classical data, demonstrating superior performance compared to traditional neural networks.

The QCNN technology utilizes parameterized quantum circuits and quantum states to process data more efficiently than classical CNNs, particularly in scenarios involving multiple categories. The system shows remarkable advantages in computational efficiency, convergence speed, and energy consumption, making it particularly valuable for applications in speech recognition, medical diagnostics, financial risk control, and autonomous driving.

As part of its quantum intelligence strategy, HOLO plans to invest over $400 million in cutting-edge technology sectors, including quantum computing, quantum holography, blockchain development, and AI/AR technologies.

Positive

• Development of superior QCNN technology showing better performance than traditional neural networks
• Planned investment of over $400 million in cutting-edge technology sectors
• Enhanced computational efficiency and faster convergence speed compared to classical CNNs
• Potential applications across multiple high-value sectors including medical diagnostics and autonomous driving

Negative

• Technology still depends on future advancement in quantum hardware capabilities
• Significant R&D investment required for industrialization of the technology
• Implementation relies on quantum hardware that is not yet widely available

Insights

Quantum Computing Technology Analyst

HOLO announces advanced quantum neural network technology promising superior performance over classical systems for complex data classification tasks.

MicroCloud Hologram's announcement of their Quantum Convolutional Neural Network (QCNN) represents a significant technical advancement in the practical application of quantum computing to machine learning. The technology leverages quantum parallelism and high-dimensional space representation to tackle multi-class classification problems—a critical function in applications ranging from information retrieval to autonomous driving.

What makes this development particularly notable is the architectural approach. Rather than simply quantizing classical convolutional layers, HOLO has constructed parameterized quantum circuits that simulate CNN operations while exploiting quantum advantages. By encoding data through tensor product structures into exponentially large Hilbert spaces, the system can extract cross-regional correlations during quantum evolution—a capability that becomes increasingly valuable as classification categories multiply.

The company's implementation includes two complementary optimization methods: one using polynomial approximations from higher-order derivative calculations, and another employing finite difference methods. This dual approach addresses the notorious gradient vanishing problem in quantum circuit optimization while improving training convergence.

From an industry perspective, this represents a meaningful step toward practical quantum machine learning applications. The press release indicates performance advantages in convergence speed with fewer parameters, suggesting potential efficiency gains when deployed on more advanced quantum hardware in the future.

It's worth noting that HOLO has positioned this development as part of a broader quantum intelligence strategy, with the company reportedly planning to invest $400 million across various cutting-edge technologies including quantum computing. While the technology shows promise, investors should recognize that industrial-scale implementation will likely depend on continued advancements in quantum hardware capabilities, particularly in qubit counts and error correction.

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).

For more information, please visit http://ir.mcholo.com/

Safe Harbor Statement

This press release contains forward-looking statements as defined by the Private Securities Litigation Reform Act of 1995. Forward-looking statements include statements concerning plans, objectives, goals, strategies, future events or performance, and underlying assumptions and other statements that are other than statements of historical facts. When the Company uses words such as "may," "will," "intend," "should," "believe," "expect," "anticipate," "project," "estimate," or similar expressions that do not relate solely to historical matters, it is making forward-looking statements. Forward-looking statements are not guarantees of future performance and involve risks and uncertainties that may cause the actual results to differ materially from the Company's expectations discussed in the forward-looking statements. These statements are subject to uncertainties and risks including, but not limited to, the following: the Company's goals and strategies; the Company's future business development; product and service demand and acceptance; changes in technology; economic conditions; reputation and brand; the impact of competition and pricing; government regulations; fluctuations in general economic; financial condition and results of operations; the expected growth of the holographic industry and business conditions in China and the international markets the Company plans to serve and assumptions underlying or related to any of the foregoing and other risks contained in reports filed by the Company with the Securities and Exchange Commission ("SEC"), including the Company's most recently filed Annual Report on Form 10-K and current report on Form 6-K and its subsequent filings. For these reasons, among others, investors are cautioned not to place undue reliance upon any forward-looking statements in this press release. Additional factors are discussed in the Company's filings with the SEC, which are available for review at www.sec.gov. The Company undertakes no obligation to publicly revise these forward-looking statements to reflect events or circumstances that arise after the date hereof.

 

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SOURCE MicroCloud Hologram Inc.

FAQ

What is MicroCloud Hologram's (NASDAQ:HOLO) new quantum computing technology?

HOLO has developed a Multi-Class Quantum Convolutional Neural Network (QCNN) that uses quantum computing to enhance multi-class classification of data, showing superior performance compared to traditional neural networks.

How much is HOLO investing in quantum computing and related technologies?

MicroCloud Hologram plans to invest over $400 million in cutting-edge technology sectors, including quantum computing, quantum holography, blockchain development, and AI/AR technologies.

What advantages does HOLO's QCNN technology offer over traditional neural networks?

HOLO's QCNN technology offers superior computational efficiency, faster convergence speed, better energy consumption, and improved performance in processing complex multi-category classification tasks.

What are the potential applications of HOLO's quantum computing technology?

The technology can be applied in various fields including speech recognition, medical diagnostics, financial risk control, and autonomous driving, particularly in tasks involving high-dimensional complex data.

What are the current limitations of HOLO's QCNN technology?

The technology's full potential depends on future advances in quantum hardware, including improvements in qubit counts and error correction capabilities.