MicroCloud Hologram Inc. Develops Quantum Algorithm Technology for Deep Convolutional Neural Network Exchange Submissions

Rhea-AI Summary

MicroCloud Hologram Inc. (NASDAQ: HOLO) has developed a groundbreaking quantum algorithm technology for deep convolutional neural network (CNN) exchange submissions. The core innovation is the Quantum Convolutional Neural Network (QCNN), which replicates classical CNNs while overcoming quantum computing challenges.

The QCNN implementation includes quantum state encoding for high-dimensional data mapping, quantum convolution kernels for feature extraction, and measurement-based nonlinear operations. The technology showed comparable classification accuracy to classical CNNs while demonstrating superior computational speed and resource efficiency.

The technology shows practical potential in various fields, including medical image analysis, autonomous driving, natural language processing, and financial data analysis. However, challenges remain in optimizing quantum circuits for larger datasets and addressing hardware limitations such as noise and qubit constraints.

Positive

• Developed new QCNN technology showing improved computational efficiency
• Achieved comparable classification accuracy to traditional CNNs with better resource utilization
• Technology applicable across multiple high-value sectors (medical, autonomous driving, finance)

Negative

• Technology faces hardware limitations and scalability challenges
• Requires further optimization for larger datasets
• Implementation constrained by current quantum computing infrastructure

SHENZHEN, China, Jan. 21, 2025 /PRNewswire/ -- MicroCloud Hologram Inc. (NASDAQ: HOLO), ("HOLO" or the "Company"), a technology service provider, have developed a quantum algorithm technology for deep convolutional neural network (CNN) exchange submissions, aimed at overcoming the computational bottlenecks of traditional CNNs and achieving performance improvements through the advantages of quantum computing. The core innovation of this technology lies in the design and implementation of the Quantum Convolutional Neural Network (QCNN). The QCNN not only fully replicates the output of classical CNNs but also overcomes common challenges in quantum computing, such as the difficulty of implementing nonlinear operations. Through carefully designed quantum circuits, HOLO has successfully implemented nonlinear activation functions and pooling operations within the quantum framework, opening a new door for quantum deep learning. More importantly, the architecture of the QCNN significantly improves computational efficiency in both forward and backward propagation, providing strong support for the training process of deep neural networks.

From a technical perspective, the implementation of QCNN consists of several key modules. First, it designs an input method based on quantum state encoding, which maps high-dimensional data into quantum states. This encoding technique leverages the properties of quantum state superposition and entanglement, allowing the convolution operation to be performed in parallel within high-dimensional space, significantly reducing computational complexity. Next, HOLO developed a set of quantum convolution kernels, which are implemented as unitary operations and can efficiently extract local features from the input data. By combining the inner product calculations of quantum states, the convolution process is completed at quantum speed.

For the implementation of nonlinear activation functions, HOLO introduces a measurement-based nonlinear operation. By performing partial measurements on quantum states, this approach achieves nonlinear mapping while preserving quantum superposition. This method overcomes the bottleneck of implementing nonlinear operations in quantum computing, while maintaining the unitarity of the computational process. Furthermore, QCNN also supports pooling operations, which are performed through reduction measurements of quantum states, making the feature dimension reduction process more efficient.

In terms of training, HOLO proposes an optimization algorithm based on quantum gradient computation. This method utilizes the parameterized representation of quantum states and combines it with the gradient descent method, enabling efficient updates of network parameters. To validate the performance of QCNN, numerical simulations of classification tasks were conducted on relevant datasets. The results show that, compared to classical CNNs, QCNN achieves comparable classification accuracy, but with significant advantages in computational speed and resource utilization efficiency. Particularly when handling large-scale datasets and high-dimensional inputs, the potential of QCNN is fully demonstrated.

The development of this technology is not only theoretically groundbreaking but also shows great potential in practical applications. In the field of image recognition, the performance improvements of QCNN enable it to handle more complex tasks in various scenarios. For instance, in medical image analysis, QCNN can quickly and accurately detect abnormal lesions, providing doctors with reliable diagnostic support. In the autonomous driving domain, QCNN's efficient computational capabilities allow real-time processing of environmental information around the vehicle, enhancing driving safety. Furthermore, QCNN also holds potential value in fields such as natural language processing and financial data analysis.

Although HOLO's QCNN has made significant progress, future research directions remain full of challenges and opportunities. First, further optimizing quantum circuits to handle larger datasets and more complex tasks is an issue worth exploring. Additionally, limitations in quantum computing hardware, such as noise and the constraints on the number of qubits, remain major bottlenecks for the technology's development. To address these issues, it is essential to continue exploring more robust quantum algorithm designs while closely monitoring developments in quantum hardware to ensure the practical feasibility of the technology.

Quantum Convolutional Neural Networks (QCNN), as an innovative deep learning framework, not only provide new ideas for the practical application of quantum computing but also bring infinite possibilities for the future development of deep learning. The implementation of HOLO's quantum algorithm technology for deep convolutional neural network exchange submissions not only demonstrates the immense potential of combining quantum computing with machine learning but also marks an important step toward a new era of intelligent computing.

Looking ahead, the potential of quantum convolutional neural networks will continue to be explored with the further advancements in quantum computing hardware. The breakthrough significance of this technology lies not only in its ability to address current computational bottlenecks but also in the new perspective it brings to the field of deep learning. The parallelism and superposition capabilities of quantum computing enable QCNN to efficiently process high-dimensional data, showing exceptional adaptability, especially when faced with increasingly complex data environments. By deeply integrating with industry needs, QCNN is expected to play an irreplaceable role in fields such as healthcare, transportation, finance, and fundamental science.

More importantly, the success of this technology also lays the foundation for the development of next-generation intelligent systems. From quantum artificial intelligence to collaborative frameworks for distributed quantum computing, the development of QCNN marks our progression toward a new computing era driven by quantum technology. This is not just a technological leap, but also a significant driving force for socioeconomic development. The power of quantum computing will provide entirely new solutions to many complex problems humanity faces. The successful development of QCNN is the starting point of this journey and is destined to become a milestone in the future integration of quantum technology and artificial intelligence.

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. 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 the new quantum technology developed by HOLO in January 2025?

HOLO developed a quantum algorithm technology for deep convolutional neural network (CNN) exchange submissions, specifically a Quantum Convolutional Neural Network (QCNN) that improves computational efficiency while maintaining accuracy.

How does HOLO's QCNN technology perform compared to traditional CNNs?

HOLO's QCNN achieves comparable classification accuracy to classical CNNs while showing significant advantages in computational speed and resource utilization efficiency.

What are the main applications of HOLO's new QCNN technology?

The QCNN technology can be applied in medical image analysis, autonomous driving systems, natural language processing, and financial data analysis.

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

The main limitations include the need for quantum circuit optimization for larger datasets, quantum computing hardware constraints, noise issues, and limitations in the number of available qubits.