MicroCloud Hologram Inc. Develops a Noise-Resistant Deep Quantum Neural Network (DQNN) Architecture to Optimize Training Efficiency for Quantum Learning Tasks

Rhea-AI Summary

MicroCloud Hologram (NASDAQ: HOLO) has developed a breakthrough noise-resistant Deep Quantum Neural Network (DQNN) architecture that optimizes quantum computing and learning tasks. The innovation uses qubits as neurons and unitary operations as perceptrons, enabling efficient hierarchical training while reducing quantum errors. Key features include a fidelity-based optimization strategy that minimizes training steps and quantum resource requirements, while maintaining stability in noisy environments. The architecture's unique design ensures qubit requirements scale with network width rather than depth, making it viable for current Noisy Intermediate-Scale Quantum (NISQ) computers. Benchmark tests demonstrated the network's ability to accurately learn unknown quantum operations and maintain performance even with noisy data, positioning HOLO at the forefront of quantum AI development.

Positive

• Development of innovative noise-resistant DQNN architecture that enhances quantum computing efficiency
• Architecture reduces quantum resource demands while maintaining robust performance in noisy environments
• Successful benchmark tests proving accurate learning capabilities and excellent generalization
• Scalable design that keeps qubit requirements manageable even as network depth increases

Negative

• No immediate revenue impact or commercialization timeline mentioned
• Technology still in development phase with no clear path to market implementation
• Dependent on future quantum hardware advancement for full potential realization

SHENZHEN, China, June 10, 2025 (GLOBE NEWSWIRE) -- MicroCloud Hologram Inc. (NASDAQ: HOLO), (“HOLO” or the "Company"), a technology service provider, announced the development of a noise-resistant Deep Quantum Neural Network (DQNN) architecture aimed at achieving universal quantum computing and optimizing the training efficiency of quantum learning tasks. This innovation is not merely a quantum simulation of traditional neural networks but a deep quantum learning framework capable of processing real quantum data. By reducing quantum resource demands and enhancing training stability, this architecture lays the foundation for future Quantum Artificial Intelligence (Quantum AI) applications.

Deep Neural Networks (DNNs) have demonstrated remarkable capabilities in various fields such as computer vision, natural language processing, and autonomous driving. However, with the rapid advancement of quantum computing, the scientific community is actively exploring how to leverage quantum computing to enhance the performance of machine learning models. Traditional quantum neural networks often borrow structures from classical neural networks and simulate classical weight update mechanisms using Parameterized Quantum Circuits (PQCs). However, these approaches are typically constrained by noise effects, and training complexity increases significantly as network depth grows.

Against this backdrop, HOLO has proposed a Deep Quantum Neural Network architecture that uses qubits as neurons and arbitrary unitary operations as perceptrons. This architecture not only supports efficient hierarchical training but also effectively reduces quantum errors, enabling robust learning from noisy data. This innovation overcomes the previous bottleneck of limited depth scalability in quantum neural networks, opening new opportunities for quantum artificial intelligence applications.

The core of this architecture lies in the construction of quantum neurons. Unlike classical neural networks, which use scalar values to represent neuron activation states, the neurons in a quantum neural network are represented by quantum states. These quantum states can store richer information and enhance computational power through mechanisms such as quantum superposition and entanglement.

Each neuron updates its state through unitary operations, analogous to activation functions in classical neural networks. These unitary operations preserve the normalization property of quantum states and ensure that information is not lost during computation. This perceptron design endows the quantum neural network with powerful expressive capabilities, enabling it to adapt to complex quantum data patterns while reducing computational errors.

To enable efficient training of the quantum neural network, HOLO employs an optimization strategy based on fidelity. Fidelity is a key metric that measures the similarity between two quantum states and is widely used in quantum information processing. During training, the quantum neural network aims to maximize the fidelity between the current state and the desired target state, rather than minimizing a loss function as in classical neural networks. This strategy allows the quantum neural network to converge to an optimal solution in fewer training steps, significantly reducing the quantum resources required for training.

Moreover, this optimization approach exhibits strong robustness, effectively handling the inherent noise and errors in quantum systems. In quantum hardware experiments, HOLO validated the effectiveness of this optimization method and found that it maintains stable learning performance even in noisy environments. This characteristic makes the architecture practically viable on current Noisy Intermediate-Scale Quantum (NISQ) computers.

While the depth expansion of classical neural networks typically leads to an exponential increase in parameters, quantum neural networks face challenges related to the number of qubits and the complexity of entanglement during expansion. To address this, the architecture optimizes the quantum state encoding method, ensuring that the required number of qubits scales only with the network’s width rather than its depth.

This innovative design implies that even as the neural network becomes very deep, the required qubit resources remain within a manageable range, thereby reducing hardware demands. This feature enables the deep quantum neural network to be trained on existing quantum processors and provides a feasible path for the realization of large-scale quantum machine learning models in the future.

HOLO conducted several benchmark tests. One key task involved learning unknown quantum operations, where the quantum neural network was trained to predict how unknown quantum operations affect different input states. The results demonstrated that this architecture not only accurately learns target quantum operations but also exhibits excellent generalization capabilities. This means that even with limited training data, the quantum neural network can still infer reasonable quantum mapping relationships. Furthermore, even when the training data contains some noise, the network maintains stable learning performance, further proving its robustness in noisy environments.

As quantum computing technology continues to advance, the practical application prospects of deep quantum neural networks are becoming increasingly broad. The development of HOLO’s architecture not only advances the field of quantum machine learning but also opens new possibilities for various industries. HOLO plans to further optimize this architecture and explore its potential applications on larger-scale quantum computers. In the future, with the development of quantum hardware, deep quantum neural networks are expected to play a critical role in more real-world scenarios, paving new paths for the integration of artificial intelligence and quantum computing.

HOLO has successfully developed a noise-resistant deep quantum neural network architecture that overcomes the limitations of traditional quantum neural networks, achieving efficient hierarchical training and quantum computing optimization. By using fidelity as the optimization target, this network reduces the demand for computational resources while maintaining robustness against noisy data. Experimental results have demonstrated its excellent generalization capabilities and practical feasibility, laying the foundation for the future development of quantum artificial intelligence. As quantum computing technology continues to mature, this innovative architecture is poised to play a significant role in multiple industries, ushering artificial intelligence into a new era of quantum computing.

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.

Contacts
MicroCloud Hologram Inc.
Email: IR@mcvrar.com

FAQ

What is MicroCloud Hologram's new DQNN architecture and how does it work?

MicroCloud's Deep Quantum Neural Network architecture uses qubits as neurons and unitary operations as perceptrons, enabling efficient hierarchical training while reducing quantum errors. It employs a fidelity-based optimization strategy to minimize training steps and resource requirements.

How does HOLO stock's quantum neural network differ from traditional neural networks?

Unlike traditional neural networks using scalar values, HOLO's quantum neural network uses quantum states as neurons, enabling richer information storage through quantum superposition and entanglement, while maintaining stability in noisy environments.

What are the practical applications of MicroCloud Hologram's quantum neural network?

The technology shows potential for learning unknown quantum operations and handling noisy data, with applications in quantum computing and artificial intelligence across various industries, though specific commercial applications are still in development.

What competitive advantage does HOLO's DQNN architecture provide?

The architecture's key advantage is its ability to scale efficiently with network width rather than depth, reducing hardware demands while maintaining performance in noisy environments, making it viable for current quantum computers.

What is the market potential for HOLO's quantum neural network technology?

While specific market figures aren't provided, the technology positions HOLO at the forefront of quantum AI development, with potential applications across multiple industries as quantum computing technology matures.