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/
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Email: IR@mcvrar.com
