MicroCloud Hologram Inc. Releases Hybrid Quantum-Classical Three-Dimensional Object Technology for Multi-Channel Quantum Convolutional Neural Networks

SHENZHEN, China, April 14, 2026 /PRNewswire/ -- MicroCloud Hologram Inc. (NASDAQ: HOLO), ("HOLO" or the "Company"), a technology service provider, released a forward-looking technological achievement: the hybrid quantum-classical three-dimensional object technology for multi-channel quantum convolutional neural networks, which, by introducing quantum computing at the level of core operators for three-dimensional vision, provides an entirely new engineered implementation path for high-dimensional perception tasks.

The core idea of this technology is not simply to attach quantum computing as an accelerator outside traditional deep learning models, but rather, starting from the computational essence of three-dimensional object detection, to re-examine the way convolution operations are expressed in high-dimensional feature spaces. Through long-term research, the HOLO technology team discovered that the primary computational burden in three-dimensional detection tasks is concentrated in multi-channel feature mapping, the sliding of convolution kernels in voxelized space, and the massive redundant computations generated during cross-scale feature fusion processes. These operations consume enormous computational power under the classical computing paradigm, yet their mathematical structures inherently possess a high degree of parallelism and additivity, which precisely aligns closely with the characteristics of quantum state superposition and parallel evolution. Based on this, HOLO proposed the Multi-Channel Quantum Convolutional Neural Network (MC-QCNN) as a key module in the hybrid quantum-classical three-dimensional detection framework.

At the overall architecture level, this hybrid quantum-classical three-dimensional object detection system adopts a clear division-of-labor design. The classical computing part is responsible for completing sensor data preprocessing, construction of three-dimensional point cloud or voxelized representations, as well as high-level semantic decoding and bounding box regression tasks, while the quantum computing part is precisely embedded into the convolutional feature extraction stage—which has the highest computational complexity and the fastest growth in feature dimensions. In this way, the system avoids the engineering infeasibility that would arise from quantizing the entire three-dimensional detection pipeline, while maximally unleashing the potential advantages of quantum computing in parallel feature mapping and channel-level convolutional computations.

The Multi-Channel Quantum Convolutional Neural Network is one of the core innovations of HOLO's technology. Unlike traditional quantum neural networks that only handle single-channel or low-dimensional inputs, MC-QCNN employs a scalable quantum state encoding strategy to map multi-channel three-dimensional feature maps into the quantum state space. Each channel is no longer treated as an independent classical feature map; instead, through quantum state entanglement and superposition mechanisms, joint representation is achieved within the quantum circuit. This design enables the correlations among multiple channels to be captured simultaneously in a single quantum evolution process, thereby significantly reducing the introduction of redundant computations and redundant parameters.

Specific implementation logic: The convolution module first normalizes and structurally encodes the multi-channel three-dimensional features coming from the classical network, making them satisfy the physical constraints required for quantum state preparation. Subsequently, parameterized quantum circuits are used to construct quantum convolution kernels. These convolution kernels no longer correspond to numerical weight matrices in the classical sense, but are instead defined by a set of trainable quantum gate parameters. During the evolution process, the quantum circuit naturally achieves parallel mapping of the high-dimensional feature space, which is equivalent to completing the joint computation of multiple classical convolution kernels in a single evolution. Finally, through measurement operations, the quantum state is mapped back to the classical feature space and fed into subsequent classical network layers for further processing.

In order to ensure the trainability and stability of this hybrid architecture in real-world engineering environments, HOLO introduced a knowledge distillation mechanism as a key auxiliary strategy during the model training phase. In this process, a high-performance classical three-dimensional object detection model is used as the teacher model, while the hybrid quantum-classical detection model serves as the student model. By learning the behavior distribution of the teacher model at intermediate feature layers and final prediction results, more efficient convergence is achieved. This design effectively alleviates the issues of the relatively small parameter space in quantum models and large gradient noise, enabling MC-QCNN to achieve detection accuracy that approaches or, in some scenarios, even exceeds that of pure classical models, while still operating under constrained quantum resources.

From an engineering implementation perspective, HOLO's hybrid quantum-classical three-dimensional object detection technology does not rely on large-scale, fault-tolerant quantum computers, but is instead designed for current and near-future noisy intermediate-scale quantum devices (NISQ). This strategy makes the technology realistically deployable in the short term, while also reserving sufficient room for expansion as quantum hardware performance improves in the future. With the continuous increase in the number of qubits, coherence time, and quantum gate fidelity, the scale and expressive power of multi-channel quantum convolutional networks are expected to be further enhanced, thereby driving the continuous evolution of three-dimensional perception systems in both performance and efficiency.

HOLO states that this technology is not merely a single-point breakthrough for three-dimensional object detection tasks, but rather a generalizable quantum-enhanced computing paradigm. The multi-channel quantum convolution concept it proposes can naturally be extended to a broader range of three-dimensional computer vision tasks, such as point cloud semantic segmentation, three-dimensional scene understanding, multi-sensor fusion perception, and more. By introducing quantum computing at the level of key operators, the company is exploring a technical route different from the traditional approach of trading computational power for accuracy, providing a more efficient and sustainable development direction for high-dimensional intelligent perception systems.

As the demand for three-dimensional perception capabilities continues to rise in autonomous driving, smart cities, and industrial intelligence, computational complexity and energy consumption issues will become key factors constraining the large-scale application of technology. HOLO's hybrid quantum-classical three-dimensional object detection technology based on multi-channel quantum convolutional neural networks has emerged precisely against this backdrop. It not only demonstrates the practical value of quantum computing in real-world artificial intelligence tasks, but also provides a clear and feasible engineering paradigm for enterprise-level quantum technology research and development. HOLO will continue to advance the optimization and industrial implementation of this technology, driving quantum-enhanced three-dimensional computer vision from the laboratory to real-world application scenarios.

About MicroCloud Hologram Inc.

MicroCloud Hologram Inc. (NASDAQ: HOLO) is committed to the research and development and application of holographic technology. Its holographic technology services include holographic light detection and ranging (LiDAR) solutions based on holographic technology, holographic LiDAR point cloud algorithm architecture design, technical holographic imaging solutions, holographic LiDAR sensor chip design, and holographic vehicle intelligent vision technology, providing services to customers offering holographic advanced driving assistance systems (ADAS). MicroCloud Hologram Inc. provides holographic technology services to global customers. MicroCloud Hologram Inc. also provides holographic digital twin technology services and owns proprietary holographic digital twin technology resource libraries. Its holographic digital twin technology resource library utilizes a combination of holographic digital twin software, digital content, space data-driven data science, holographic digital cloud algorithms, and holographic 3D capture technology to capture shapes and objects in 3D holographic form. MicroCloud Hologram Inc. focuses on the development of quantum computing and quantum holography. With cash reserves exceeding 390 million USD, the company plans to invest over 400 million USD in blockchain development, quantum computing R&D, quantum holography technology, as well as in the development of derivatives and technologies in cutting-edge fields such as AI, AR, and more. MicroCloud Hologram Inc.'s goal is to become a global leader in quantum holography and quantum computing technologies.

Safe Harbor Statement

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SOURCE MicroCloud Hologram Inc.