MicroCloud Hologram Inc. Releases Hybrid Quantum-Classical Convolutional Neural Network, Achieving New Breakthrough in MNIST Multi-Class Classification
MicroCloud Hologram (NASDAQ: HOLO) announced a hybrid quantum-classical Quantum Convolutional Neural Network (QCNN) applied to the MNIST multi-class classification task on Oct 24, 2025. The company says the QCNN achieved accuracy comparable to classical CNNs using a quantum circuit with 8 data qubits and 4 auxiliary qubits, a novel Quantum Perceptron model, and a hybrid training loop that combines quantum feature extraction with classical optimization (softmax + cross-entropy). HOLO positions this work as a practical NISQ-era pathway and states plans to invest over $400 million in quantum, quantum holography, AI, AR, and related technologies.
MicroCloud Hologram (NASDAQ: HOLO) ha annunciato un modello ibrido quantum-classico Quantum Convolutional Neural Network (QCNN) applicato al compito di classificazione multi-classe MNIST il 24 ottobre 2025. L'azienda afferma che la QCNN ha raggiunto un'accuratezza paragonabile a quella delle CNN classiche utilizzando un circuito quantistico con 8 qubit di dati e 4 qubit ausiliari, un nuovo modello di Quantum Perceptron e un ciclo di addestramento ibrido che combina l'estrazione di caratteristiche quantistiche con l'ottimizzazione classica (softmax + cross-entropy). HOLO presenta questo lavoro come una via praticabile nell'era NISQ e dichiara piani di investire oltre $400 milioni in quantum, olografia quantistica, AI, AR e tecnologie correlate.
MicroCloud Hologram (NASDAQ: HOLO) anunció una red neuronal cuántica-convolucional híbrida Quantum Convolutional Neural Network (QCNN) aplicada a la tarea de clasificación multiclase MNIST el 24 de octubre de 2025. La compañía afirma que la QCNN logró una precisión comparable a las CNN clásicas utilizando un circuito cuántico con 8 qubits de datos y 4 qubits auxiliares, un nuevo modelo de Quantum Perceptron y un bucle de entrenamiento híbrido que combina la extracción de características cuánticas con la optimización clásica (softmax + entropía cruzada). HOLO posiciona este trabajo como una vía práctica en la era NISQ y señala planes de invertir más de $400 millones en quantum, holografía cuántica, IA, AR y tecnologías relacionadas.
MicroCloud Hologram (NASDAQ: HOLO) 하이브리드 양자-고전 양자 컨볼루션 신경망(QCNN)이 MNIST 다중 클래스 분류 작업에 2025년 10월 24일 적용되었다고 발표했다. 회사에 따르면 QCNN은 전통적 CNN과 대등한 정확도를 달성했으며, 데이터 큐빗 8개와 보조 큐빗 4개를 사용하고, 새로운 양자 퍼셉트론 모델과 양자 특징 추출과 고전 최적화를 결합한 하이브리드 학습 루프(소프트맥스 + 교차 엔트로피)를 통해 구현되었다고 한다. HOLO는 이 작업을 NISQ 시대의 실용적 경로로 제시하며 양자, 양자 홀로그램, AI, AR 및 관련 기술에 4억 달러 이상 투자할 계획이라고 밝혔다.
MicroCloud Hologram (NASDAQ : HOLO) a annoncé un réseau de neurones convolutifs quantiques hybrides Quantum Convolutional Neural Network (QCNN) appliqué à la tâche de classification multi-classe MNIST le 24 octobre 2025. La société affirme que la QCNN a atteint une précision comparable à celle des CNN classiques en utilisant un circuit quantique avec 8 qubits de données et 4 qubits auxiliaires, un nouveau modèle de Quantum Perceptron et une boucle d'apprentissage hybride qui combine l'extraction de caractéristiques quantiques avec l'optimisation classique (softmax + entropie croisée). HOLO présente ce travail comme une voie pratique à l'ère NISQ et annonce des plans d'investir plus de $400 millions dans le quantum, l'holographie quantique, l'IA, l'AR et les technologies associées.
MicroCloud Hologram (NASDAQ: HOLO) kündigte ein hybrides quantum-klassisches Quantum Convolutional Neural Network (QCNN) an, das auf die MNIST-Multiklassen-klassifikation angewendet wird, am 24. Oktober 2025. Das Unternehmen sagt, dass die QCNN eine Genauigkeit erreicht hat, die mit klassischen CNNs vergleichbar ist und verwendet einen Quanten-Schaltkreis mit 8 Daten-Qubits und 4 Hilfs-Qubits, ein neuartiges Quantum-Perceptron-Modell und eine hybride Trainingsschleife, die Quanten-Feature-Extraction mit klassischer Optimierung (Softmax + Kreuzentropie) kombiniert. HOLO positioniert diese Arbeit als praktikablen Weg im NISQ-Zeitalter und gibt an, mehr als $400 Millionen in Quantum, Quanten-Holografie, KI, AR und verwandte Technologien investieren zu wollen.
MicroCloud Hologram (NASDAQ: HOLO) أعلن عن شبكة عصبونية تلافيفية كمومية هجينة QCNN تطبق على مهمة التصنيف متعدد الفئات MNIST في 24 أكتوبر 2025. تقول الشركة أن QCNN حققت دقة تقارن بCNNs الكلاسيكية باستخدام دائرة كمومية تحتوي على 8 كيوبتات بيانات و4 كيوبتات مساعدة، ونموذج Perceptron كمومي جديد، وتدريب هجين يدمج استخراج السمات الكمية مع التحسين الكلاسيكي (softmax + تقاطع-التورين). تضع HOLO هذا العمل كمسار عملي في عصر NISQ وتعلن عن خطط لاستثمار أكثر من $400 مليون في الكوانتم، الهولوغرافيا الكمية، الذكاء الاصطناعي، الواقع المعزز والتكنولوجيات ذات الصلة.
MicroCloud Hologram (NASDAQ: HOLO) 宣布了一种混合量子-经典 量子卷积神经网络（QCNN），应用于 MNIST 多类分类任务，时间为 2025 年 10 月 24 日。公司表示 QCNN 已实现 与经典 CNN 相当的准确性，使用一个包含 8 个数据量子比特 和 4 个辅助量子比特 的量子线路、一个新颖的量子感知机模型，以及将量子特征提取与经典优化（softmax + 交叉熵）相结合的混合训练循环。HOLO 将这项工作定位为 NISQ 时代的实用路径，并表示计划在量子、量子全息、人工智能、增强现实及相关技术领域投资超过 $400 百万美元。
- Validated QCNN on MNIST with accuracy comparable to classical CNNs
- Quantum circuit design uses 8 data qubits + 4 auxiliary qubits
- Introduced a novel Quantum Perceptron and optimized circuit structure
- Planned investment of $400 million into quantum and related technologies
- Validation limited to the MNIST benchmark dataset only
- No explicit numeric accuracy, training cost, or hardware runtime metrics provided
SHENZHEN, China, Oct. 24, 2025 (GLOBE NEWSWIRE) -- MicroCloud Hologram Inc. (NASDAQ: HOLO), (“HOLO” or the "Company"), a technology service provider, proposed a Quantum Convolutional Neural Network (QCNN) based on hybrid quantum-classical learning and successfully applied it to the multi-class classification problem on the MNIST dataset, achieving accuracy comparable to classical Convolutional Neural Networks (CNNs). This achievement not only demonstrates the practical feasibility of quantum computing in machine learning tasks but also provides a new pathway for application exploration in the subsequent NISQ (Noisy Intermediate-Scale Quantum) era.
The multi-class classification problem is one of the most common tasks in computer vision and artificial intelligence applications. Whether in image recognition, handwritten digit recognition, traffic sign detection, medical image analysis, or natural scene understanding, multi-class classification algorithms play an irreplaceable role. Classical convolutional neural networks have accumulated significant achievements in this field and have achieved near-human recognition performance on multiple benchmark datasets. However, at the same time, as the depth and width of models increase, the reliance of classical methods on computational resources continues to grow, with model training and inference requiring large-scale GPU/TPU clusters, making cost and energy consumption issues that cannot be ignored.
Quantum computing, with its exponential acceleration and high-dimensional information processing capabilities, provides a new approach to solving problems in artificial intelligence. In theory, quantum algorithms can significantly improve computational efficiency in certain problems through the advantages of superposition and parallel computing. It is against this backdrop that HOLO proposed and implemented a quantum convolutional neural network method based on hybrid quantum-classical learning, which was validated on the MNIST multi-class classification task.
HOLO's approach is based on a hybrid quantum-classical learning framework, which leverages the combination of classical optimizers and quantum circuits to harness the strengths of both. Specifically, the quantum component handles feature extraction and high-dimensional mapping tasks, while the classical component is responsible for loss function optimization and final classification prediction. In terms of architecture, HOLO proposed a novel Quantum Perceptron model and designed an optimized quantum circuit structure, enabling the quantum convolutional layer to efficiently extract data features.
In the input layer, this approach uses eight qubits for data encoding, which undertake the quantum representation of MNIST image information. Additionally, four auxiliary qubits are introduced to enhance the circuit's expressive power and nonlinear modeling capabilities. Through this design, the entire circuit can effectively map input data under a limited qubit scale, providing high-quality quantum features for subsequent classification tasks.
In the output stage, the measurement results of the quantum circuit are fed into a softmax activation function, and the classification error is calculated using the Cross-Entropy Loss function. Subsequently, the classical optimizer updates the parameters in the quantum circuit based on gradient feedback, thereby realizing the training process. This hybrid model not only fully utilizes the mature experience of classical optimization but also avoids the convergence difficulties associated with purely quantum training.
This technical implementation consists of four main steps, enabling the quantum circuit to be called and optimized like a classical neural network layer.
Data Encoding Stage: The MNIST dataset contains grayscale handwritten digit images. Each image is scaled and normalized, then mapped to eight qubits through Angle Encoding or Amplitude Encoding. This process transforms the two-dimensional pixel matrix into a quantum state, leveraging quantum superposition to represent more information.
Quantum Convolution Stage: In this stage, the quantum circuit implements a feature extraction function similar to a convolution kernel through quantum gate operations. Unlike the sliding of convolution kernels in classical CNNs, quantum convolution utilizes quantum entanglement and superposition states to achieve nonlinear feature combinations, thereby effectively mapping input data in high-dimensional spaces. The optimized circuit structure proposed by HOLO, by introducing auxiliary qubits, enhances the feature representation capability, enabling the model to better capture inter-class differences in multi-class classification tasks.
Quantum Pooling Stage: Classical convolutional neural networks typically use pooling layers to reduce feature dimensionality and computational complexity. In the quantum version, HOLO achieves information compression by measuring a part of qubits or through specific quantum gate operations. This not only reduces the consumption of qubit resources but also enhances the model’s generalization ability to some extent.
Output and Optimization Stage: The measurement results of the quantum circuit form the model’s output vector, which is transformed into a class probability distribution through the softmax activation function. The Cross-Entropy Loss function is used to measure the discrepancy between the predicted results and the true labels. The classical optimizer adjusts the quantum circuit parameters (e.g., rotation angles) based on this loss, thereby iteratively improving classification performance.
HOLO’s quantum convolutional neural network demonstrates innovation in several aspects: First, HOLO designed a new quantum perceptron model that can more efficiently extract input features and provide stronger nonlinear mapping capabilities for the quantum convolutional layer. Second, the proposed optimized quantum circuit structure fully utilizes auxiliary qubits, enabling improved model performance under limited resources. Additionally, HOLO’s hybrid quantum-classical learning framework, through the integration of softmax and cross-entropy, successfully achieves the optimization of quantum circuit parameters, addressing the convergence difficulties of purely quantum training.
HOLO’s achievement lays the foundation for the application of quantum machine learning in real-world scenarios. In the future, methods based on quantum convolutional neural networks can be applied to more complex datasets and tasks. For example, in autonomous driving, quantum neural networks can assist vehicles in rapidly performing multi-class traffic sign recognition in real-time scenarios; in medical imaging, they can aid doctors in multi-class classification of lesions, thereby improving diagnostic efficiency; in fields such as financial risk control and security monitoring, quantum convolutional neural networks can also play a significant role.
From an industry perspective, HOLO’s research provides a novel AI algorithm solution. By integrating quantum computing with classical learning, future enterprises can achieve significant advantages in terms of energy efficiency, parameter efficiency, and computational acceleration in model training. This hybrid model also offers a feasible path for practical implementation in the NISQ era, helping enterprises gain a competitive edge at the forefront of quantum technology and artificial intelligence integration. This achievement not only demonstrates the potential of quantum computing in artificial intelligence but also provides a theoretical and practical foundation for subsequent larger-scale experiments and applications. It is believed that, with the continuous advancement of quantum hardware and the ongoing improvement of hybrid learning frameworks, quantum convolutional neural networks will showcase their unique advantages in more scenarios in the future, pushing artificial intelligence to new heights.
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
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 did MicroCloud Hologram (HOLO) announce on October 24, 2025?
How many qubits does HOLO's QCNN use in the MNIST implementation (HOLO)?
Does HOLO report specific accuracy or training cost numbers for the QCNN (HOLO)?
What training approach does HOLO use for its quantum neural network (HOLO)?
Will HOLO invest in quantum and related technologies following this announcement (HOLO)?
