MicroCloud Hologram Inc. Develops Nonlinear Quantum Optimization Technology Based on Efficient Model Encoding
MicroCloud Hologram Inc. (NASDAQ: HOLO) has developed a groundbreaking nonlinear quantum optimization algorithm based on efficient model encoding technology. The innovation features two key advancements: multi-basis graph encoding and nonlinear activation functions. The algorithm achieves double computational speed while using half the quantum resources compared to traditional methods.
The technology can process computations with 512 qubits on a single GPU through optimized tensor network structure. The algorithm reduces measurement complexity to polynomial levels while maintaining accuracy. Key applications include portfolio optimization in finance, logistics optimization, and AI model training. The technology demonstrates significant potential for industrial quantum computing applications, particularly in solving complex non-convex optimization problems.
MicroCloud Hologram Inc. (NASDAQ: HOLO) ha sviluppato un innovativo algoritmo di ottimizzazione quantistica non lineare basato su una tecnologia efficiente di codifica del modello. L'innovazione presenta due avanzamenti chiave: codifica grafica multi-base e funzioni di attivazione non lineari. L'algoritmo raggiunge il doppio della velocità computazionale utilizzando metà delle risorse quantistiche rispetto ai metodi tradizionali.
La tecnologia è in grado di elaborare calcoli con 512 qubit su una singola GPU grazie a una struttura ottimizzata di reti tensoriali. L'algoritmo riduce la complessità delle misurazioni a livelli polinomiali mantenendo l'accuratezza. Le applicazioni principali includono l'ottimizzazione di portafogli finanziari, l'ottimizzazione della logistica e l'addestramento di modelli di intelligenza artificiale. La tecnologia mostra un potenziale significativo per applicazioni industriali del calcolo quantistico, specialmente nella risoluzione di problemi di ottimizzazione non convessi complessi.
MicroCloud Hologram Inc. (NASDAQ: HOLO) ha desarrollado un innovador algoritmo de optimización cuántica no lineal basado en una tecnología eficiente de codificación de modelos. La innovación presenta dos avances clave: codificación gráfica multibase y funciones de activación no lineales. El algoritmo logra el doble de velocidad computacional utilizando la mitad de los recursos cuánticos en comparación con los métodos tradicionales.
La tecnología puede procesar cálculos con 512 qubits en una sola GPU mediante una estructura optimizada de redes tensoriales. El algoritmo reduce la complejidad de la medición a niveles polinomiales manteniendo la precisión. Las aplicaciones clave incluyen la optimización de carteras financieras, la optimización logística y el entrenamiento de modelos de IA. La tecnología demuestra un gran potencial para aplicaciones industriales de computación cuántica, especialmente en la resolución de problemas complejos de optimización no convexa.
MicroCloud Hologram Inc. (NASDAQ: HOLO)는 효율적인 모델 인코딩 기술을 기반으로 한 혁신적인 비선형 양자 최적화 알고리즘을 개발했습니다. 이 혁신은 다중 기저 그래프 인코딩과 비선형 활성화 함수라는 두 가지 주요 발전을 특징으로 합니다. 이 알고리즘은 기존 방법에 비해 양자 자원을 절반만 사용하면서 계산 속도를 두 배로 향상시킵니다.
이 기술은 최적화된 텐서 네트워크 구조를 통해 단일 GPU에서 512 큐비트의 계산을 처리할 수 있습니다. 알고리즘은 정확성을 유지하면서 측정 복잡도를 다항식 수준으로 낮춥니다. 주요 응용 분야로는 금융 포트폴리오 최적화, 물류 최적화, AI 모델 훈련 등이 있습니다. 이 기술은 특히 복잡한 비볼록 최적화 문제 해결에 있어 산업용 양자 컴퓨팅 응용에 큰 잠재력을 보여줍니다.
MicroCloud Hologram Inc. (NASDAQ : HOLO) a développé un algorithme d'optimisation quantique non linéaire révolutionnaire basé sur une technologie efficace de codage de modèle. Cette innovation présente deux avancées majeures : un codage graphique multi-base et des fonctions d'activation non linéaires. L'algorithme atteint une vitesse de calcul doublée tout en utilisant la moitié des ressources quantiques par rapport aux méthodes traditionnelles.
La technologie peut traiter des calculs avec 512 qubits sur un seul GPU grâce à une structure optimisée de réseau tensoriel. L'algorithme réduit la complexité des mesures à un niveau polynomial tout en maintenant la précision. Les applications clés incluent l'optimisation de portefeuilles financiers, l'optimisation logistique et l'entraînement de modèles d'IA. Cette technologie montre un potentiel significatif pour les applications industrielles de l'informatique quantique, notamment dans la résolution de problèmes complexes d'optimisation non convexe.
MicroCloud Hologram Inc. (NASDAQ: HOLO) hat einen bahnbrechenden nichtlinearen Quantenoptimierungsalgorithmus entwickelt, der auf einer effizienten Modellkodierungstechnologie basiert. Die Innovation umfasst zwei wesentliche Fortschritte: Multi-Basis-Grafkodierung und nichtlineare Aktivierungsfunktionen. Der Algorithmus erreicht doppelte Rechengeschwindigkeit bei halbiertem Quantenressourceneinsatz im Vergleich zu herkömmlichen Methoden.
Die Technologie kann Berechnungen mit 512 Qubits auf einer einzigen GPU durch eine optimierte Tensor-Netzwerk-Struktur verarbeiten. Der Algorithmus reduziert die Messkomplexität auf polynomiale Ebenen bei gleichbleibender Genauigkeit. Wichtige Anwendungsgebiete sind Portfolio-Optimierung im Finanzwesen, Logistikoptimierung und KI-Modelltraining. Die Technologie zeigt großes Potenzial für industrielle Quantencomputing-Anwendungen, insbesondere bei der Lösung komplexer nicht-konvexer Optimierungsprobleme.
- Doubles computational speed while halving quantum resource requirements
- Successfully processes 512 qubits on a single GPU through optimized tensor network
- Reduces measurement complexity to polynomial level while maintaining accuracy
- Demonstrates practical applications in finance, logistics, and AI training
- None.
Insights
HOLO's new quantum optimization algorithm shows promise with reduced resource requirements, but lacks verified commercial implementation or revenue impact.
MicroCloud Hologram has announced a potentially significant advancement in quantum optimization algorithms, employing two key innovations that address fundamental limitations in current approaches. Their multi-basis graph encoding method represents complex optimization problems using fewer qubits, while their implementation of nonlinear activation functions appears designed to navigate non-convex optimization landscapes more effectively than traditional variational quantum algorithms (VQAs).
The technical claims are substantial - reducing measurement complexity to polynomial levels while doubling computational speed and halving quantum resource requirements. Most notably, they claim their tensor network structure enables simulations with 512 qubits on a single GPU, which would represent a meaningful efficiency breakthrough if validated.
What's particularly interesting from a technical perspective is their approach to the optimization landscape problem. Non-convex optimization is notoriously difficult, with algorithms frequently getting trapped in local minima. Their adaptive nonlinear activation functions potentially offer a more robust path to global optima, which would be valuable across numerous computational domains.
However, this announcement lacks critical technical specifics. There's no mention of the actual quantum hardware platform used for testing, no quantitative performance benchmarks against established algorithms, and no independent verification of results. The company references
While they position this as having applications in finance, logistics, and AI training, there's no evidence of actual implementations, commercial partnerships, or revenue potential from this technology. Without benchmarking against classical optimization approaches, it's impossible to assess whether their quantum approach offers practical advantages in real-world scenarios given the current state of quantum hardware.
SHENZHEN, China, May 12, 2025 (GLOBE NEWSWIRE) -- MicroCloud Hologram Inc. (NASDAQ: HOLO), (“HOLO” or the "Company"), a technology service provider, announced the development of a groundbreaking nonlinear quantum optimization algorithm based on efficient model encoding technology. This algorithm significantly enhances computational efficiency while reducing the consumption of quantum resources. This innovation not only addresses the key bottlenecks of current quantum optimization methods but also demonstrates remarkable performance advantages in practical applications, paving the way for the industrial adoption of quantum computing.
Traditional quantum optimization algorithms primarily rely on the Variational Quantum Algorithm (VQA) framework, where the depth of quantum circuits is often high, making the demand for computational resources difficult to meet. However, HOLO's efficient model encoding technology overcomes this limitation through two key innovations: multi-basis graph encoding and the application of nonlinear activation functions.
The multi-basis graph encoding method is a novel quantum encoding strategy that effectively represents high-dimensional optimization problems with a limited number of qubits. In HOLO's approach, an optimized tensor network structure is employed to map high-dimensional optimization spaces using fewer qubits. This not only reduces the depth of quantum circuits but also improves computational efficiency.
On the other hand, the introduction of nonlinear activation functions enables HOLO's optimization method to better address non-convex optimization problems. Traditional variational quantum algorithms are often constrained by the optimization landscape, easily getting trapped in local minima when dealing with complex non-convex problems. In contrast, HOLO's nonlinear activation functions can adaptively adjust the optimization path during training, allowing the algorithm to converge more efficiently to the global optimum. This innovation significantly enhances the algorithm's optimization capabilities, demonstrating greater adaptability in tackling large-scale optimization challenges.
In quantum computing, the efficient utilization of computational resources is of paramount importance. HOLO's nonlinear quantum optimization algorithm technology not only achieves a breakthrough in computational performance but also significantly improves resource utilization efficiency.
First, compared to existing methods, HOLO's algorithm reduces measurement complexity to a polynomial level. Measurement complexity is a critical metric in quantum computing, directly impacting the execution time and accuracy of computational tasks. Traditional quantum optimization methods typically require a large number of repeated measurements, whereas HOLO's algorithm optimizes measurement strategies, significantly reducing the number of measurements while maintaining computational accuracy. This leads to a notable improvement in overall computational efficiency.
Second, HOLO's algorithm doubles computational speed while halving the demand for quantum resources. This breakthrough stems from HOLO's optimized quantum circuit architecture. Compared to traditional approaches, HOLO's shallow circuit design can complete computational tasks in less time while reducing the need for qubits and quantum gate operations. In other words, this algorithm technology not only runs faster but also imposes lower hardware requirements, making it more feasible for implementation on current quantum computers.
In experiments, HOLO employed an efficient simulation strategy based on tensor methods. While traditional quantum computing simulations face exponential scaling issues as the number of qubits increases, our algorithm, with its optimized tensor network structure, enables computations to be completed on a single GPU even with 512 qubits. This experimental result not only validates the efficiency of our algorithm but also further demonstrates its potential for application in large-scale optimization problems.
HOLO's nonlinear quantum optimization algorithm has achieved groundbreaking progress in theoretical research while also showcasing broad prospects across multiple real-world application scenarios.
In the financial sector, optimization algorithms are widely used in tasks such as portfolio optimization and risk management. HOLO's algorithm can compute optimal investment portfolios in a shorter time and effectively address non-convex optimization challenges arising from market fluctuations. This opens up new possibilities for the application of quantum computing in the financial industry.
In logistics and supply chain management, the ability to solve optimization problems directly impacts overall efficiency. HOLO's technology can be applied to tasks such as intelligent scheduling and route planning, helping businesses utilize resources more efficiently, thereby reducing costs and improving service quality.
Furthermore, in the fields of artificial intelligence and machine learning, HOLO's algorithm can serve as an efficient optimization tool for training deep learning models. Leveraging the parallel computing capabilities of quantum computing, our algorithm can provide faster convergence speeds during the optimization process, laying the groundwork for future quantum artificial intelligence.
HOLO remains committed to advancing the development of quantum computing technology and continuously exploring new optimization methods. In the future, plans are in place to further refine this technology to accommodate larger-scale computational tasks. As quantum computing technology continues to progress, there is every reason to believe that efficient quantum optimization algorithms will play an increasingly vital role. HOLO's research not only offers a new perspective on quantum optimization but also establishes a solid foundation for the industrial application of quantum computing. In the forthcoming era of quantum computing, we will continue to lead technological innovation, contributing even more to global scientific and technological advancement.
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
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