MicroCloud Hologram Inc. Utilizes Matrix Product States to Achieve High-Precision Quantum State Preparation with Mirror-Symmetric Probability Distribution
MicroCloud Hologram (NASDAQ: HOLO) has announced a breakthrough in quantum computing technology using Matrix Product States (MPS) for high-precision quantum state preparation with mirror-symmetric probability distribution. The new method achieves:
- Computational efficiency increase by two orders of magnitude
- Improved accuracy through shallow quantum circuit design
- Linear scalability with qubit numbers
- Enhanced performance on current Noisy Intermediate-Scale Quantum (NISQ) devices
The technology primarily uses nearest-neighbor qubit gates and reduces entanglement through mirror symmetry, making it particularly effective for quantum Monte Carlo methods, quantum financial modeling, and quantum machine learning applications. The method's approximation accuracy mainly depends on bond dimension rather than qubit numbers, setting the stage for large-scale adoption.
MicroCloud Hologram (NASDAQ: HOLO) ha annunciato un avanzamento nella tecnologia di calcolo quantistico utilizzando gli Stati Prodotto Matrice (MPS) per la preparazione di stati quantistici ad alta precisione con distribuzione di probabilità simmetrica rispetto allo specchio. Il nuovo metodo raggiunge:
- Aumento dell'efficienza computazionale di due ordini di grandezza
- Maggiore accuratezza attraverso la progettazione di circuiti quantistici superficiali
- Scalabilità lineare con il numero di qubit
- Prestazioni migliorate sui dispositivi quantistici attuali a scala intermedia rumorosa (NISQ)
La tecnologia utilizza principalmente porte qubit a vicinato e riduce l'intreccio attraverso la simmetria dello specchio, rendendola particolarmente efficace per metodi Monte Carlo quantistici, modellazione finanziaria quantistica e applicazioni di apprendimento automatico quantistico. L'accuratezza dell'approssimazione del metodo dipende principalmente dalla dimensione del legame piuttosto che dal numero di qubit, preparando il terreno per un'adozione su larga scala.
MicroCloud Hologram (NASDAQ: HOLO) ha anunciado un avance en la tecnología de computación cuántica utilizando Estados Producto Matriz (MPS) para la preparación de estados cuánticos de alta precisión con una distribución de probabilidad simétrica respecto al espejo. El nuevo método logra:
- Aumento de la eficiencia computacional de dos órdenes de magnitud
- Mejora de la precisión a través del diseño de circuitos cuánticos superficiales
- Escalabilidad lineal con el número de qubits
- Rendimiento mejorado en los dispositivos cuánticos actuales de escala intermedia ruidosa (NISQ)
La tecnología utiliza principalmente puertas de qubit de vecino más cercano y reduce el entrelazamiento a través de la simetría del espejo, lo que la hace particularmente efectiva para métodos de Monte Carlo cuántico, modelado financiero cuántico y aplicaciones de aprendizaje automático cuántico. La precisión de aproximación del método depende principalmente de la dimensión del enlace en lugar del número de qubits, allanando el camino para una adopción a gran escala.
마이크로클라우드 홀로그램 (NASDAQ: HOLO)은 거울 대칭 확률 분포를 이용한 고정밀 양자 상태 준비를 위해 행렬 곱 상태(MPS)를 사용하는 양자 컴퓨팅 기술의 혁신을 발표했습니다. 새로운 방법은 다음을 달성합니다:
- 두 배의 효율성 증가
- 얕은 양자 회로 설계를 통한 정확도 향상
- 큐비트 수에 따른 선형 확장성
- 현재의 노이즈 중간 규모 양자(NISQ) 장치에서의 성능 향상
이 기술은 주로 최근접 큐비트 게이트를 사용하며 거울 대칭을 통해 얽힘을 줄여, 양자 몬테카를로 방법, 양자 재무 모델링 및 양자 기계 학습 응용 프로그램에 특히 효과적입니다. 이 방법의 근사 정확도는 주로 큐비트 수가 아닌 결합 차원에 따라 달라지며, 대규모 채택을 위한 기반을 마련합니다.
MicroCloud Hologram (NASDAQ: HOLO) a annoncé une avancée dans la technologie de l'informatique quantique utilisant des États de Produit Matriciel (MPS) pour la préparation d'états quantiques de haute précision avec une distribution de probabilité symétrique par rapport au miroir. La nouvelle méthode atteint :
- Augmentation de l'efficacité computationnelle de deux ordres de grandeur
- Amélioration de la précision grâce à la conception de circuits quantiques peu profonds
- Scalabilité linéaire avec le nombre de qubits
- Performance améliorée sur les dispositifs quantiques actuels à échelle intermédiaire bruyante (NISQ)
La technologie utilise principalement des portes de qubits à proximité et réduit l'intrication grâce à la symétrie miroir, la rendant particulièrement efficace pour les méthodes de Monte Carlo quantiques, la modélisation financière quantique et les applications d'apprentissage automatique quantique. La précision d'approximation de la méthode dépend principalement de la dimension de liaison plutôt que du nombre de qubits, préparant le terrain pour une adoption à grande échelle.
MicroCloud Hologram (NASDAQ: HOLO) hat einen Durchbruch in der Quantencomputing-Technologie angekündigt, der Matrix-Produkt-Zustände (MPS) zur hochpräzisen Vorbereitung von Quantenstaaten mit einer spiegelsymmetrischen Wahrscheinlichkeitsverteilung verwendet. Die neue Methode erreicht:
- Steigerung der rechnerischen Effizienz um zwei Größenordnungen
- Verbesserte Genauigkeit durch das Design flacher Quanten-Schaltungen
- Lineare Skalierbarkeit mit der Anzahl der Qubits
- Verbesserte Leistung auf aktuellen Rausch-Intermediate-Scale-Quantum (NISQ)-Geräten
Die Technologie nutzt hauptsächlich nächstgelegene Qubit-Gatter und reduziert die Verschränkung durch Spiegelsymmetrie, was sie besonders effektiv für Quanten-Monte-Carlo-Methoden, quantenfinanzielle Modellierung und Anwendungen des quantenbasierten maschinellen Lernens macht. Die Näherungsgenauigkeit der Methode hängt hauptsächlich von der Bindungsdimension und nicht von der Anzahl der Qubits ab, was den Weg für eine großflächige Einführung ebnet.
- Achieved 100x improvement in computational efficiency
- Technology demonstrates superior precision in experimental tests
- Linear scalability enables adaptation to larger-scale quantum systems
- Reduced computational complexity while maintaining high accuracy
- Accuracy depends on bond dimension, requiring balance with computational resources
- Implementation may be by different quantum hardware architectures
- Additional computational costs with increased bond dimension
Insights
This announcement represents a significant technical advancement in quantum computing from HOLO. The company has developed a Matrix Product States (MPS) based method enabling more efficient quantum state preparation with mirror-symmetric probability distributions, achieving computational efficiency improvements of two orders of magnitude.
The core innovation centers on reducing quantum entanglement through mirror symmetry properties, allowing for shallower quantum circuits primarily composed of nearest-neighbor qubit gates. This approach demonstrates linear scalability with qubit count - a important advantage for implementation on current Noisy Intermediate-Scale Quantum (NISQ) devices.
What makes this development particularly valuable is its potential application in quantum Monte Carlo methods, quantum financial modeling, and quantum machine learning - all areas requiring efficient probability distribution loading. Traditional methods face entanglement challenges that increase circuit depth and noise susceptibility.
The technical innovation lies in representing high-dimensional probability distributions in low-rank decomposed form through MPS, while leveraging mirror symmetry to reduce redundant parameters. This enables HOLO's shallow quantum circuit design to achieve superior precision while minimizing error accumulation.
While promising, implementation challenges remain including dependence on bond dimension and variations across quantum hardware architectures. The research positions HOLO at the forefront of quantum state preparation techniques with potential applications in finance and machine learning as quantum computing hardware continues to advance.
SHENZHEN, China, March 18, 2025 (GLOBE NEWSWIRE) -- MicroCloud Hologram Inc. (NASDAQ: HOLO), (“HOLO” or the "Company"), a technology service provider, announced the proposal of a new method based on Matrix Product States (MPS) that enables high-precision quantum state preparation with a mirror-symmetric probability distribution. This research not only reduces the entanglement of the probability distribution but also significantly improves the accuracy of the matrix product state approximation, resulting in a computational efficiency increase by two orders of magnitude.
This new technology adopts a shallow quantum circuit design, primarily composed of nearest-neighbor qubit gates, and features linear scalability with respect to the number of qubits, greatly enhancing its feasibility on current noisy quantum devices. Furthermore, the study found that in tensor networks, approximation accuracy mainly depends on the bond dimension, with minimal dependence on the number of qubits, laying the foundation for future large-scale adoption. This research not only provides innovative optimization methods in theory but also demonstrates superior precision in experimental tests, foreshadowing broad prospects for quantum computing in practical applications.
Probability distributions play a critical role in quantum computing. Many quantum algorithms rely on the efficient loading of probability distributions, such as quantum Monte Carlo methods, quantum financial modeling, and quantum machine learning. However, traditional methods for loading probability distributions often face high levels of entanglement, causing the depth of quantum circuits to increase rapidly. This leads to reduced computational efficiency and heightened susceptibility to quantum noise.
HOLO constructs quantum states based on Matrix Product States (MPS) and leverages mirror symmetry to optimize the loading of probability distributions. Mirror symmetry implies that the probability distribution can, to some extent, reduce redundant information through symmetric transformations, thereby lowering the system's entanglement. This optimization approach enables more efficient quantum state preparation in shallow quantum circuits, making it particularly suitable for current Noisy Intermediate-Scale Quantum (NISQ) computers.
MPS is a tensor network model commonly used in quantum information and computation. It represents high-dimensional probability distributions in a low-rank decomposed form, thus reducing computational complexity. By exploiting mirror symmetry, this study successfully reduced redundant parameters, improving the approximation accuracy of MPS by two orders of magnitude. This means that, under the same computational resource constraints, this method can load probability distributions more accurately than existing MPS approaches, thereby enhancing the overall performance of quantum algorithms.
Another key advantage of HOLO’s method lies in its optimized shallow quantum circuit design. Traditional quantum state preparation methods typically require deep quantum circuits involving a large number of global gate operations, which lead to noise accumulation and pose significant challenges for current NISQ devices.
This study employs a novel quantum circuit design primarily composed of nearest-neighbor qubit gates. This design approach offers the following advantages:
Reduced Circuit Depth: By minimizing global gate operations, it avoids complex non-local entanglement operations, making the circuit easier to implement on current quantum hardware.
Improved Computational Stability: Since errors in noisy quantum devices increase with circuit depth, using shallower circuits reduces error accumulation and enhances computational accuracy.
Linear Scalability: The computational complexity of this method grows linearly with the number of qubits, enabling the technology to adapt to larger-scale quantum systems.
Under the same hardware conditions, this method achieves a precision improvement of two orders of magnitude compared to existing matrix product state-based quantum state preparation techniques, while significantly reducing computation time. This lays a foundation for large-scale quantum computing applications.
The core idea of using MPS for quantum state preparation is to represent high-dimensional probability distributions as low-rank tensor decompositions, thereby reducing computational load and optimizing storage structures.
Low Entanglement Representation: Since the entanglement of quantum states determines computational difficulty, the MPS method reduces computational complexity through low-rank approximations, making quantum states easier to implement on quantum hardware.
Suitable for High-Dimensional Probability Distributions: The MPS method is particularly well-suited for compressing and storing high-dimensional probability distributions, making it an ideal tool for fields such as quantum finance and quantum machine learning.
Controllable Computational Complexity: Compared to traditional global quantum state preparation methods, the MPS approach can control computational complexity while maintaining high computational accuracy across different qubit scales.
However, the HOLO method still faces some challenges. For instance, the accuracy of MPS depends to some extent on the bond dimension, and an increase in bond dimension introduces additional computational costs. Therefore, in practical applications, it is necessary to balance computational accuracy and resource demands to achieve optimal performance. Additionally, different quantum hardware architectures may impact the implementation of the MPS method. Future research could further optimize the implementation of MPS to make it adaptable to a wider range of quantum computing platforms.
The quantum state preparation method proposed by HOLO, based on matrix product states with mirror-symmetric probability distributions, achieves a computational accuracy two orders of magnitude higher than existing methods by reducing entanglement, optimizing shallow quantum circuit designs, and enhancing the approximation accuracy of MPS. This breakthrough not only provides a more feasible quantum state preparation solution for current NISQ devices but also lays the groundwork for future large-scale quantum computing applications.
Future research directions include further optimizing the computational complexity of matrix product states, improving their adaptability across different quantum hardware platforms, and exploring additional potential application areas. Moreover, as quantum computing hardware continues to advance, this method is expected to demonstrate even greater computational capabilities on real quantum devices, driving quantum computing toward a new stage of practical utility.
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
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Contacts
MicroCloud Hologram Inc.
Email: IR@mcvrar.com
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