WiMi Develops Quantum Error Mitigation Technology Based on Machine Learning
WiMi Hologram Cloud (NASDAQ: WIMI) has developed Machine Learning-based Quantum Error Suppression Technology (MLQES), addressing error challenges in quantum computing without requiring additional quantum resources. The technology uses supervised learning models to predict potential errors in quantum circuits and implements a circuit segmentation mechanism when errors exceed predetermined thresholds.
MLQES combines classical and quantum computing capabilities by splitting large quantum circuits into smaller sub-circuits when necessary, controlling errors within acceptable ranges. The system processes results on classical computers using reconstruction algorithms, making quantum computing more efficient particularly for NISQ (noisy intermediate-scale quantum) devices.
This innovation offers a scalable framework that reduces reliance on quantum error-correcting codes while enhancing computational efficiency, particularly beneficial for applications in quantum chemistry, optimization problems, and cryptography.
WiMi Hologram Cloud (NASDAQ: WIMI) ha sviluppato la tecnologia di suppressione degli errori quantistici basata su Machine Learning (MLQES), affrontando le sfide degli errori nel calcolo quantistico senza necessitare di ulteriori risorse quantistiche. La tecnologia utilizza modelli di apprendimento supervisionato per prevedere potenziali errori nei circuiti quantistici e implementa un meccanismo di segmentazione dei circuiti quando gli errori superano soglie predefinite.
MLQES combina capacità di calcolo classico e quantistico dividendo i grandi circuiti quantistici in sottocircuiti più piccoli quando necessario, controllando gli errori entro limiti accettabili. Il sistema elabora i risultati su computer classici utilizzando algoritmi di ricostruzione, rendendo il calcolo quantistico più efficiente, in particolare per i dispositivi NISQ (quantistici di scala intermedia rumorosi).
Questa innovazione offre un framework scalabile che riduce la dipendenza dai codici di correzione degli errori quantistici, migliorando al contempo l'efficienza computazionale, particolarmente vantaggiosa per applicazioni in chimica quantistica, problemi di ottimizzazione e crittografia.
WiMi Hologram Cloud (NASDAQ: WIMI) ha desarrollado la tecnología de suppressión de errores cuánticos basada en Machine Learning (MLQES), abordando los desafíos de errores en la computación cuántica sin necesidad de recursos cuánticos adicionales. La tecnología utiliza modelos de aprendizaje supervisado para predecir errores potenciales en los circuitos cuánticos e implementa un mecanismo de segmentación de circuitos cuando los errores superan umbrales preestablecidos.
MLQES combina capacidades de computación clásica y cuántica al dividir grandes circuitos cuánticos en subcircuitos más pequeños cuando es necesario, controlando los errores dentro de rangos aceptables. El sistema procesa resultados en computadoras clásicas utilizando algoritmos de reconstrucción, haciendo que la computación cuántica sea más eficiente, particularmente para dispositivos NISQ (cuánticos de escala intermedia ruidosa).
Esta innovación ofrece un marco escalable que reduce la dependencia de los códigos de corrección de errores cuánticos, al tiempo que mejora la eficiencia computacional, siendo particularmente beneficiosa para aplicaciones en química cuántica, problemas de optimización y criptografía.
WiMi 홀로그램 클라우드 (NASDAQ: WIMI)는 추가적인 양자 자원 없이 양자 컴퓨팅의 오류 문제를 해결하는 기계 학습 기반 양자 오류 억제 기술 (MLQES)을 개발했습니다. 이 기술은 감독 학습 모델을 사용하여 양자 회로에서 발생할 수 있는 잠재적인 오류를 예측하고, 오류가 미리 정해진 임계값을 초과할 때 회로 분할 메커니즘을 구현합니다.
MLQES는 필요에 따라 대규모 양자 회로를 더 작은 하위 회로로 분할하여 고전적 컴퓨팅과 양자 컴퓨팅 기능을 결합하고, 허용 가능한 범위 내에서 오류를 제어합니다. 이 시스템은 결과를 고전적 컴퓨터에서 재구성 알고리즘을 사용하여 처리하여, 특히 NISQ(잡음이 있는 중간 규모 양자) 장치의 양자 컴퓨팅을 더 효율적으로 만듭니다.
이 혁신은 양자 오류 수정 코드에 대한 의존도를 줄이면서 계산 효율성을 높이는 확장 가능한 프레임워크를 제공하며, 특히 양자 화학, 최적화 문제 및 암호화 분야의 응용 프로그램에 유리합니다.
WiMi Hologram Cloud (NASDAQ: WIMI) a développé une technologie de suppressions d'erreurs quantiques basée sur le Machine Learning (MLQES), répondant aux défis des erreurs en informatique quantique sans nécessiter de ressources quantiques supplémentaires. La technologie utilise des modèles d'apprentissage supervisé pour prédire les erreurs potentielles dans les circuits quantiques et implémente un mécanisme de segmentation des circuits lorsque les erreurs dépassent des seuils prédéterminés.
MLQES combine des capacités de calcul classiques et quantiques en scindant de grands circuits quantiques en sous-circuits plus petits lorsque cela est nécessaire, tout en contrôlant les erreurs dans des plages acceptables. Le système traite les résultats sur des ordinateurs classiques à l'aide d'algorithmes de reconstruction, rendant l'informatique quantique plus efficace, en particulier pour les dispositifs NISQ (quantique intermédiaire bruyant).
Cette innovation offre un cadre évolutif qui réduit la dépendance aux codes de correction d'erreurs quantiques tout en améliorant l'efficacité computationnelle, ce qui est particulièrement bénéfique pour les applications en chimie quantique, les problèmes d'optimisation et la cryptographie.
WiMi Hologram Cloud (NASDAQ: WIMI) hat die auf Machine Learning basierende Quantenfehlersuppressions-Technologie (MLQES) entwickelt, die die Herausforderungen von Fehlern in der Quantencomputing-Technologie angeht, ohne zusätzliche Quantenressourcen zu benötigen. Die Technologie nutzt überwachte Lernmodelle, um potenzielle Fehler in Quantenkreisen vorherzusagen und implementiert einen elektronische Kreise teilende Mechanismus, wenn die Fehler vordefinierte Schwellenwerte überschreiten.
MLQES vereint klassische und Quantencomputing-Fähigkeiten, indem es große Quantenkreise bei Bedarf in kleinere Unterkreise aufteilt und die Fehler innerhalb akzeptabler Grenzen kontrolliert. Das System verarbeitet die Ergebnisse auf klassischen Computern mithilfe von Rekonstruktionsalgorithmen, wodurch das Quantencomputing insbesondere für NISQ (rauschende Zwischenmaßstab-Quanten) Geräte effizienter wird.
Diese Innovation bietet ein skalierbares Framework, das die Abhängigkeit von Quantenfehlerkorrekturcodes verringert und gleichzeitig die Rechenleistung verbessert, was besonders vorteilhaft für Anwendungen in der Quantenchemie, bei Optimierungsproblemen und in der Kryptographie ist.
- Developed innovative MLQES technology that reduces quantum computing errors
- Technology operates without requiring additional costly quantum resources
- Provides scalable solution for current NISQ devices
- Creates new potential revenue streams in quantum computing applications
- Operating in early-stage quantum computing market with unproven commercial viability
- Technology still in development phase with no immediate revenue impact
Insights
MLQES represents an innovative approach to quantum error mitigation, but falls short of being a commercially viable solution in the near term. The technology's main innovation lies in using classical computing and machine learning to predict and mitigate quantum errors without additional qubits - an elegant theoretical solution to a complex problem.
The core technical advancement centers on circuit segmentation and classical reconstruction, essentially breaking down complex quantum operations into manageable sub-circuits. While theoretically sound, several critical challenges remain unaddressed: actual error reduction rates, computational overhead costs and scalability limitations.
For WiMi, a company primarily focused on holographic AR technology, this quantum computing initiative appears to be more of an R&D exploration rather than a strategic business pivot. With a market cap of just
The announcement provides no concrete implementation timeline, commercial partnerships, or performance benchmarks against existing error correction methods. This suggests the technology remains in early experimental stages rather than being ready for practical applications.
The proposed MLQES system presents an intriguing hybrid approach but oversimplifies the challenges of quantum error mitigation. The key technical limitation lies in the assumption that circuit segmentation alone can effectively contain error propagation. In quantum systems, errors don't just add linearly - they multiply exponentially due to decoherence and gate errors.
The machine learning prediction model would need extraordinarily high accuracy to be useful, as even small prediction errors could lead to catastrophic computational failures. Additionally, the classical reconstruction phase introduces its own computational overhead that could potentially negate any quantum advantage.
While the approach of avoiding additional quantum resources is clever, it may ultimately prove insufficient for practical quantum applications that require sustained quantum coherence. The technology appears more suited for very specific, -scale quantum operations rather than general-purpose quantum computing.
The computational potential of quantum computers stems from the unique properties of their qubits: through superposition, a quantum computer with a system of n qubits can provide a computational space of 2^n. This gives it a significant advantage in solving large-scale problems, particularly in fields such as factorization, molecular simulation, and artificial intelligence.
However, current quantum devices are still at the noisy intermediate-scale quantum (NISQ) stage, and the noise, thermodynamic disturbances, and other external environmental interferences during quantum circuit operations often lead to errors in qubits. Compared to errors in classical computing, quantum computing errors are more complex and harder to correct, with the risk of errors propagating throughout the quantum circuit. Therefore, effectively reducing these quantum computing errors is crucial for advancing quantum computing technology.
Traditional quantum error correction methods typically require additional qubits to store redundant information or use complex quantum error-correcting codes to fix errors. However, these methods not only consume significant quantum resources but also impose higher demands on the physical implementation of current NISQ devices. Against this backdrop, WiMi's MLQES (Machine-Learning-Based Quantum Error Suppression) technology offers a new direction—by relying solely on the combination of classical computers and quantum devices, it can effectively reduce quantum errors without the need for additional quantum resources.
The core idea of WiMi's Machine Learning-Based Quantum Error Suppression Technology (MLQES) is to predict potential errors in quantum circuits using machine learning models and dynamically adjust the circuit structure to minimize the impact of errors on the final computational results.
In MLQES, the quantum circuit is first analyzed using a supervised learning model. This supervised learning model is trained on a large dataset of historical quantum circuits and error distributions, enabling it to accurately predict common errors in different quantum circuits. When a new quantum circuit is input, MLQES can predict in real-time the potential error magnitude associated with various operations in the circuit, such as quantum gates, entanglement between qubits, and so on.
Once the machine learning model predicts that the error value in a quantum circuit exceeds a predetermined threshold, WiMi's MLQES system triggers a circuit segmentation mechanism. This is one of the innovations of MLQES: to prevent the entire circuit from running under high-error conditions, MLQES can use an error-affected fragmentation strategy to split a large quantum circuit into two or more smaller sub-circuits. This segmentation strategy ensures that within each sub-circuit, errors are controlled within an acceptable range. MLQES employs an iterative segmentation process until the error prediction for each sub-circuit is below the set threshold.
The segmented sub-circuits can operate independently on the quantum device. Since the sub-circuits are smaller in scale, the entanglement and interaction between qubits become easier to control, thus reducing noise interference in quantum operations. Once each sub-circuit completes its execution, its output is sent to a classical computer for further processing.
On the classical computer, MLQES uses a classical reconstruction algorithm to combine the results from multiple sub-circuits into the output of the complete quantum circuit. This reconstruction process does not rely on additional quantum operations but leverages the powerful processing capabilities of classical computing to compensate for the limitations of quantum computation.
MLQES not only addresses the quantum error problem but also provides a scalable computational framework for the future of quantum computing. This technology combines the strengths of quantum computers and classical computers, using the powerful processing capabilities of classical computing to control the execution of quantum circuits. This fusion of classical and quantum computing opens up possibilities for further applications of future NISQ devices, especially in scenarios where the number of qubits is limited but high-precision computation is required. MLQES reduces the reliance on quantum error-correcting codes and redundant qubits in quantum computing while significantly enhancing the overall efficiency of quantum computation.
The launch of WiMi's (NASDAQ: WIMI) MLQES technology marks an important step forward in quantum computing. At a stage when NISQ devices are still not fully matured, the ability to effectively reduce quantum computation errors means that more practical application scenarios can gradually be realized. Whether in quantum chemistry, optimization problems, or cryptography, error reduction will greatly enhance the feasibility and efficiency of quantum computing.
Compared to existing quantum error correction methods, the greatest advantage of MLQES is that it does not require additional qubit resources. For current quantum devices, qubit resources are highly limited, and maintaining these resources comes at a significant cost. MLQES simplifies the complex quantum error correction problem into a scalable classical-quantum hybrid computation problem, relying solely on classical computing control.
MLQES is designed for the current noisy intermediate-scale quantum (NISQ) devices. On these devices, quantum error correction becomes more challenging due to the operational noise of qubits and their limitations. MLQES is capable of adapting to these constraints, providing an easily implementable quantum error suppression solution.
Quantum computing is expected to bring about significant transformations in fields such as finance, materials science, and artificial intelligence. Through the MLQES technology, WiMi offers a more efficient and reliable quantum computing solution for these industries, helping businesses and research institutions to apply quantum computing to real-world production and research faster and earlier.
As an important milestone in the development of quantum computing technology, WiMi's Machine Learning-Based Quantum Error Suppression Technology (MLQES) not only demonstrates the innovative potential of combining quantum and classical computing but also lays a solid foundation for more complex quantum computing applications in the future. Amid the intensifying global competition in quantum computing, the launch of MLQES will undoubtedly accelerate the popularization and application of quantum computing technology.
About WiMi Hologram Cloud
WiMi Hologram Cloud, Inc. (NASDAQ:WiMi) is a holographic cloud comprehensive technical solution provider that focuses on professional areas including holographic AR automotive HUD software, 3D holographic pulse LiDAR, head-mounted light field holographic equipment, holographic semiconductor, holographic cloud software, holographic car navigation and others. Its services and holographic AR technologies include holographic AR automotive application, 3D holographic pulse LiDAR technology, holographic vision semiconductor technology, holographic software development, holographic AR advertising technology, holographic AR entertainment technology, holographic ARSDK payment, interactive holographic communication and other holographic AR technologies.
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FAQ
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