Advantages
Full-Stack Integrated Design
We build a complete, vertically integrated tech stack — from application layers (e.g., load forecasting, power prediction) down to quantum algorithms, programming frameworks, and hardware support — enabling deep fusion of quantum computing with industry needs.
Multi-Algorithm Synergy
By integrating diverse quantum-inspired models (such as QLSTM, QTransformer, and QMLP), we cover time-series prediction, stream processing, and large-scale model training, supporting R&D and deployment across diverse power industry scenarios.
Platform & Hardware Co-Design
The platform layer enables structured data processing, encryption, and simulation, while the underlying hardware — including superconducting quantum computers, virtual compute clusters, and quantum-classical hybrid systems — ensures computational flexibility and power.
Supports Multiple Quantum Programming Frameworks
Compatibility with diverse frameworks enhances programming flexibility and reduces development barriers, enabling seamless integration with existing classical computing workflows.
Industry Pain Points
Computational Inefficiency
Traditional algorithms are time-consuming and fail to meet real-time demands for critical tasks such as power flow calculations and load forecasting.
Inadequate Accuracy
Conventional methods struggle with complex data and uncertain scenarios, resulting in limited precision and impaired decision-making.
Difficulty Processing Massive Data
As data volumes surge, traditional computing faces severe computational bottlenecks, hindering grid data analysis and application efficiency.
Solution Architecture
Application Scenarios
Power Prediction
Quantum Long Short-Term Memory (QLSTM) networks enable high-precision short-term solar power forecasting, enhancing grid supply-demand balance.
Load Forecasting
QTensor-based quantum spatio-temporal attention networks integrate multi-source data (e.g., weather, user behavior) to improve the accuracy of grid load forecasting.
Power Flow Calculation
Quantum-accelerated power flow algorithms solve complex grid equations faster, enabling real-time grid scheduling and optimization.
Fault Diagnosis
Quantum feature extraction and classification models enhance fault detection accuracy, enabling rapid identification and response to grid anomalies.