QML Engine
Quantum graph neural networks, variational quantum classifiers, quantum kernel methods, and hybrid ML pipelines for defense intelligence and pattern recognition.
The QML Engine provides quantum-enhanced machine learning algorithms for problems where classical methods plateau. Quantum Graph Neural Networks (QGNNs) for relational data in intelligence analysis, Variational Quantum Classifiers (VQCs) for high-dimensional pattern recognition, and quantum kernel methods for non-linear feature spaces. All algorithms include automatic classical fallbacks.
Quantum machine learning model development and training.
What's Included
Quantum Graph Neural Networks
QGNN architectures for intelligence graph analysis, social network detection, and relational pattern recognition using parameterized quantum circuits.
Variational Quantum Classifiers
VQC for high-dimensional classification tasks with quantum feature maps and trainable rotation gates.
Quantum Kernel Methods
Quantum-enhanced kernel functions for support vector machines with provably larger feature spaces.
Hybrid ML Pipelines
Combined quantum-classical architectures with quantum layers embedded in classical neural networks.
Quantum Data Compression
Quantum autoencoders for dimensionality reduction of high-dimensional datasets.
Specs & Parameters
Use Cases
Defense Intelligence Analysis
QGNN-based analysis of intelligence graphs for pattern detection and entity resolution in classified datasets.
Financial Pattern Recognition
VQC-based anomaly detection and pattern recognition in high-frequency trading and fraud detection.
Drug Discovery
Quantum kernel methods for molecular property prediction and drug-target interaction modeling.
Ready for QML Engine?
Typical engagement: 6-12 weeks. From assessment to deployment, Qubit handles the full pipeline.