Personalized Healthcare from Point-to-Point.
Targeted.
Interpretable.
Quantum-enhanced.
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It’s built for datasets where conventional tools stall—whether due to dimensionality, noise, or complexity. From biomarker discovery to response prediction, Xpectra adapts to real-world constraints, delivering computational insight with clinical relevance.
Quantum feature embedding for structured biomedical signals​​
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Variational hybrid circuits adapted for today’s NISQ hardware
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Optimization frameworks combining quantum sampling with classical training
Xpectra is Quantiqun's algorithm that harnesses hybrid quantum-classical algorithms to tackle high-complexity problems in healthcare. By combining the unique power of quantum computing with classical machine learning, Xpectra unlocks new possibilities in treatment optimization, and biomedical research.
Xpectra is built around a three-layer architecture
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Classical Core Modules
Preprocessing, feature selection, and statistical modeling using tried-and-tested classical ML pipelines.
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Quantum-Augmented Layer
Select modules—such as kernel evaluations, dimensionality reductions, or entangled state mappings—use quantum subroutines, either variational or non-parametric.
Interpretation & Clinical Mapping
Final results are mapped back to interpretable formats (e.g., SHAP values, biomarkers, pathway-level visualizations) so physicians or researchers can act on the output.