QILIS, or Quantum-Inspired Lifecycle Interpretability System, is a framework for providing interpretability across the full lifecycle of neural network models. It combines quantum-inspired metrics, semantic evaluation, and dynamic optimization to ensure models remain transparent, efficient, and explainable from training through inference and analysis.

Key components include:

• DRMP for propagating relevance metrics like mutual information, cosine similarity, and purity across layers and phases.
• AMSE for maintaining semantic coherence of features.
• RBCO for dynamically pruning low-relevance features to improve efficiency.
• A knowledge base for storing and retrieving feature relevance data.
• An interpretive output generator for creating human-readable explanations.

QILIS supports various architectures, including CNNs, RNNs, and transformers, and is especially suited for high-stakes applications such as healthcare and finance.

Use Cases and Features

1. Healthcare Diagnostics
QILIS enables interpretable AI decisions in critical applications like disease detection and treatment recommendations. By tracing feature relevance from data input to diagnosis, it supports clinical transparency, regulatory compliance, and patient trust.

2. Financial Fraud Detection
In complex, high-volume transactional environments, QILIS helps identify fraud indicators by highlighting relevant features and filtering noise. Its lifecycle relevance tracking ensures consistency and traceability of fraud detection logic for auditors and regulators.

3. Audit-Grade AI Interpretability
Captured at the moment of decision with post-inference justification without rerun, ensuring immediate accountability and transparency.