Quantum Network of Assets (QNA): A Density-Operator Framework for Market Dependence and Structural Risk Diagnostics

Battle Count: 65

QNA is explicitly positioned as a structural diagnostic rather than a predictive trading signal. QEWS is designed to detect abnormal structural deviation in dependence geometry, not to forecast returns. However, the framework is relevant for risk management, portfolio construction (tracking diversification capacity erosion), regime detection, and understanding cross-asset dependence reconfiguration around macro/policy events. The April 2025 tariff escalation case shows QEWS detecting structural shifts that classical spectral benchmarks miss, which could inform risk overlay decisions. The framework complements rather than replaces existing covariance-based tools used in quantitative trading.

The core computation involves: (1) constructing rolling multi-feature blocks per asset, (2) normalizing to unit-norm amplitude vectors, (3) forming the density matrix as an average of rank-one projectors, (4) computing eigen-decomposition for entropy and purity, (5) computing rolling z-scores for QEWS. The mathematical formalism is dense (density operators, partial traces, von Neumann entropy), but the actual numerical implementation uses standard linear algebra (eigen-decompositions via numpy). The multi-feature construction and rolling-window management add moderate engineering complexity. No optimization, training, or iterative fitting is required.

The paper describes a modular Python implementation using numpy and pandas with standard linear algebra routines for eigen-decompositions. The pipeline is deterministic and fully reproducible given input time series. However, no explicit GitHub repository or code link is provided. Data is sourced via yfinance (publicly available). Feature construction, rolling window parameters, and numerical safeguards (eigenvalue clipping) are documented. Sensitivity analyses across feature sets and window lengths are reported.