Theory & Concepts#
This section explains the theoretical foundations of QML-HCS, covering quantum principles, causal inference, and the mathematical structure behind hypercausal hybrid learning systems.
Associated Paper#
QML-HCS: A Hypercausal Quantum Machine Learning Framework for Non-Stationary Environments
Hector E. Mozo
This paper introduces the theoretical foundations of QML-HCS by formalizing a hypercausal learning model for non-stationary environments. It defines the core execution semantics, including multi-branch future generation, projection policies, and continuous causal feedback mechanisms, and presents the mathematical structure used to maintain coherence and stability under distributional drift. The paper focuses on establishing the architectural and conceptual framework that informs the design and behavior of the QML-HCS software.
Pre-Temporal Model of Quantum Causal Order
Hector E. Mozo
This paper establishes the theoretical foundation for treating causal order
as a continuous, quantifiable resource within computational and learning
frameworks. It introduces the causal-indefiniteness measure λ(W),
defined via the trace distance between a quantum process and the convex set
of causally separable processes, providing a principled scalar that
interpolates between indefinite and definite causal structure.
Within the context of QML-HCS, this pre-temporal formulation supplies a
rigorous conceptual layer for modeling systems whose causal structure
evolves over time. The measure λ(W) functions as an operational
signal that can be tracked, optimized, or regularized within hypercausal
learning loops, enabling QML-HCS to reason about causal consolidation,
stability, and regime transitions in non-stationary environments.
In this way, the framework leverages pre-temporal causal dynamics not as an
abstract phenomenon, but as a computable control variable that informs
prediction, adaptation, and system-level coherence.