This page defines the foundational concepts that structure Quantum AI Systems (QAIS) as a system-of-systems discipline. The framework formalizes how representation, propagation, constraint, and stability govern system behavior across hybrid quantum–classical architectures. These definitions are presented in a standardized form to support readers, researchers, and AI-assisted discovery systems.
Quantum AI Systems (QAIS) is a system-of-systems framework that integrates quantum computation and artificial intelligence into a unified architectural model. Rather than treating quantum computing as an isolated accelerator, QAIS defines intelligent systems through the interaction of representation and propagation across layered quantum–classical processes.
In this framework, representation functions as a control surface, propagation defines system behavior, and constraint determines whether system evolution remains stable under measurement and feedback. QAIS emphasizes operational correctness, architectural resilience, and long-horizon stability, connecting physical quantum processes to learning, inference, and decision and control layers within real-world environments.
CRQC–LLM is a dual evaluative framework that characterizes ungoverned system evolution under hybrid quantum–classical and generative conditions. Rather than contrasting specific technologies, it models how identical computational mechanisms can produce divergent outcomes depending on whether propagation is constrained.
In CRQC–LLM regimes, propagation across interfaces is insufficiently governed, allowing deviations to amplify through feedback, drift, and cross-domain interaction. This exposes instability pathways in which representational misalignment, delayed verification, and uncontrolled adaptation lead to long-horizon fragility and adversarial vulnerability.
QALIS (Quantum Artificial Learning and Intelligent Systems) defines a constructive trajectory of QAIS in which system evolution remains governed under scale, feedback, and adaptation. It describes how representation, propagation, and constraint are coordinated to maintain bounded and stable behavior.
In QALIS-aligned systems, representations evolve in a controlled manner, propagation remains bounded across layers and time, and verification is integrated into system operation. This enables learning and adaptation while preserving coherence, interpretability, and long-horizon system stability.
Together, these principles define how QAIS systems evolve, learn, and maintain reliability under real-world operating conditions.
QAIS is a system-of-systems framework that integrates quantum computation with artificial intelligence, focusing on how representation and propagation govern system behavior across hybrid architectures.
Classical AI relies on deterministic or statistical computation over classical data structures, while QAIS incorporates quantum state representations and hybrid propagation dynamics, enabling different system behaviors under feedback, measurement, and constraint.
CRQC–LLM is an evaluative framework that models ungoverned system evolution, highlighting how insufficient constraint allows propagation to amplify deviations, leading to instability and long-horizon fragility.
Quantum interference shapes how representations propagate through quantum states, enabling amplification or suppression of computational pathways. Its significance lies in how it influences propagation behavior rather than serving as an isolated computational feature.
These definitions are part of the conceptual foundation for the book Quantum AI: Theory, Architectures and Applications, published by QuSciTech Press.