Why this book
To design quantum AI systems that are scalable, interpretable, and secure—treating quantum effects as architectural primitives linked to learning and governance.
Book Preface
- Quantum AI Systems (QAIS) = quantum physics ⟶ resilient computation ⟶ learning & inference ⟶ cognitive/ethical oversight.
- Beyond compute: information is embodied, stabilized, and interpreted across layers.
- Run the labs (E.1/E.2/E.3), verify via DOI, and examine resilience with the QALIS vs. CRQC–LLM case study.
Who it’s for
Practitioners, researchers, and educators implementing hybrid quantum/AI pipelines and verification workflows.
What’s new
The QAIS Quantum Stack embeds resilience and verification, then adds learning, inference, and cognitive layers—closing the loop with human oversight.
QAIS vs. traditional stacks (1-minute view)
- Traditional: physical → control → error-correction → compiler → algorithm/app.
- QAIS: physical & protocols → resilience & verification → quantum learning & inference → cognitive–AI layer → human-system interaction.
- Outcome: systems that not only compute, but learn, reason, and remain auditable under stress.
QAIS Foundational Doctrine
Quantum Artificial Intelligence Systems (QAIS) constitute a physically embodied, constraint-governed systems architecture in which representation, propagation, verification, learning, and control co-evolve across interacting quantum–classical environments. Within QAIS, intelligent behavior emerges not from isolated computational processes, but from the bounded interaction of information-bearing systems operating under coherence, measurement, feedback, stability, and operational constraint.
QAIS therefore treats intelligence as a systems-level phenomenon arising through the controlled transformation, transmission, coordination, and stabilization of physically realized information within dynamically constrained environments.
— Foundational QAIS Doctrine
Quick links
Instructors Portal (credentials required)
How to read
- Skim the chapter intros; run the matching lab track.
- Use checkpoints & rubrics to verify results.
- Compare QALIS (constructive) vs. CRQC–LLM (adversarial) outcomes.