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.
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.