What’s inside

Brief summary (from the Preface)

The QAIS Quantum Stack expands the conventional “hardware–to–application” view by treating quantum systems as learning, reasoning, and resilient architectures. Beginning at the quantum physical substrate, QAIS advances through operational protocols and verification, then into quantum learning and inference, culminating in cognitive–AI layers and human–system interaction. By integrating physical, computational, cognitive, and ethical dimensions, QAIS frames architectures that not only operate but also learn, reason, and remain verifiable under stress, preparing practitioners to design scalable, interpretable, and resilient systems.

Quantum Stack (book’s conception)

The Quantum Stack is the core conception used throughout this book. It organizes ideas and practice from physical laws to intelligent systems so that each layer builds on the last and informs the next.

• Quantum Physics — nature’s rules at the smallest scales; coherence and entanglement define usable state spaces.
• Quantum Mechanics — operators, transformations, measurement, and teleportation as operational protocols.
• Resilience & Verification — error correction, calibration, and trust layers that stabilize information.
• Quantum Learning & Inference — adaptive, hybrid pipelines where interference and entanglement support reasoning.
• Cognitive–AI Architecture — representational intelligence linking quantum features to decision models.
• Human–System Interaction — interpretability, governance, and ethical oversight that close the loop.

How this QAIS Quantum Stack differs from other stacks

Traditional stacks (e.g., IBM, NIST, IEEE) typically progress from physical hardware and control through error correction, compiler/middleware, and finally algorithms and applications. These models describe how computation is executed but generally stop short of cognition, decision-making, and trust.

• Beyond computation: QAIS treats information as physically embodied, stabilized, and interpreted across layers — not merely processed.
• Embedded resilience: Verification and error correction are positioned as architectural layers that shape behavior, not just low-level tooling.
• Learning & reasoning as layers: QAIS elevates quantum learning and inference to first-class layers where adaptive, hybrid pipelines enable contextual reasoning.
• Cognitive integration: A dedicated cognitive–AI architecture layer links quantum features to decision models, enabling inference to emerge from entanglement and context.
• Human-in-the-loop: The final layer formalizes interpretability, governance, and oversight, ensuring trustworthy, auditable operation.
• System-of-systems view: QAIS reframes the familiar hardware→application continuum as a system-of-systems for intelligent quantum architectures.

Chapter roadmap

• Foundations: Physics of information → state spaces → measurement and interference.
• Operations & protocols: Transformations, teleportation, and resource management.
• Resilience layers: Error models, correction/mitigation, verification, and trust.
• Learning & inference: Hybrid quantum–classical pipelines and adaptive algorithms.
• Cognitive–AI architecture: Representations, reasoning under uncertainty, and decision policies.
• Human–system interaction: Interpretability, governance, and deployment ethics.
• Hands-on labs and case studies: Executable notebooks aligned to each layer and outcome.

Appendices (A–F)

• Appendix A — Case Study · An end-to-end scenario tying layers together.
• Appendix B — Q&A · Targeted review with concise answers.
• Appendix C — Glossary · Unified terms and symbols.
• Appendix D — Quantum-Stack · Stack reference mapping chapters to layers.
• Appendix E — Hands-on Exercises · E.1 (Beginner), E.2 (Intermediate), E.3 (Advanced).
• Appendix F — CRQC–LLM · Hybrid pipelines and evaluation interfaces.