Quantum AI Systems — Appendix E Hands-On Labs
This page lists the complete set of Appendix E companion laboratories for Quantum AI Systems: Theory, Architecture, and Applications.
All labs are designed for execution in Google Colab or locally with Python 3.10+ and Qiskit, but access to runnable labs is managed from the Labs Hub.
Repository Structure
qais-labs/
│
├─ Beginner_Labs/ ← Labs E.1.1 – E.1.11 (mapped to Chapters 1–11)
│ ├─ notebooks/ ← Jupyter / Colab notebooks (.ipynb)
│ └─ AppendixE_Beginner_&_Advance_Labs.pdf
│
├─ Intermediate_Labs/ ← Labs E.2.1 – E.2.6 (mapped to Chapters 5, 8, 9, 10)
│ └─ notebooks/
│
├─ Advanced_Labs/ ← Labs E.3.1 – E.3.8 (mapped to Chapters 1–13)
│ └─ notebooks/
│
└─ README.md
Getting Started
Use the Labs Hub to access available lab tracks and secure entry points for gated content. Public preview materials, book-access pathways, and instructor-only routes are managed there.
Beginner Labs (Part 1 / E.1)
| Lab # | Chapter | Title |
|---|---|---|
| Lab 1 | Ch. 1 — Foundations of QAIS | Superposition — Probability Distribution |
| Lab 2 | Ch. 1 — Foundations of QAIS | Bell State (Entanglement) — Correlated Outcomes |
| Lab 3 | Ch. 2 — Operations & Scientific Framework | Angle Encoding & Statevectors — Bloch Sphere |
| Lab 4 | Ch. 4 — Encoding Classical Data | Quantum Kernel SVM vs Logistic Regression |
| Lab 5 | Ch. 2 — Operations & Scientific Framework | Depth & Noise Sensitivity — Probability Decay |
| Lab 6 | Ch. 10 — Quantum Communication | Quantum Teleportation (Protocol Demo) |
| Lab 7 | Ch. 8 — Optimization & Control | ZZ Expectation Scan (QAOA-style Observable) |
| Lab 8 | Ch. 10 — Quantum Communication | BB84 QKD — Error Rate Comparison |
| Lab 9 | Ch. 9 — Quantum Encoding & Info Metrics | Fidelity & Trace Distance |
| Lab 10 | Ch. 10 — Quantum Communication | Entanglement-Assisted Tamper Check |
| Lab 11 | Ch. 8 — Optimization & Control | Simple VQE-Style Minimization |
Intermediate Labs (Part 2 / E.2)
| Lab # | Chapter | Title |
|---|---|---|
| E.2.1 | Ch. 5 — Quantum Reasoning and Decision Architectures | Hybrid / ML Comparison — Quantum Kernel SVM vs Logistic Regression |
| E.2.2 | Ch. 8 — Optimization and Control in Quantum AI Systems | Quantum Control & Parameter Sweeps — ⟨ZZ⟩ Scan |
| E.2.3 | Ch. 10 — Quantum Communication for Distributed AI Systems | Quantum Communication & Encoding — BB84 QKD |
| E.2.4 | Ch. 9 — Quantum Encoding & Information Metrics | Fidelity & Trace Distance — Robustness Metrics |
| E.2.5 | Ch. 10 — Quantum Communication for Distributed AI Systems | Trust / Tamper Verification — Entanglement Tamper Check |
| E.2.6 | Ch. 8 — Optimization and Control in Quantum AI Systems | Quantum Optimization under Noise — VQE Toy Minimization |
Advanced Labs (Part 3 / E.3)
| Lab # | Chapter | Title |
|---|---|---|
| Adv. 1 | Ch. 2 — Operations & Scientific Framework | Bloch Trajectories Under Composite Gates |
| Adv. 2 | Ch. 1 — Foundations of QAIS | CHSH Correlation Sweep |
| Adv. 3 | Ch. 7 — Quantum Machine Learning Architectures | Tiny VQC vs Logistic Regression |
| Adv. 4 | Ch. 12 — Capstone QAIS Integration | Realizing Applied Quantum AI Systems → QALIS and CRQC-LLM |
| Adv. 5 | Ch. 6 — Quantum Advantage in QAIS | Grover Success vs Noise |
| Adv. 6 | Ch. 9 — Quantum Encoding & Info Metrics | Density Matrix Simulation |
| Adv. 7 | Ch. 13 — Extended Case Studies | Case Study Stress-Test |
| Adv. 8 | Ch. 6 — Quantum Advantage in Quantum AI Systems | Shor’s Algorithm — Period Finding via Quantum Fourier Transform |
Expected Results Guide
| Lab # | Title | Expected Results |
|---|---|---|
| Lab 1 | Superposition | Balanced counts for |0⟩ and |1⟩, confirming superposition. |
| Lab 2 | Bell State | Only 00 and 11 appear, showing entanglement. |
| Lab 3 | Angle Encoding | Bloch vectors rotate with input features; amplitudes match encodings. |
| Lab 4 | Quantum Kernel SVM | Quantum kernel shows nonlinear structure; logistic regression gives linear baseline. |
| Lab 5 | Depth & Noise | Probability decays with depth under noise; flat under ideal simulation. |
| Lab 6 | Teleportation | Destination qubit matches original state after corrections. |
| Lab 7 | ZZ Expectation Scan | ⟨ZZ⟩ oscillates smoothly with scan angle. |
| Lab 8 | BB84 QKD | QBER remains low without Eve and rises sharply when Eve is present. |
| Lab 9 | Fidelity & Trace Distance | Fidelity decreases while trace distance rises under perturbation. |
| Lab 10 | Tamper Check | Untampered runs show only 00/11; tampered runs introduce 01/10. |
| Lab 11 | VQE | Energy curve decreases and converges toward the minimum. |
| E.2.1 | Hybrid / ML Comparison | Quantum kernel heatmap shows nonlinear structure; logistic regression provides a linear baseline. |
| E.2.2 | ⟨ZZ⟩ Scan | ⟨ZZ⟩ varies smoothly and periodically with scan angle, revealing interference structure. |
| E.2.3 | BB84 QKD | QBER remains low without Eve and rises sharply with interception. |
| E.2.4 | Fidelity & Trace Distance | Fidelity decreases and trace distance increases with stronger perturbation. |
| E.2.5 | Tamper Verification | Untampered runs show 00/11 only; tampering introduces 01/10. |
| E.2.6 | VQE Toy Minimization | Energy decreases with iterations and converges near the minimum. |
| Adv. 1 | Bloch Trajectories | Distinct Bloch sphere paths confirm that gate order matters. |
| Adv. 2 | CHSH Sweep | S-value exceeds 2 and approaches the Tsirelson bound (~2.828). |
| Adv. 3 | Tiny VQC | Nonlinear decision boundary richer than logistic regression. |
| Adv. 4 | Hybrid Pipeline | High accuracy (~0.95–0.97) with structured kernel heatmap. |
| Adv. 5 | Grover vs Noise | Success probability decays as noise increases. |
| Adv. 6 | Density Matrix | Mixed states contract the Bloch sphere and reduce coherence. |
| Adv. 7 | Case Study Stress-Test | Stress tests reveal resilience challenges in QALIS vs. CRQC–LLM. |
| Adv. 8 | Shor’s Algorithm | QFT measurement histograms reveal periodic frequency peaks. Continued-fraction decoding identifies candidate periods, illustrating the order-finding mechanism underlying Shor’s factoring algorithm. |
Citation
If you use these labs in teaching, research, or derivative works, please cite as:
Wilson, J. (2025). Quantum AI Systems: Appendix E Hands-On Labs. Companion Repository.
Requirements
- Python 3.10+
- Qiskit ≥ 1.0
- NumPy
- Matplotlib
- SciPy
- scikit-learn
- Optional: PennyLane for extensions
Notes
All labs are Colab-ready — no local installation is required for supported runs.
For local runs, install dependencies with:
pip install qiskit numpy matplotlib scipy scikit-learn pennylane