Table of contents

Quantum Optimization from First Principles

A complete learning path from zero quantum background to confident modeling, algorithms, and implementation

Read each section in order. Every title can be opened as a TheoryTrace document.

• Cover
• Copyright
• How to read this book
• Introduction
• Chapter 1: What Quantum Optimization Is
• Chapter 2: Classical Optimization Foundations
• Chapter 3: Linear Algebra for Quantum Thinking
• Chapter 4: Probability, Information, and Measurement
• Chapter 5: Qubits from Scratch
• Chapter 6: Multiple Qubits and Entanglement
• Chapter 7: Quantum Gates and Circuits
• Chapter 8: Hamiltonians and Energy Landscapes
• Chapter 9: From Optimization Problems to QUBO and Ising Models
• Chapter 10: Complexity, Hardness, and What Quantum Computers Can Promise
• Chapter 11: Classical Baselines You Must Know
• Chapter 12: Adiabatic Quantum Computing
• Chapter 13: Quantum Annealing
• Chapter 14: Gate-Based Quantum Optimization
• Chapter 15: The Quantum Approximate Optimization Algorithm
• Chapter 16: Variational Quantum Algorithms
• Chapter 17: Grover Search and Amplitude Amplification
• Chapter 18: Quantum Walks and Advanced Optimization Ideas
• Chapter 19: Noise, Error, and NISQ Reality
• Chapter 20: Modeling Real Problems
• Chapter 21: Implementing Quantum Optimization in Software
• Chapter 22: Benchmarking and Scientific Evaluation
• Chapter 23: Case Studies from Start to Finish
• Chapter 24: The Research Frontier and Your Next Steps
• Conclusion