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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.
- Cover1
- Copyright2
- How to read this book3
- Introduction4
- Chapter 1: What Quantum Optimization Is5
- Chapter 2: Classical Optimization Foundations6
- Chapter 3: Linear Algebra for Quantum Thinking7
- Chapter 4: Probability, Information, and Measurement8
- Chapter 5: Qubits from Scratch9
- Chapter 6: Multiple Qubits and Entanglement10
- Chapter 7: Quantum Gates and Circuits11
- Chapter 8: Hamiltonians and Energy Landscapes12
- Chapter 9: From Optimization Problems to QUBO and Ising Models13
- Chapter 10: Complexity, Hardness, and What Quantum Computers Can Promise14
- Chapter 11: Classical Baselines You Must Know15
- Chapter 12: Adiabatic Quantum Computing16
- Chapter 13: Quantum Annealing17
- Chapter 14: Gate-Based Quantum Optimization18
- Chapter 15: The Quantum Approximate Optimization Algorithm19
- Chapter 16: Variational Quantum Algorithms20
- Chapter 17: Grover Search and Amplitude Amplification21
- Chapter 18: Quantum Walks and Advanced Optimization Ideas22
- Chapter 19: Noise, Error, and NISQ Reality23
- Chapter 20: Modeling Real Problems24
- Chapter 21: Implementing Quantum Optimization in Software25
- Chapter 22: Benchmarking and Scientific Evaluation26
- Chapter 23: Case Studies from Start to Finish27
- Chapter 24: The Research Frontier and Your Next Steps28
- Conclusion29