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Table of contents
Quantum Optimization
From classical foundations and historical milestones to modern algorithms, hardware, and frontier research
Read each section in order. Every title can be opened as a TheoryTrace document.
- Cover1
- Copyright2
- How to read this book3
- Introduction4
- Chapter 1: The Optimization Landscape5
- Chapter 2: Quantum Computing Foundations for Optimization6
- Chapter 3: Classical Algorithms Every Quantum Optimizer Must Know7
- Chapter 4: Complexity, Oracles, and Limits of Quantum Speedup8
- Chapter 5: From Optimization Problems to Hamiltonians9
- Chapter 6: Grover Search and Amplitude Amplification10
- Chapter 7: Quantum Walk Algorithms for Search and Optimization11
- Chapter 8: Adiabatic Quantum Computing12
- Chapter 9: Quantum Annealing13
- Chapter 10: Hardware for Quantum Annealing and Ising Machines14
- Chapter 11: The Quantum Approximate Optimization Algorithm15
- Chapter 12: Variational Quantum Algorithms Beyond QAOA16
- Chapter 13: Continuous Optimization and Quantum Linear Algebra17
- Chapter 14: Quantum Machine Learning for Optimization18
- Chapter 15: Error, Noise, and Fault Tolerance in Quantum Optimization19
- Chapter 16: Benchmarking and Claims of Quantum Advantage20
- Chapter 17: Landmark Papers and Historical Development21
- Chapter 18: Applications in Science, Engineering, and Industry22
- Chapter 19: Frontier Research Directions23
- Chapter 20: Designing a Quantum Optimization Research Project24
- Conclusion25