Quantum Algorithms and Applications
Volume 2 (contains Parts III & IV + Appendices, b/w)
Unlock the Potential of Quantum Algorithms
This book presents a progressive scaffolding approach to quantum algorithms and applications, guiding readers from core primitives to practical workflows in simulation, optimization, quantum machine learning, and linear systems, with an emphasis on mastering durable concepts and principles.
Features
Unlock the Potential of Quantum Algorithms
This book presents a progressive scaffolding approach to quantum algorithms and applications, guiding readers from core primitives to practical workflows in simulation, optimization, quantum machine learning, and linear systems, with an emphasis on mastering durable concepts and principles.
Features
- A systematic, student-friendly route through quantum algorithms.
- Comprehensive coverage, from core primitives to real applications.
- Up to date: QSVT, SQD, QML, classical shadows, differential-equation solvers, and the quest for quantum advantage.
- Dr. Peter Y. Lee (Ph.D., Princeton University) - Expert in quantum nanostructures with extensive experience in teaching and academic program leadership.
- Dr. Ran Cheng (Ph.D., University of Texas at Austin) - Specialist in condensed matter theory and an award-winning physicist.
- Dr. Huiwen Ji (Ph.D., Princeton University) - Accomplished researcher with a solid background in quantum chemistry and numerous awards.
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Quantum Algorithms and Applications—
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Description
Volume 2 (contains Parts III & IV + Appendices, b/w)
Unlock the Potential of Quantum Algorithms
This book presents a progressive scaffolding approach to quantum algorithms and applications, guiding readers from core primitives to practical workflows in simulation, optimization, quantum machine learning, and linear systems, with an emphasis on mastering durable concepts and principles.
Features
Unlock the Potential of Quantum Algorithms
This book presents a progressive scaffolding approach to quantum algorithms and applications, guiding readers from core primitives to practical workflows in simulation, optimization, quantum machine learning, and linear systems, with an emphasis on mastering durable concepts and principles.
Features
- A systematic, student-friendly route through quantum algorithms.
- Comprehensive coverage, from core primitives to real applications.
- Up to date: QSVT, SQD, QML, classical shadows, differential-equation solvers, and the quest for quantum advantage.
- Dr. Peter Y. Lee (Ph.D., Princeton University) - Expert in quantum nanostructures with extensive experience in teaching and academic program leadership.
- Dr. Ran Cheng (Ph.D., University of Texas at Austin) - Specialist in condensed matter theory and an award-winning physicist.
- Dr. Huiwen Ji (Ph.D., Princeton University) - Accomplished researcher with a solid background in quantum chemistry and numerous awards.
