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Part I. Linear Algebra for Quantum Computing
1. The Most Important Step to Understand Quantum Computing
2. First Impression
3. Basis, Basis Vectors, and Inner Product
4. Orthonormal Basis, Bra–Ket Notation, and Measurement
5. Changing Basis, Uncertainty Principle, and Bra–Ket Operations
6. Observables, Operators, Eigenvectors, and Eigenvalues
7. Pauli Spin Matrices, Adjoint Matrix, and Hermitian Matrix
8. Operator Rules, Real Eigenvalues, and Projection Operator
9. Eigenvalue, Matrix Diagonalization and Unitary Matrix
10. Unitary Transformation, Completeness, and Construction of Operator
11. Hilbert Space, Tensor Product, and Multi-Qubit
12. Tensor Product of Operators, Partial Measurement, and Matrix Representation in a Given Basis
Part II. Quantum Computing: Gates and Algorithms
13. Quantum Register and Data Processing, Entanglement, the Bell States, and EPR Paradox
14. Concepts Review, Density Matrix, and Entanglement Entropy
15. Quantum Gate Introduction: NOT and CNOT Gates
16. SWAP, Phase Shift, and CCNOT (Toffoli) Gates
17. Walsh–Hadamard Gate and Its Properties
18. Two Quantum Circuit Examples
19. No-Cloning Theorem and Quantum Teleportation I
20. Quantum Teleportation II and Entanglement Swapping
21. Deutsch Algorithm
22. Quantum Oracles and Construction of Quantum Gate Matrices
23. Grover’s Algorithm: I
24. Grover’s Algorithm: II
25. Quantum Fourier Transform I
26. Quantum Fourier Transform II
27. Bloch Sphere and Single-Qubit Arbitrary Unitary Gate
28. Quantum Phase Estimation
29. Shor’s Algorithm
30. The Last But Not the Least

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Introduction to quantum computing : from a layperson to a programmer in 30 steps 이용현황 표 - 등록번호, 청구기호, 권별정보, 자료실, 이용여부로 구성 되어있습니다.
등록번호 청구기호 권별정보 자료실 이용여부
0003003205 006.3843 -A23-3 서울관 서고(열람신청 후 1층 대출대) 이용가능

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알라딘제공
This textbook introduces quantum computing to readers who do not have much background in linear algebra. The author targets undergraduate and master students, as well as non-CS and non-EE students who are willing to spend about 60 -90 hours seriously learning quantum computing. Readers will be able to write their program to simulate quantum computing algorithms and run on real quantum computers on IBM-Q. Moreover, unlike the books that only give superficial, “hand-waving” explanations, this book uses exact formalism so readers can continue to pursue more advanced topics based on what they learn from this book.
  • Encourages students to embrace uncertainty over the daily classical experience, when encountering quantum phenomena;
  • Uses narrative to start each section with analogies that help students to grasp the critical concept quickly;
  • Uses numerical substitutions, accompanied by Python programming and IBM-Q quantum computer programming, as examples in teaching all critical concepts.


New feature

This textbook introduces quantum computing to readers who do not have much background in linear algebra. The author targets undergraduate and master students, as well as non-CS and non-EE students who are willing to spend about 60 -90 hours seriously learning quantum computing. Readers will be able to write their program to simulate quantum computing algorithms and run on real quantum computers on IBM-Q. Moreover, unlike the books that only give superficial, “hand-waving” explanations, this book uses exact formalism so readers can continue to pursue more advanced topics based on what they learn from this book.
  • Encourages students to embrace uncertainty over the daily classical experience, when encountering quantum phenomena;
  • Uses narrative to start each section with analogies that help students to grasp the critical concept quickly;
  • Uses numerical substitutions, accompanied by Python programming and IBM-Q quantum computer programming, as examples in teaching all critical concepts.


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