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Description
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.
The Most Important Step to Understand Quantum Computing.- First Impression.- Basis, Basis Vectors, and Inner Product.- Orthonormal Basis, Bra-Ket Notation, and Measurement.- Changing Basis, Uncertainty Principle, and Bra-ket Operations.- Observables, Operators, Eigenvectors, and Eigenvalues.- Pauli Spin Matrices, Adjoint Matrix, and Hermitian Matrix.- Operator Rules, Real Eigenvalues, and Projection Operator.- Eigenvalue and Matrix Diagonalization; Unitary Matrix.- Unitary Transformation, Completeness, and Construction of Operator.- Hilbert Space, Tensor Product, and Multi-Qubit.- Tensor Product of Operators, Partial Measurement, and Matrix Representation in a Given Basis.- Quantum Register and Data Processing, Entanglement and the Bell States.- Concepts Review, Density Matrix, and Entanglement Entropy.- Quantum Gate Introduction; NOT and C-NOT Gates.- SWAP, Phase Shift and CC-NOT (Toffoli) Gates.- Walsh-Hadamard Gate and its Properties.- 13 more chapters.
Hiu Yung Wong is an Assistant Professor and Silicon Valley AMDT Endowed Chair in Electrical Engineering, San Jose State University. He received his Ph.D. degree in Electrical Engineering and Computer Science from the University of California, Berkeley in 2006. From 2006 to 2009, he worked as a Technology Integration Engineer in Spansion. From 2009 to 2018, he was a TCAD Senior Staff Application Engineer in Synopsys, during which he received Synopsys Excellence Award in 2010. In 2021, he received the NSF CAREER award and the Newnan Brothers Award for Faculty Excellence.
His research interests include the applications of machine learning in simulation and manufacturing, cryogenic electronics, quantum computing, reliability simulations, wide bandgap devices (such as GaN, SiC, Ga2O3, and diamond) simulations, novel semiconductor devices design, and Design Technology Co-Optimization (DTCO). His work has produced 80 papers and 10 issued patents.
