Learn Quantum Computing with Python and Q#

Description

Learn Quantum Computing with Python and Q#: A Hands-on approach MEAP V05 Copyright Welcome Brief contents Chapter 1: Introducing Quantum Computing 1.1   Who This Book is For 1.2   Who This Book is Not For 1.2.1   Textbooks and other resources for learning further 1.3   How this book is organized 1.4   Why does quantum computing matter? 1.4.1   Decisions that are strongly impacted by quantum computing 1.5   What Can Quantum Computers Do? 1.5.1   Some useful quantum algorithms 1.6   What is a Quantum Computer? 1.6.1   How will we use quantum computers? 1.6.2   What can’t quantum computers do? 1.7   What is a Program? 1.7.1   What is a Quantum Program? 1.8   Summary Chapter 2: Qubits: The Building Blocks 2.1   Why do we need random numbers? 2.1.1   Things some humans like to use randomness for 2.1.2  Statements about probability 2.1.3   Quantum random number generator algorithm 2.2   What are Classical Bits? 2.2.1   What Can We Do With Classical Bits? 2.2.2   Abstractions are our friend 2.3   Approaching Vectors 2.3.1  Examples of vectors 2.4   Seeing the Matrix for Ourselves 2.4.1   Examples of linear functions 2.4.2  Party with inner products 2.5   Qubits: States and Operations 2.5.1   State of the qubit 2.5.2   The game of Operations 2.5.3   Measuring Qubits 2.5.4   Generalizing measurement: basis independence 2.5.5   Simulating qubits in code 2.6   Programming a Working QRNG 2.6.1   QRNG 2.6.2   Quantum device interface requirements. 2.6.3   Qubit interface requirements 2.7   Summary Chapter 3: Sharing Secrets with Quantum Key Distribution 3.1   All’s Fair in Love and Encryption 3.1.1   Quantum NOT operations 3.1.2   Sharing classical bits with qubits 3.2   A tale of two bases 3.3   Quantum Key Distribution: BB84 3.3.1   Steps of the BB84 protocol: 3.4   Using our secret key to send secret messages 3.5   Summary Chapter 4: Nonlocal Games: Working with multiple qubits 4.1   Nonlocal Games 4.1.1   What are nonlocal games? 4.1.2   Testing quantum physics itself: The CHSH game 4.1.3  Classical strategy 4.2   Working with multiple qubit states 4.2.1   Registers 4.2.2   Why is it hard to simulate quantum computers? 4.2.3   Tensor products for state preparation 4.2.4   Tensor products for qubit operations on registers 4.3   QuTiP of the Iceberg 4.3.1   Quantum objects in QuTiP 4.3.2   Upgrading the simulator 4.3.3   Measuring up: How can we measure multiple qubits? 4.4   CHSH: Quantum strategy 4.5   Summary Chapter 5: Teleportation and entanglement: Moving quantum data around 5.1   Moving quantum data 5.1.1   Swapping out our simulator 5.1.2   What other two-qubit gates are there? 5.2   All the single (qubit) rotations 5.2.1   Relating rotations to coordinates: The Pauli operations 5.3   Teleportation 5.4   Part I: Conclusion Chapter 6: Changing the odds: An introduction to Q# 6.1   Introducing the Quantum Development Kit 6.2   Functions and Operations in Q# 6.3   Passing Operations as Arguments 6.4   Playing Morgana’s Game in Q# 6.5   Summary Chapter 7: What is a Quantum Algorithm? 7.1   Classical and quantum algorithms 7.2   Deutsch–Jozsa Algorithm: moderate improvements for searching 7.2.1   Lady of the (Quantum) Lake 7.2.2   Oracles: representing classical functions in quantum algorithms 7.3   Simulating the Deutsch–Jozsa algorithm in Q# 7.4   Exploring the Deutsch–Jozsa Algorithm by example 7.4.1   Step 1. Preparing the input state for Deutsch–Jozsa 7.4.2   Step 2. Applying the oracle 7.4.3   Steps 3 and 4. Undo the preparation on the target qubit and measure. 7.5   Reflecting back 7.5.1   Shoes and socks: applying and undoing quantum operations 7.5.2   Using Hadamard instructions to flip control and target 7.5.3   Phase Kickback 7.6   Summary Chapter 8: Quantum sensing: It’s not just a phase 8.1   Phase estimation: leveraging useful properties of qubits for measurement 8.1.1   Part and Partial Application 8.2   User-Defined Types 8.3   Run, snake, run: Running Q# from Python 8.4   Eigenstates and Local Phases 8.5   Controlled application: Turning global phases into local phases 8.5.1   Controlling any operation 8.6   Implementing Lancelot’s best strategy for the phase estimation game 8.7   Summary Appendix A: Installing Required Software A.1  Installing a Python Environment A.1.1  Installing Anaconda A.1.2  Installing Python packages with Anaconda: QuTiP A.2  Installing the Quantum Development Kit A.2.1  Installing the .NET Core SDK A.2.2  Installing the Project Templates A.2.3  Installing the Visual Studio Code extension A.2.4  Installing IQ# for Jupyter Notebook A.2.5  Installing the qsharp Python package