Learn Quantum Computing with Python and Q#: A hands-on approach

Description

Learn Quantum Computing with Python and Q# introduces quantum computing from a practical perspective. Summary Learn Quantum Computing with Python and Q# demystifies quantum computing. Using Python and the new quantum programming language Q#, you’ll build your own quantum simulator and apply quantum programming techniques to real-world examples including cryptography and chemical analysis. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Quantum computers present a radical leap in speed and computing power. Improved scientific simulations and new frontiers in cryptography that are impossible with classical computing may soon be in reach. Microsoft’s Quantum Development Kit and the Q# language give you the tools to experiment with quantum computing without knowing advanced math or theoretical physics. About the book Learn Quantum Computing with Python and Q# introduces quantum computing from a practical perspective. Use Python to build your own quantum simulator and take advantage of Microsoft’s open source tools to fine-tune quantum algorithms. The authors explain complex math and theory through stories, visuals, and games. You’ll learn to apply quantum to real-world applications, such as sending secret messages and solving chemistry problems. What's inside     The underlying mechanics of quantum computers     Simulating qubits in Python     Exploring quantum algorithms with Q#     Applying quantum computing to chemistry, arithmetic, and data About the reader For software developers. No prior experience with quantum computing required. About the author Dr. Sarah Kaiser works at the Unitary Fund, a non-profit organization supporting the quantum open-source ecosystem, and is an expert in building quantum tech in the lab. Dr. Christopher Granade works in the Quantum Systems group at Microsoft, and is an expert in characterizing quantum devices. Table of Contents PART 1 GETTING STARTED WITH QUANTUM 1 Introducing quantum computing 2 Qubits: The building blocks 3 Sharing secrets with quantum key distribution 4 Nonlocal games: Working with multiple qubits 5 Nonlocal games: Implementing a multi-qubit simulator 6 Teleportation and entanglement: Moving quantum data around PART 2 PROGRAMMING QUANTUM ALGORITHMS IN Q# 7 Changing the odds: An introduction to Q# 8 What is a quantum algorithm? 9 Quantum sensing: It’s not just a phase PART 3 APPLIED QUANTUM COMPUTING 10 Solving chemistry problems with quantum computers 11 Searching with quantum computers 12 Arithmetic with quantum computers Learn Quantum Computing with Python and Q# brief contents contents foreword preface acknowledgments about this book Who should read this book How this book is organized: A roadmap About the code liveBook discussion forum Other online resources Going further about the authors about the cover illustration Part 1—Getting started with quantum 1 Introducing quantum computing 1.1 Why does quantum computing matter? 1.2 What is a quantum computer? 1.3 How will we use quantum computers? 1.3.1 What can quantum computers do? 1.3.2 What can’t quantum computers do? 1.4 What is a program? 1.4.1 What is a quantum program? Summary 2 Qubits: The building blocks 2.1 Why do we need random numbers? 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 Qubits: States and operations 2.3.1 State of the qubit 2.3.2 The game of operations 2.3.3 Measuring qubits 2.3.4 Generalizing measurement: Basis independence 2.3.5 Simulating qubits in code 2.4 Programming a working QRNG Summary 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 3.4 Using a secret key to send secret messages Summary 4 Nonlocal games: Working with multiple qubits 4.1 Nonlocal games 4.1.1 What are nonlocal games? 4.1.2 Testing quantum physics: 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 Summary 5 Nonlocal games: Implementing a multi-qubit simulator 5.1 Quantum objects in QuTiP 5.1.1 Upgrading the simulator 5.1.2 Measuring up: How can we measure multiple qubits? 5.2 CHSH: Quantum strategy Summary 6 Teleportation and entanglement: Moving quantum data around 6.1 Moving quantum data 6.1.1 Swapping out the simulator 6.1.2 What other two-qubit gates are there? 6.2 All the single (qubit) rotations 6.2.1 Relating rotations to coordinates: The Pauli operations 6.3 Teleportation Summary Part 1: Conclusion Part 2—Programming quantum algorithms in Q# 7 Changing the odds: An introduction to Q# 7.1 Introducing the Quantum Development Kit 7.2 Functions and operations in Q# 7.2.1 Playing games with quantum random number generators in Q# 7.3 Passing operations as arguments 7.4 Playing Morgana’s game in Q# Summary 8 What is a quantum algorithm? 8.1 Classical and quantum algorithms 8.2 Deutsch–Jozsa algorithm: Moderate improvements for searching 8.2.1 Lady of the (quantum) Lake 8.3 Oracles: Representing classical functions in quantum algorithms 8.3.1 Merlin’s transformations 8.3.2 Generalizing our results 8.4 Simulating the Deutsch–Jozsa algorithm in Q# 8.5 Reflecting on quantum algorithm techniques 8.5.1 Shoes and socks: Applying and undoing quantum operations 8.5.2 Using Hadamard instructions to flip control and target 8.6 Phase kickback: The key to our success Summary 9 Quantum sensing: It’s not just a phase 9.1 Phase estimation: Using useful properties of qubits for measurement 9.1.1 Part and partial application 9.2 User-defined types 9.3 Run, snake, run: Running Q# from Python 9.4 Eigenstates and local phases 9.5 Controlled application: Turning global phases into local phases 9.5.1 Controlling any operation 9.6 Implementing Lancelot’s best strategy for the phase-estimation game Summary Part 2: Conclusion Part 3—Applied quantum computing 10 Solving chemistry problems with quantum computers 10.1 Real chemistry applications for quantum computing 10.2 Many paths lead to quantum mechanics 10.3 Using Hamiltonians to describe how quantum systems evolve in time 10.4 Rotating around arbitrary axes with Pauli operations 10.5 Making the change we want to see in the system 10.6 Going through (very small) changes 10.7 Putting it all together Summary 11 Searching with quantum computers 11.1 Searching unstructured data 11.2 Reflecting about states 11.2.1 Reflection about the all-ones state 11.2.2 Reflection about an arbitrary state 11.3 Implementing Grover’s search algorithm 11.4 Resource estimation Summary 12 Arithmetic with quantum computers 12.1 Factoring quantum computing into security 12.2 Connecting modular math to factoring 12.2.1 Example of factoring with Shor’s algorithm 12.3 Classical algebra and factoring 12.4 Quantum arithmetic 12.4.1 Adding with qubits 12.4.2 Multiplying with qubits in superposition 12.4.3 Modular multiplication in Shor’s algorithm 12.5 Putting it all together Summary Wrapping up Appendix A—Installing required software A.1 Running samples online A.1.1 Using Binder A.1.2 Using GitHub Codespaces A.2 Installing locally using Anaconda A.2.1 Installing Anaconda A.2.2 Installing packages with Anaconda A.3 Installing the Quantum Development Kit A.3.1 Installing the .NET Core SDK A.3.2 Installing the project templates A.3.3 Installing the Visual Studio Code extension A.3.4 Installing IQ# for Jupyter Notebook Appendix B—Glossary and quick reference B.1 Glossary B.2 Dirac notation B.3 Quantum operations B.4 Q# Language B.4.1 Types B.4.2 Q# declarations and statements B.4.3 Q# expressions and operators B.4.4 Q# standard libraries B.4.5 IQ# magic commands Appendix C—Linear algebra refresher C.1 Approaching vectors C.2 Seeing the matrix for ourselves C.2.1 Party with inner products Appendix D—Exploring the Deutsch–Jozsa algorithm by example D.1 Using our skills to try things D.2 Step 1: Preparing the input state for Deutsch–Jozsa D.3 Step 2: Applying the oracle D.3.1 Example 1: The “id” oracle D.3.2 Example 2: The “not” oracle D.3.3 Example 3: The “zero” oracle D.4 Steps 3 and 4: Undo the preparation on the target qubit, and measure index Symbols A B C D E F G H I K L M N O P Q R S T U V W X Y Z