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Learn Quantum Computing with Python and Q#: A hands-on approach [Pehme köide]

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  • Formaat: Paperback / softback, 300 pages, kõrgus x laius x paksus: 234x186x24 mm, kaal: 710 g
  • Ilmumisaeg: 15-Jul-2021
  • Kirjastus: Manning Publications
  • ISBN-10: 1617296139
  • ISBN-13: 9781617296130
Teised raamatud teemal:
Learn Quantum Computing with Python and Q#: A hands-on approachSuurem pilt
  • Formaat: Paperback / softback, 300 pages, kõrgus x laius x paksus: 234x186x24 mm, kaal: 710 g
  • Ilmumisaeg: 15-Jul-2021
  • Kirjastus: Manning Publications
  • ISBN-10: 1617296139
  • ISBN-13: 9781617296130
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781617296130.html
Märksõnad:
Learn Quantum Computing with Python and Q# demystifies quantum computing. Using Python and the new quantum programming language Q#, youll learn QC fundamentals as you apply quantum programming techniques to real-world examples including cryptography and chemical analysis.

 

Learn Quantum Computing with Python and Q# builds your understanding of quantum computers, using Microsofts Quantum Development Kit to abstract away the mathematical complexities. Youll learn QC basics as you create your own quantum simulator in Python, then move on to using the QDK and the new Q# language for writing and running algorithms very different to those found in classical computing.

 

Key Features

·   The underlying mechanics of how quantum computers work

·   How to simulate qubits in Python

·   Q# and the Microsoft Quantum Developer Kit

·   How to apply quantum algorithms to real-world examples

 

For readers with basic programming skills and some experience of linear algebra, calculus and complex numbers.

 

About the technology

Quantum computing is the next step in computing power and scalability, with the potential to impact everything from data science to information security. Using qubits, the fundamental unit of quantum information, quantum computers can solve problems beyond the scale of classical computing. Software packages like Microsoft's Quantum Development Kit and the Q# language are now emerging to give programmers a quick path to exploring quantum development for the first time.

 

Christopher Granade completed his PhD in physics (quantum information) at the University of Waterloos Institute for Quantum Computing, and now works in the Quantum Architectures and Computation (QuArC) group at Microsoft. He works in developing the standard libraries for Q# and is an expert in the statistical characterization of quantum devices from classical data. Previously, Christopher helped Scott Aaronson prepare lectures into his recent book, Quantum Computing Since Democritus.

 

Sarah Kaiser completed her PhD in physics (quantum information) at the University of Waterloos Institute for Quantum Computing. She has spent much of her career developing new quantum hardware in the lab, from satellites to hacking quantum cryptography hardware. Communicating what is so exciting about quantum is her passion, and she loves finding new demos and tools to help enable the quantum community to grow. When not at the keyboard, she loves kayaking and writing books about engineering for kids.
Foreword xv
preface xvii
acknowledgments xix
About this book xxi
About the Authors xxv
About the Cover Illustration xxvi
Part 1 Getting started with quantum
1(130)
1 Introducing quantum computing
3(14)
1.1 Why does quantum computing matter?
4(1)
1.2 What is a quantum computer?
5(3)
1.3 How will we use quantum computers?
8(5)
"What can quantum computers do?
10(1)
What can't quantum computers do?
11(2)
1.4 What is a program?
13(4)
What is a quantum program?
14(3)
2 Qubits: The building blocks
17(37)
2.1 Why do we need random numbers?
19(3)
2.2 What are classical bits?
22(5)
What can we do with classical bits'?
23(3)
Abstractions are our friend
26(1)
2.3 Qubits: States and operations
27(19)
State of the qubit
28(2)
The game of operations
30(4)
Measuring qubits
34(4)
Generalizing measurement: Basis independence
38(3)
Simulating qubits in code
41(5)
2.4 Programming a working QRNG
46(8)
3 Sharing secrets with quantum key distribution
54(21)
3.1 All's fair in love and encryption
54(9)
Quantum NOT operations
58(4)
Sharing classical bits with qubits
62(1)
3.2 A tale of two bases
63(3)
3.3 Quantum key distribution: BB84
66(5)
3.4 Using a secret key to send secret messages
71(4)
4 Nonlocal games: Working with multiple qubits
75(15)
4.1 Nonlocal games
76(5)
What are nonlocal games?
76(1)
Testing quantum physics: The CHSH game
76(4)
Classical strategy
80(1)
4.2 Working with multiple qubit states
81(9)
Registers
81(2)
Why is it hard to simulate quantum computers?
83(2)
Tensor products for state preparation
85(1)
Tensor products for qubit operations on registers
86(4)
5 Nonlocal games: Implementing a multi-qubit simulator
90(18)
5.1 Quantum objects in QuTiP
91(12)
Upgrading the simulator
96(3)
Measuring up: How can we measure multiple qubits?
99(4)
5.2 CHSH: Quantum strategy
103(5)
6 Teleportation and entanglement: Moving quantum data around
108(23)
6.1 Moving quantum data
109(8)
Swapping out the simulator
112(3)
What other two-qubit gates are there?
115(2)
6.2 All the single (qubit) rotations
117(9)
Relating rotations to coordinates: The Pauli operations
119(7)
6.3 Teleportation
126(5)
Part 2 Programming quantum algorithms in Q#
131(86)
7 Changing the odds: An introduction to Q#
133(19)
7.1 Introducing the Quantum Development Kit
134(3)
7.2 Functions and operations in Q#
137(6)
Playing games with quantum random number generators in Q#
138(5)
7.3 Passing operations as arguments
143(6)
7.4 Playing Morgana's game in Q#
149(3)
8 What is a quantum algorithm?
152(33)
8.1 Classical and quantum algorithms
153(3)
8.2 Deutsch--Jozsa algorithm: Moderate improvements for searching
156(5)
Lady of the (quantum) Lake
156(5)
8.3 Oracles: Representing classical functions in quantum algorithms
161(9)
Merlin's transformations
162(3)
Generalizing our results
165(5)
8.4 Simulating the Deutsch--Jozsa algorithm in Q#
170(4)
8.5 Reflecting on quantum algorithm techniques
174(6)
Shoes and socks: Applying and undoing quantum operations
175(3)
Using Hadamard instructions to flip control and target
178(2)
8.6 Phase kickback: The key to our success
180(5)
9 Quantum sensing: It's not just a phase
185(32)
9.1 Phase estimation: Using useful properties of qubits for measurement
186(5)
Part and partial application
186(5)
9.2 User-defined types
191(6)
9.3 Run, snake, run: Running Q# from Python
197(5)
9.4 Eigenstates and local phases
202(4)
9.5 Controlled application: Turning global phases into local phases
206(6)
Controlling any operation
210(2)
9.6 Implementing Lancelot's best strategy for the phase-estimation game
212(3)
9.7 Summary
215(1)
9.8 Part 2: Conclusion
215(2)
Part 3 Applied quantum computing
217(90)
10 Solving chemistry problems with quantum computers
219(30)
10.1 Real chemistry applications for quantum computing
220(2)
10.2 Many paths lead to quantum mechanics
222(3)
10.3 Using Hamiltonians to describe how quantum systems evolve in time
225(4)
10.4 Rotating around arbitrary axes with Pauli operations
229(8)
10.5 Making the change we want to see in the system
237(2)
10.6 Going through (very small) changes
239(3)
10.7 Putting it all together
242(7)
11 Searching with quantum computers
249(29)
11.1 Searching unstructured data
250(6)
11.2 Reflecting about states
256(8)
Reflection about the all-ones state
257(1)
Reflection about an arbitrary state
258(6)
11.3 Implementing Graver's search algorithm
264(7)
11.4 Resource estimation
271(7)
12 Arithmetic with quantum computers
278(29)
12.1 Factoring quantum computing into security
279(4)
12.2 Connecting modular math to factoring
283(5)
Example of factoring with Shor's algorithm
287(1)
12.3 Classical algebra and factoring
288(3)
12.4 Quantum arithmetic
291(8)
Adding with qubits
292(1)
Multiplying with qubits in superposition
293(3)
Modular multiplication in Shor's algorithm
296(3)
12.5 Putting it all together
299(8)
Appendix A Installing required software 307(7)
Appendix B Glossary and quick reference 314(13)
Appendix C Linear algebra refresher 327(14)
Appendix D Exploring the Deutsch--Jozsa algorithm by example 341(10)
index 351
Christopher Granade completed his PhD in physics (quantum information) at the University of Waterloos Institute for Quantum Computing, and now works in the Quantum Architectures and Computation (QuArC) group at Microsoft. He works in developing the standard libraries for Q# and is an expert in the statistical characterization of quantum devices from classical data. Previously, Christopher helped Scott Aaronson prepare lectures into his recent book, Quantum Computing Since Democritus.

 

Sarah Kaiser completed her PhD in physics (quantum information) at the University of Waterloos Institute for Quantum Computing. She has spent much of her career developing new quantum hardware in the lab, from satellites to hacking quantum cryptography hardware. Communicating what is so exciting about quantum is her passion, and she loves finding new demos and tools to help enable the quantum community to grow. When not at the keyboard, she loves kayaking and writing books about engineering for kids.