Quantum Computing Experimentation with Amazon Braket

Cover
Title Page
Copyright and Credits
Dedication
Foreword
Reviews
Contributors
Table of Contents
Preface
Section 1: Getting Started with Amazon Braket
Chapter 1: Setting Up Amazon Braket
	Technical requirements
	The overall Amazon Braket landscape
	Creating an AWS account
	Starting the Amazon Braket service
	Configuring the AWS S3 service
	Working with the notebook service
		Starting the notebook service in Braket
		Using notebooks and examples in Braket
		Shutting down the notebook service in Braket
	Remote access to Amazon Braket using Boto3
	Validating if SDK installation is working
	Signing in to the AWS account
	Summary
	Further reading
Chapter 2: Braket Devices Explained
	Annealing-based quantum devices
		Introducing D-Wave quantum devices
	Gate-based quantum devices
		An overview of IonQ\'s quantum device
		Introducing Rigetti quantum devices
		Oxford Quantum Circuits
	Amazon Braket simulators
		Simulators executing on a local device
		Simulators executing on Amazon resources
	Summary
	Further reading
Chapter 3: User Setup, Tasks, and Understanding Device Costs
	Technical requirements
	Setting up user groups and users
		Creating a user group
		Setting up users
	Creating a policy for users
	Running test code
	Finding your tasks and results
	Understanding device costs and billing
		QPU versus simulator devices
		Viewing your charges
	Summary
	Further reading
Chapter 4: Writing Your First Amazon Braket Code Sample
	Technical requirements
	Finding active devices
	Assigning a device
		Using the local simulator
		Using Amazon simulators or quantum devices
	Estimating the cost of the device
	Creating a simple quantum circuit
		Putting together a simple quantum circuit example
		Representing a binary value using a quantum circuit
		Running a circuit on the Amazon simulator
		Actual cost of using the Amazon simulator
	Summary
	Further reading
	Concluding Section 1
Section 2: Building Blocks for Real-World Use Cases
Chapter 5: Using a Quantum Annealer – Developing a QUBO Function and Applying Constraints
	Technical requirement
	Solving optimization problems
		Simulated annealing
		Quantum annealing
	Quadratic Unconstrained Binary Optimization (QUBO) problems
		A simple conceptual model for D-Wave
		A QUBO example using three variables and ExactSolver()
		Running the three-variable problem on D-Wave annealer
	A party optimization example
	A team selection example
		A simple process for solving problems using D-Wave
		Reviewing data
		Representing the problem in graph form
		Summarizing the problem
		The traditional formulation
		A tool to visualize the energy landscape
		A simple penalty function to implement the constraint
		Running the problem on classical and quantum solvers
	Summary
	Further reading
Chapter 6: Using Gate-Based Quantum Computers – Qubits and Quantum Circuits
	Technical requirements
	What is a quantum circuit?
	Understanding the basics of a qubit
		Using matrix mathematics
		Using matrix mathematics to represent single-qubit gates
		Using quantum gates in a quantum circuit
	Single-qubit gate rotation example – the Bloch Clock
		Representing the hour of the day using θ
		Representing the minutes and seconds using φ
	Building multiple qubit quantum circuits
		Three-qubit circuit example
	Example inspired by the Google Supremacy experiment
		The actual Google experiment
		Circuit implementation on Amazon Braket
		Execution results for a single 7x2 circuit
	Summary
	Further reading
Chapter 7: Using Gate Quantum Computers – Basic Quantum Algorithms
	Technical requirements
	What is a quantum Oracle?
	Observing the effect of amplitude amplification
		Grover\'s operator using unitary matrices
		Grover\'s search algorithm using quantum circuits
		Repetitions of the Grover diffuser operator
		Using Grover\'s algorithm in searches
	Working with phases
		Translating between the Computational basis and the Fourier basis
		Adding phase information to a qubit
		How the phase adder circuit is used in quantum circuits
		Using Quantum Fourier Transform and its inverse
		Adding numbers using the phase adder
	Summary
	Further reading
Chapter 8: Using Hybrid Algorithms – Optimization Using Gate-Based Quantum Computers
	Technical requirements
	Representing a binary quadratic function using a phase adder
	Introduction to QAOA concepts
	Experimentally validating QAOA concepts
	Fine-tuning parameters for QAOA
	Implementing QAOA for optimization
	Summary
	Further reading
Chapter 9: Running QAOA on Simulators and Amazon Braket Devices
	Technical requirements
	Further QAOA considerations
		Full QAOA hybrid algorithm using a classical parameter optimizer
		Multiple-step parameter optimization in QAOA
	Benchmarking QAOA on Amazon Braket devices
		Optimizing an 11x11 matrix
		Optimizing a 34x34 matrix
		Optimizing a 38x38 sparse matrix
	Summary of results
	Summary
	Further reading
	Concluding section 2
Section 3: Real-World Use Cases
Chapter 10: Amazon Braket Hybrid Jobs, PennyLane, and other Braket Features
	Technical requirement
	Utilizing Amazon Braket Hybrid Jobs
		Permissions
		Using Amazon Braket Hybrid Jobs
	A QAOA example using Amazon Braket Hybrid Jobs
		Job Control Code
		Job Source Module
	Xanadu PennyLane
		Calling Amazon Braket devices from PennyLane
		Using PennyLane within Amazon Braket Hybrid Jobs
	Xanadu Borealis
	IBM Qiskit
	Other Amazon Braket Hybrid Jobs features
		Controlling the region of the environment
		Hardware configuration
		Multiple parallel device execution
		Debugging failed jobs
		Containers
	Summary
	Further reading
Chapter 11: Single-Objective Optimization Use Case
	Technical requirements
	Introduction to the knapsack problem
	Visualizing the knapsack problem
	QUBO formulation for the knapsack problem
		Implementing the knapsack QUBO in code
		Stitching the QUBO matrices together
	Getting results from different QUBO samplers
		Using the probabilistic sampler
		Running the knapsack problem on a D-Wave device
		Running the knapsack problem on Amazon Braket simulator SV1
		Running the knapsack optimization problem on a Rigetti Aspen 11 device
		Running on a Rigetti Aspen M-1 device
	A process for solving constrained optimization problems
	Summary
	Further reading
Chapter 12: Multi-Objective Optimization Use Case
	Technical requirements
	Looking into a mock inventory management problem
		Setting up the multi-objective problem
		Evaluating the best product mix based on scenario A
	Determining the conflict based on the opposing objectives
		Evaluating the results with the probabilistic solver
		Evaluating the optimal values using the D-Wave annealer
	Determining a better global solution
		Evaluating with the classical probabilistic solver
		Evaluating the best solution using D-Wave
	Summary
	Further reading
	Concluding section 3
Appendix –  Knapsack BQM Derivation
Index
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