1st Edition
Quantum Computing Strategy Foundations and Applicability
Quantum computing is not merely an incremental advancement in computing technology; it represents a fundamentally different paradigm from classical computing. Rooted in quantum mechanics, it introduces an entirely new information theory. As a result, translating existing models, solution designs, and approaches to quantum computing is a complex and non-trivial task. This comprehensive book demystifies complex quantum concepts through accessible explanations, practical case studies, and real-world examples from various industries including aerospace, agriculture, automotive, chemicals, energy, finance, government, healthcare, manufacturing, supply chain and telecommunications.
The book blends business perspective with scientific rigor. It is split into two parts. The first section explains the foundational technical concepts covering quantum mechanics principles that enable quantum technologies, key quantum algorithms, mathematical concepts, quantum computing technologies, post-quantum cryptography, types of problems quantum computers solve, and the technology outlook. The second section covers practical applicability providing industry use case examples, how to approach quantum computing problems, explains how to map use cases to quantum computing, the responsible use of quantum computing, and details a roadmap for businesses to prepare for quantum adoption. This structured approach equips readers with the knowledge and tools needed to integrate quantum computing into their strategic planning effectively.
Quantum Computing Strategy: Foundations and Applicability is an essential reference for technology enthusiasts, business leaders, policymakers, and educators seeking to understand the benefit quantum computing brings for enterprises. It is designed to be a self-contained learning resource.
Chapter 2: Quantum Computers Overview
2.1 Analog and Digital Quantum Computers
2.2 Quantum Computer Simulators
2.3 Qubit Modalities Definitions
Chapter 3: Quantum Programming
Chapter 4: Quantum Algorithms Overview
4.4 Quantum Inspired Algorithms
Chapter 5: Algorithms Foundations
5.1 Grover Unstructured Search
5.5 Harrow–Hassidim–Lloyd Linear Solvers
5.5 Quantum Metropolis Equilibrium
6.6 Variational Quantum Eigensolver (VQE)
6.7 Quantum Amplitude Estimation (QAE)
6.8 Quantum Approximate Optimization Algorithm (QAOA)
6.9 Quadratic Unconstrained Binary Optimization (QUBO)
6.10 Quantum Differential Equation (QDE)
6.11 Quantum artificial intelligence (QAI)
Chapter 7: Problem Categorization
Chapter 8: Quantum Computing Risk
8.1 Quantum Cryptographic Schemes
8.2 Quantum-secure Cryptography QKD
8.3 Post-quantum cryptography algorithms
8.4 Quantum Safety Strategy Plan
Chapter 9: Technology Adoption Outlook
10.3 Use Case: Irregular Operations
11.1 Use Case: Efficient Fertilizers
11.3 Use Case: Weather Forecast
11.4 Use Case: Improved Crop Yield
12.1 Use Case: EV Batteries / Fuel cells
12.2 Use Case: Transport Routing Flow
12.3 Use Case: Object Detection
12.4 Use Case: Aerodynamic Design
13.1 Use Case: Understanding molecular properties
13.2 Use Case: Design of Aggregates
13.3 Use Case: Crystal Structure
13.4 Use Case: Chemical Reactions Catalysts
14.1 Use Case: Reservoir Simulation
14.1 Use Case: Energy Unit Commitment
14.2 Use Case: Smart-grid Operation
14.3 Use Case: Gas Turbine Design
15.1 Use Case: Portfolio Management
15.2 Use Case: Fraudulent Transactions
15.3 Use Case: Product Pricing Accuracy
16.1 Use Case: Carbon Capture Sustainability
16.2 Use Case: Transport Efficiency
16.3 Use Case: Satellite Imaging
16.4 Use Case: Military Operations
Chapter 17: Healthcare life sciences
17.1 Use Case: Drug Candidates
17.2 Use Case: Medical Imaging
17.3 Use Case: Protein Pathology
18.1 Use Case: Improving Materials
18.2 Use Case: Assembly Line Flow
18.3 Use Case: Predictive Maintenance
18.4 Use Case: Components Performance
19.1 Use Case: Energy Delivery
19.2 Use Case: Load Optimization
19.3 Use Case: Just-in-time Logistics
19.4 Use Case: Demand Forecast
Chapter 20: Telecommunications
20.3 Use Case: Network Planning
20.3 Use Case: Service Quality
20.4 Use Case: MIMO Spectrum Efficiency
Chapter 21: Use case problem mapping
Biography
Elena Yndurain is a high-tech executive and professor expert in operationalizing innovation. She holds a PhD in Telematics Engineering focused on AI, an Executive MBA, B.Sc. in CS and Math. She has worked internationally in consulting, technology, multilateral banking, and software start-ups.
