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.