| 1. | EXECUTIVE SUMMARY | 
| 1.1. | Overview | 
| 1.1.1. | Quantum Computing Market: Analyst Opinion | 
| 1.1.2. | The race for quantum computing: an ultra-marathon not a sprint | 
| 1.1.3. | Introduction to quantum computers | 
| 1.1.4. | Summary of applications for quantum computing | 
| 1.1.5. | The number of companies commercializing quantum computers is growing | 
| 1.1.6. | Investment in quantum computing is growing | 
| 1.1.7. | The business model for quantum computing | 
| 1.1.8. | Colocation data centers key partners for quantum hardware developers to reach more customers | 
| 1.1.9. | Four major challenges for quantum hardware | 
| 1.1.10. | Shortage of quantum talent is a challenge for the industry | 
| 1.1.11. | Blueprint for a quantum computer: qubits, initialization, readout, manipulation | 
| 1.1.12. | How is the industry benchmarked? | 
| 1.1.13. | Quantum supremacy and qubit number | 
| 1.1.14. | Ranking competing technologies by coherence time | 
| 1.1.15. | Introduction to the IDTechEx Quantum Commercial Readiness Level (QCRL) | 
| 1.1.16. | Predicting the tipping point for quantum computing | 
| 1.1.17. | Demand for quantum computer hardware will lag user number | 
| 1.1.18. | Comparing the physical qubit roadmap of major quantum hardware developers (chart) | 
| 1.1.19. | Comparing the qubit roadmap of major quantum hardware developers (discussion) | 
| 1.1.20. | Comparing characteristics of different quantum computer technologies | 
| 1.1.21. | Summarizing the promises and challenges of leading quantum hardware | 
| 1.1.22. | Summarizing the promises and challenges of leading quantum hardware | 
| 1.1.23. | Competing quantum computer architectures: Summary table | 
| 1.1.24. | Hardware agnostic approaches de-risk quantum strategy | 
| 1.1.25. | Main conclusions (I) | 
| 1.1.26. | Main conclusions (II) | 
| 1.2. | Quantum Computing Market - Key Updates for 2023/24 | 
| 1.2.1. | Entering the logical qubit era (1) | 
| 1.2.2. | Comparing progress in logical qubit number scalability between key players/qubit modalities | 
| 1.2.3. | Quantum computer market coverage: key forecasting changes | 
| 1.2.4. | Business Model Trends: Vertically Integrated vs. The Quantum 'Stack' | 
| 1.2.5. | Infrastructure Trends: Modular vs. Single Core | 
| 1.2.6. | Overviewing early adopters of on-premises quantum computers | 
| 1.2.7. | China's tech giants change course away from quantum and towards AI | 
| 1.2.8. | Big chip makers are advancing their quantum computing capabilities | 
| 1.2.9. | Confidence in the potential of topological quantum computing is rising | 
| 1.2.10. | Quantum and AI - ally or competitor? | 
| 2. | INTRODUCTION TO QUANTUM COMPUTING | 
| 2.1. | Chapter overview | 
| 2.2. | Sector overview | 
| 2.2.1. | Introduction to quantum computers | 
| 2.2.2. | Investment in quantum computing is growing | 
| 2.2.3. | Government funding in the US, China, and Europe is driving the commercializing of quantum technologies | 
| 2.2.4. | USA National Quantum Initiative aims to accelerate research and economic development | 
| 2.2.5. | The UK National Quantum Technologies Program | 
| 2.2.6. | Eleven quantum technology innovation hubs now established in Japan | 
| 2.2.7. | Quantum in South Korea: ambitions to become a global leader in the 2030s | 
| 2.2.8. | Quantum in Australia: creating clear benchmarks of national quantum eco-system success | 
| 2.2.9. | Collaboration versus quantum nationalism | 
| 2.2.10. | The quantum computing industry is becoming more competitive which is driving innovation | 
| 2.2.11. | The business model for quantum computing | 
| 2.2.12. | Commercial partnership is driver for growth and a tool for technology development | 
| 2.2.13. | Partnerships forming now will shape the future of quantum computing for the financial sector | 
| 2.2.14. | Four major challenges for quantum hardware | 
| 2.2.15. | A complex eco-system | 
| 2.2.16. | Shortage of quantum talent is a challenge for the industry | 
| 2.2.17. | Timelines for ROI are unclear in the NISQ (noisy intermediate scale quantum) era | 
| 2.2.18. | Competition with advancements in classical computing | 
| 2.2.19. | Value capture in quantum computing | 
| 2.3. | Technical primer | 
| 2.3.1. | Classical vs. Quantum | 
| 2.3.2. | Superposition, entanglement, and observation | 
| 2.3.3. | Classical computers are built on binary logic | 
| 2.3.4. | Quantum computers replace binary bits with qubits | 
| 2.3.5. | Blueprint for a quantum computer: qubits, initialization, readout, manipulation | 
| 2.3.6. | Case study: Shor's algorithm | 
| 2.3.7. | 'Hack Now Decrypt Later' (HNDL) and preparing for Q-Day/ Y2Q | 
| 2.3.8. | Applications of quantum algorithms | 
| 2.3.9. | Chapter summary | 
| 3. | BENCHMARKING QUANTUM HARDWARE | 
| 3.1. | Chapter overview | 
| 3.2. | Qubit benchmarking | 
| 3.2.1. | Noise effects on qubits | 
| 3.2.2. | Comparing coherence times | 
| 3.2.3. | Qubit fidelity and error rate | 
| 3.3. | Quantum computer benchmarking | 
| 3.3.1. | Quantum supremacy and qubit number | 
| 3.3.2. | Logical qubits and error correction | 
| 3.3.3. | Introduction to quantum volume | 
| 3.3.4. | Error rate and quantum volume | 
| 3.3.5. | Square circuit tests for quantum volume | 
| 3.3.6. | Critical appraisal of the importance of quantum volume | 
| 3.3.7. | Algorithmic qubits: A new benchmarking metric? | 
| 3.3.8. | Companies defining their own benchmarks | 
| 3.3.9. | Operational speed and CLOPS (circuit layer operations per second) | 
| 3.3.10. | Conclusions: determining what makes a good computer is hard, and a quantum computer even harder | 
| 3.4. | Industry benchmarking | 
| 3.4.1. | The DiVincenzo criteria | 
| 3.4.2. | IDTechEx - Quantum commercial readiness level (QCRL) | 
| 3.4.3. | QCRL scale (1-5, commercial application focused) | 
| 3.4.4. | QCRL scale (6-10, user-volume focused) | 
| 4. | MARKET FORECASTS | 
| 4.1. | Forecasting Methodology Overview | 
| 4.2. | Methodology: Roadmap for quantum commercial readiness level by technology | 
| 4.3. | Methodology: Establishing the total addressable market for quantum computing | 
| 4.4. | Forecast for total addressable market for quantum computing | 
| 4.5. | Predicting cumulative demand for quantum computers over time (1) | 
| 4.6. | Predicting cumulative demand for quantum computers over time (2) | 
| 4.7. | Forecast for installed base of quantum computers (2024-2044, logarithmic scale) | 
| 4.8. | Forecast for installed base of quantum computers (2024-2044, linear scale and data table) | 
| 4.9. | Forecast for installed based of quantum computers by technology (2024-2044) - logarithmic scale | 
| 4.10. | Forecast for quantum computer pricing | 
| 4.11. | Forecast for annual revenue from quantum computer hardware sales, 2024-2044 | 
| 4.12. | Forecast annual revenue from quantum computing hardware sales (breakdown by technology), 2024-2044 | 
| 4.13. | Forecast for data center number compared to historical data | 
| 4.14. | Identifying the crucial years for the on-premises business model | 
| 4.15. | Forecasting discussion - challenges in twenty-year horizons | 
| 5. | COMPETING QUANTUM COMPUTER ARCHITECTURES | 
| 5.1. | Introduction to competing quantum computer architectures | 
| 5.2. | Superconducting | 
| 5.2.1. | Introduction to superconducting qubits (I) | 
| 5.2.2. | Introduction to superconducting qubits (II) | 
| 5.2.3. | Superconducting materials and critical temperature | 
| 5.2.4. | Initialization, manipulation, and readout | 
| 5.2.5. | Superconducting quantum computer schematic | 
| 5.2.6. | Simplifying superconducting architecture requirements for scale-up | 
| 5.2.7. | Comparing key players in superconducting quantum computing (hardware) | 
| 5.2.8. | Roadmap for superconducting quantum hardware (chart) | 
| 5.2.9. | Roadmap for superconducting quantum hardware (discussion) | 
| 5.2.10. | Critical material chain considerations for superconducting quantum computing | 
| 5.2.11. | SWOT analysis: superconducting quantum computers | 
| 5.2.12. | Key conclusions: superconducting quantum computers | 
| 5.3. | Trapped ion | 
| 5.3.1. | Introduction to trapped-ion quantum computing | 
| 5.3.2. | Initialization, manipulation, and readout for trapped ion quantum computers | 
| 5.3.3. | Materials challenges for a fully integrated trapped-ion chip | 
| 5.3.4. | Comparing key players in trapped ion quantum computing (hardware) | 
| 5.3.5. | Roadmap for trapped-ion quantum computing hardware (chart) | 
| 5.3.6. | Roadmap for trapped-ion quantum computing hardware (discussion) | 
| 5.3.7. | SWOT analysis: trapped-ion quantum computers | 
| 5.3.8. | Key conclusions: trapped ion quantum computers | 
| 5.4. | Photonic platform | 
| 5.4.1. | Introduction to light-based qubits | 
| 5.4.2. | Comparing photon polarization and squeezed states | 
| 5.4.3. | Overview of photonic platform quantum computing | 
| 5.4.4. | Initialization, manipulation, and readout of photonic platform quantum computers | 
| 5.4.5. | Comparing key players in photonic quantum computing | 
| 5.4.6. | Roadmap for photonic quantum hardware (chart) | 
| 5.4.7. | Roadmap for photonic quantum hardware (discussion) | 
| 5.4.8. | SWOT analysis: photonic quantum computers | 
| 5.4.9. | Key conclusions: photonic quantum computers | 
| 5.5. | Silicon Spin | 
| 5.5.1. | Introduction to silicon-spin qubits | 
| 5.5.2. | Qubits from quantum dots ('hot' qubits are still pretty cold) | 
| 5.5.3. | CMOS readout using resonators offers a speed advantage | 
| 5.5.4. | The advantage of silicon-spin is in the scale not the temperature | 
| 5.5.5. | Initialization, manipulation, and readout | 
| 5.5.6. | Comparing key players in silicon spin quantum computing | 
| 5.5.7. | Roadmap for silicon-spin quantum computing hardware (chart) | 
| 5.5.8. | Roadmap for silicon spin (discussion) | 
| 5.5.9. | SWOT analysis: silicon spin quantum computers | 
| 5.5.10. | Key conclusions: silicon spin quantum computers | 
| 5.6. | Neutral atom (cold atom) | 
| 5.6.1. | Introduction to neutral atom quantum computing | 
| 5.6.2. | Entanglement via Rydberg states in Rubidium/Strontium | 
| 5.6.3. | Initialization, manipulation and readout for neutral-atom quantum computers | 
| 5.6.4. | Comparing key players in neutral atom quantum computing (hardware) | 
| 5.6.5. | Roadmap for neutral-atom quantum computing hardware (chart) | 
| 5.6.6. | Roadmap for neutral-atom quantum computing hardware (discussion) | 
| 5.6.7. | SWOT analysis: neutral-atom quantum computers | 
| 5.6.8. | Key conclusions: neutral atom quantum computers | 
| 5.7. | Diamond defect | 
| 5.7.1. | Introduction to diamond-defect spin-based computing | 
| 5.7.2. | Lack of complex infrastructure for diamond defect hardware enables early-stage MVPs | 
| 5.7.3. | Supply chain and materials for diamond-defect spin-based computers | 
| 5.7.4. | Comparing key players in diamond defect quantum computing | 
| 5.7.5. | Roadmap for diamond defect quantum computing hardware (chart) | 
| 5.7.6. | Roadmap for diamond-defect based quantum computers (discussion) | 
| 5.7.7. | SWOT analysis: diamond-defect quantum computers | 
| 5.7.8. | Key conclusions: diamond-defect quantum computers | 
| 5.8. | Topological qubits (Majorana) | 
| 5.8.1. | Topological qubits (Majorana mode) | 
| 5.8.2. | Initialization, manipulation, and readout of topological qubits | 
| 5.8.3. | Topological qubits still require cryogenic cooling | 
| 5.8.4. | Microsoft are the only company pursuing topological qubits so far | 
| 5.8.5. | Roadmap for topological quantum computing hardware (chart) | 
| 5.8.6. | Roadmap for topological quantum computing hardware (discussion) | 
| 5.8.7. | SWOT analysis: topological qubits | 
| 5.8.8. | Key conclusions: topological qubits | 
| 5.9. | Quantum annealers | 
| 5.9.1. | Introduction to quantum annealers | 
| 5.9.2. | How do quantum processors for annealing work? | 
| 5.9.3. | Initialization and readout of quantum annealers | 
| 5.9.4. | Annealing is best suited to optimization problems | 
| 5.9.5. | Commercial examples of use-cases for annealing | 
| 5.9.6. | 2024 could be the year for commercial advantage with quantum annealing | 
| 5.9.7. | Comparing key players in quantum annealing | 
| 5.9.8. | Roadmap for neutral-atom quantum computing hardware (chart) | 
| 5.9.9. | Roadmap for quantum annealing hardware (discussion) | 
| 5.9.10. | SWOT analysis: quantum annealers | 
| 5.9.11. | Key conclusions: quantum annealers | 
| 5.10. | Chapter summary | 
| 5.10.1. | Summarizing the promises and challenges of leading quantum hardware | 
| 5.10.2. | Summarizing the promises and challenges of leading quantum hardware | 
| 5.10.3. | Competing quantum computer architectures: Summary table | 
| 5.10.4. | Hardware agnostic approaches de-risk quantum strategy | 
| 5.10.5. | Main conclusions (I) | 
| 5.10.6. | Main conclusions (II) | 
| 6. | INFRASTRUCTURE FOR QUANTUM COMPUTING | 
| 6.1. | Chapter Overview | 
| 6.2. | Hardware agnostic platforms for quantum computing represent a new market for established technologies. | 
| 6.3. | Infrastructure Trends: Modular vs. Single Core | 
| 6.4. | Introduction to cryostats for quantum computing | 
| 6.5. | Understanding cryostat architectures | 
| 6.6. | Bluefors are the market leaders in cryostat supply for superconducting quantum computers (chart) | 
| 6.7. | Bluefors are the market leaders in cryostat supply for superconducting quantum computers (discussion) | 
| 6.8. | Opportunities in the Asian supply chain for cryostats | 
| 6.9. | Cryostats need two forms of helium, with different supply chain considerations | 
| 6.10. | Helium isotope (He3) considerations | 
| 6.11. | Summary of cabling and electronics requirements inside a dilution refrigerator for quantum computing | 
| 6.12. | Qubit readout methods: microwaves and microscopes | 
| 6.13. | Pain points for incumbent platform solutions | 
| 7. | AUTOMOTIVE APPLICATIONS FOR QUANTUM COMPUTING | 
| 7.1. | Automotive applications of quantum computing | 
| 7.1.1. | Quantum chemistry offers more accurate simulations to aid battery material discovery | 
| 7.1.2. | Quantum machine learning could make image classification for vehicle autonomy more efficient | 
| 7.1.3. | Quantum optimization for assembly line and distribution efficiency could save time, money, and energy | 
| 7.2. | Quantum computing for automotive: Key player activity | 
| 7.2.1. | Most automotive players are pursuing quantum computing for battery chemistry | 
| 7.2.2. | The automotive industry is yet to converge on a preferred qubit modality | 
| 7.2.3. | Partnerships and collaborations for automotive quantum computing | 
| 7.2.4. | Mercedes: Case study in remaining hardware agnostic | 
| 7.2.5. | Tesla: Supercomputers not quantum computers | 
| 7.2.6. | Summary of key conclusions | 
| 7.2.7. | Analyst opinion on quantum computing for automotive | 
| 8. | COMPANY PROFILES | 
| 8.1. | Aegiq | 
| 8.2. | Cold Quanta | 
| 8.3. | Diraq | 
| 8.4. | Element Six (Quantum Technologies) | 
| 8.5. | Hitachi Cambridge Laboratory (HCL) | 
| 8.6. | IBM (Quantum Computing) | 
| 8.7. | Infineon (Quantum Algorithms) | 
| 8.8. | Infleqtion (Cold Quanta) | 
| 8.9. | nu quantum | 
| 8.10. | ORCA Computing | 
| 8.11. | Powerlase Ltd | 
| 8.12. | Q.ANT | 
| 8.13. | Quantinuum | 
| 8.14. | Quantum Motion | 
| 8.15. | Quantum XChange | 
| 8.16. | QuEra | 
| 8.17. | River Lane | 
| 8.18. | SEEQC | 
| 8.19. | SemiWise | 
| 8.20. | Senko Advance Components Ltd | 
| 8.21. | Siquance | 
| 8.22. | VTT Manufacturing (Quantum Technologies) | 
| 8.23. | XeedQ |