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1. | EXECUTIVE SUMMARY |
1.1. | Introduction to quantum computers |
1.2. | Summary of applications for quantum computing |
1.3. | The number of companies commercializing quantum computers is growing |
1.4. | Investment in quantum computing is growing |
1.5. | The business model for quantum computing |
1.6. | Colocation data centers key partners for quantum hardware developers to reach more customers |
1.7. | Four major challenges for quantum hardware |
1.8. | Shortage of quantum talent is a challenge for the industry |
1.9. | Blueprint for a quantum computer: qubits, initialization, readout, manipulation |
1.10. | How is the industry benchmarked? |
1.11. | Quantum supremacy and qubit number |
1.12. | Ranking competing technologies by coherence time |
1.13. | Introduction to the IDTechEx Quantum Commercial Readiness Level (QCRL) |
1.14. | Predicting the tipping point for quantum computing |
1.15. | Demand for quantum computer hardware will lag user number |
1.16. | Forecast revenue generated by quantum computer hardware sales |
1.17. | Comparing the qubit roadmap of major quantum hardware developers (chart) |
1.18. | Comparing the qubit roadmap of major quantum hardware developers (discussion) |
1.19. | Comparing characteristics of different quantum computer technologies |
1.20. | Summarizing the promises and challenges of leading quantum hardware |
1.21. | Summarizing the promises and challenges of leading quantum hardware |
1.22. | Competing quantum computer architectures: Summary table |
1.23. | Hardware agnostic approaches de-risk quantum strategy |
1.24. | Main conclusions (I) |
1.25. | Main conclusions (II) |
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. | Collaboration versus quantum nationalism |
2.2.7. | The quantum computing industry is becoming more competitive which is driving innovation |
2.2.8. | The business model for quantum computing |
2.2.9. | Commercial partnership is driver for growth and a tool for technology development |
2.2.10. | Partnerships forming now will shape the future of quantum computing for the financial sector |
2.2.11. | Four major challenges for quantum hardware |
2.2.12. | A complex eco-system |
2.2.13. | Shortage of quantum talent is a challenge for the industry |
2.2.14. | Timelines for ROI are unclear in the NISQ (noisy intermediate scale quantum) era |
2.2.15. | Competition with advancements in classical computing |
2.2.16. | 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. | Applications of quantum algorithms |
2.3.8. | 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 (2023-2043, linear scale) |
4.8. | Forecast for installed based of quantum computers (2023-2043, logarithmic scale) |
4.9. | Forecast for installed based of quantum computers by technology (2023-2043) |
4.10. | Forecast for quantum computing technologies (adoption proportion) |
4.11. | Forecast for quantum computer pricing |
4.12. | Forecast for annual revenue from quantum computer hardware sales, 2023-2043 |
4.13. | Forecast annual revenue from quantum computing hardware sales (breakdown by technology), 2023-2043 |
4.14. | Forecast for data center number compared to historical data |
4.15. | Identifying the crucial years for the on-premises business model |
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. | Supply chain considerations for superconducting metals |
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 (notes) |
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. | Trapped Ion and Neutral Atom platforms beginning to compete |
5.6.8. | SWOT analysis: neutral-atom quantum computers |
5.6.9. | 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 |
5.8.1. | Topological qubits |
5.8.2. | Initialization, manipulation and readout of topological qubits |
5.8.3. | Topological qubits still requires 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. | Comparing key players in quantum annealing |
5.9.7. | Roadmap for neutral-atom quantum computing hardware (chart) |
5.9.8. | Roadmap for quantum annealing hardware (discussion) |
5.9.9. | SWOT analysis: quantum annealers |
5.9.10. | 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. | A note on research phase qubit hardware |
5.10.4. | Competing quantum computer architectures: Summary table |
5.10.5. | Hardware agnostic approaches de-risk quantum strategy |
5.10.6. | Main conclusions (I) |
5.10.7. | 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. | Introduction to cryostats for quantum computing |
6.4. | Understanding cryostat architectures |
6.5. | Bluefors are the market leaders in cryostat supply for superconducting quantum computers (chart) |
6.6. | Bluefors are the market leaders in cryostat supply for superconducting quantum computers (discussion) |
6.7. | Opportunities in the Asian supply chain for cryostats |
6.8. | Cryostats need two forms of helium, with different supply chain considerations |
6.9. | Helium isotope (He3) considerations |
6.10. | Summary of cabling and electronics requirements inside a dilution refrigerator for quantum computing |
6.11. | Qubit readout methods: microwaves and microscopes |
6.12. | 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. | Element Six |
8.4. | Hitachi |
8.5. | Quantum motion |
8.6. | QuiX Quantum |
8.7. | SEEQC |
8.8. | XeedQ |
Slides | 222 |
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Forecasts to | 2043 |
ISBN | 9781915514417 |