1. | EXECUTIVE SUMMARY |
1.1. | Quantum Computing Market: Analyst Opinion |
1.2. | The race for quantum computing: an ultra-marathon not a sprint |
1.3. | Introduction to quantum computers |
1.4. | Quantum computer hardware sales could be a USD$10B by 2045, with a CAGR of 30% |
1.5. | Summary of applications for quantum computing |
1.6. | The number of companies commercializing quantum computers rapidly grew in the last 20 years |
1.7. | Investment in quantum computing is growing |
1.8. | The business model for quantum computing |
1.9. | Colocation data centers key partners for quantum hardware developers to reach more customers |
1.10. | Four major challenges for quantum hardware |
1.11. | Shortage of quantum talent is a challenge for the industry |
1.12. | Blueprint for a quantum computer: qubits, initialization, readout, manipulation |
1.13. | How is the industry benchmarked? |
1.14. | Competing quantum computer architectures: Summary table |
1.15. | Introduction to the IDTechEx Quantum Commercial Readiness Level (QCRL) |
1.16. | Predicting the tipping point for quantum computing |
1.17. | Demand for quantum computer hardware will lag user number |
1.18. | Comparing the physical qubit roadmap of major quantum hardware developers (chart) |
1.19. | Comparing the qubit roadmap of major quantum hardware developers (discussion) |
1.20. | Comparing characteristics of different quantum computer technologies |
1.21. | Summarizing the promises and challenges of leading quantum hardware |
1.22. | Summarizing the promises and challenges of leading quantum hardware |
1.23. | Entering the logical qubit era (1) |
1.24. | Comparing progress in logical qubit number scalability between key players/qubit modalities |
1.25. | Business Model Trends: Vertically Integrated vs. The Quantum 'Stack' |
1.26. | Infrastructure Trends: Modular vs. Single Core |
1.27. | Overviewing early adopters of on-premises quantum computers |
1.28. | China's tech giants change course away from quantum and towards AI |
1.29. | Big chip makers are advancing their quantum computing capabilities |
1.30. | Confidence in the potential of topological quantum computing is rising |
1.31. | Quantum and AI - ally or competitor? |
1.32. | IBM: Quantum roadmap update 2024 |
1.33. | IQM release new roadmap promising quantum advantage by 2030 |
1.34. | Quantinuum: winning the race for three 9s and an accelerated development roadmap |
1.35. | IonQ: Secures a $54.5M contract with the U.S. Air Force Research Lab and expands photonic capabilities |
1.36. | Oxford Ionics achieves record fidelities in the lab |
1.37. | Aegiq - offering versatility without a universal machine |
1.38. | PsiQuantum benefiting from over $1B in investment to build quantum computing data centers in Australia and the US (1) |
1.39. | Pasqal: reaching the 1000 qubit milestone in 2024 and planning for 10,000 by 2026 |
1.40. | Infleqtion (Cold Quanta) achieve 'world's largest qubit array', and what to make it ten times bigger by 2030 |
1.41. | Quantum Brilliance offer lower power quantum solutions for HPC integration in the NISQ era, and opportunities on the edge long term |
1.42. | D-Wave intensifies focus on increasing production application deployments |
1.43. | Energy consumption concerns continue to present challenges for next generation computing |
1.44. | 'NISQ is dead' |
1.45. | NATO announced first quantum strategy in 2024 |
1.46. | The value proposition of quantum computing, and risk to security, remains a key driver for development |
1.47. | Main conclusions (I) |
1.48. | 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. | 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. | Competing quantum computer architectures: Summary table |
3.4.3. | IDTechEx - Quantum commercial readiness level (QCRL) |
3.4.4. | QCRL scale (1-5, commercial application focused) |
3.4.5. | 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 (2025-2045, logarithmic scale) |
4.8. | Forecast for installed based of quantum computers by technology (2025-2045) - logarithmic scale |
4.9. | Forecast for quantum computer pricing |
4.10. | Forecast for annual revenue from quantum computer hardware sales, 2025-2045 |
4.11. | Forecast annual revenue from quantum computing hardware sales (breakdown by technology), 2025-2045 |
4.12. | Forecasting discussion - challenges in twenty-year horizons |
4.13. | Quantum computer market coverage: key forecasting changes since the last report |
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. | Comparing key players in superconducting quantum computing (hardware) |
5.2.7. | IBM: Quantum roadmap update 2024 |
5.2.8. | Roadmap for superconducting quantum hardware (chart) |
5.2.9. | Roadmap for superconducting quantum hardware (discussion) |
5.2.10. | Simplifying superconducting architecture requirements for scale-up |
5.2.11. | IQM release new roadmap promising quantum advantage by 2030 |
5.2.12. | Critical material chain considerations for superconducting quantum computing |
5.2.13. | SWOT analysis: superconducting quantum computers |
5.2.14. | 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. | Quantinuum - winning the race for three 9s and an accelerated development roadmap |
5.3.8. | IonQ: Secures a $54.5M contract with the U.S. Air Force Research Lab and expands photonic capabilities |
5.3.9. | Oxford Ionics achieves record fidelities in the lab |
5.3.10. | SWOT analysis: trapped-ion quantum computers |
5.3.11. | 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. | PsiQuantum benefiting from over $1B in investment to build quantum computing data centers in Australia and the US (1) |
5.4.7. | PsiQuantum benefiting from over $1B in investment to build quantum computing data centers in Australia and the US (2) |
5.4.8. | Aegiq - offering versatility without a universal machine |
5.4.9. | Roadmap for photonic quantum hardware (chart) |
5.4.10. | Roadmap for photonic quantum hardware (discussion) |
5.4.11. | SWOT analysis: photonic quantum computers |
5.4.12. | 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. | QuEra receiving strategic investment from Google |
5.6.7. | Atom Computing partner with Microsoft |
5.6.8. | Pasqal: reaching the 1000 qubit milestone in 2024 and planning for 10,000 by 2026 |
5.6.9. | Infleqtion (Cold Quanta) achieve 'world's largest qubit array', and what to make it ten times bigger by 2030 |
5.6.10. | Roadmap for neutral-atom quantum computing hardware (discussion) |
5.6.11. | SWOT analysis: neutral-atom quantum computers |
5.6.12. | 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. | Quantum Brilliance offer lower power quantum solutions for HPC integration in the NISQ era, and opportunities on the edge long term |
5.7.8. | SWOT analysis: diamond-defect quantum computers |
5.7.9. | 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. | Confidence in the potential of topological quantum computing is rising |
5.8.7. | Roadmap for topological quantum computing hardware (discussion) |
5.8.8. | SWOT analysis: topological qubits |
5.8.9. | 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. | Clarity on annealing related terms |
5.9.7. | Comparing key players in quantum annealing |
5.9.8. | Roadmap for neutral-atom quantum computing hardware (chart) |
5.9.9. | D-Wave intensifies focus on increasing production application deployments |
5.9.10. | Roadmap for quantum annealing hardware (discussion) |
5.9.11. | SWOT analysis: quantum annealers |
5.9.12. | 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. | Main conclusions (I) |
5.10.5. | 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 AND FINANCE 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 |
7.1.4. | Most automotive players are pursuing quantum computing for battery chemistry |
7.1.5. | The automotive industry is yet to converge on a preferred qubit modality |
7.1.6. | Partnerships and collaborations for automotive quantum computing |
7.1.7. | Mercedes: Case study in remaining hardware agnostic |
7.1.8. | Tesla: Supercomputers not quantum computers |
7.1.9. | Summary of key conclusions |
7.1.10. | Analyst opinion on quantum computing for automotive |
7.2. | Finance Applications of Quantum Computing |
7.2.1. | Despite its early stage, preparing for quantum computing now is a key strategy in the finance industry (1) |
7.2.2. | Despite its early stage, preparing for quantum computing now is a key strategy in the finance industry (2) |
7.2.3. | Use cases of quantum computing in finance |
7.2.4. | HSBC and Quantum Key Distribution (1) |
7.2.5. | HSBC and Quantum Key Distribution (2) |
8. | MATERIALS FOR QUANTUM TECHNOLOGY |
8.1. | Chapter Overview |
8.2. | Superconductors |
8.2.1. | Overview of superconductors in quantum technology |
8.2.2. | Critical temperature plays a key role in superconductor material choice for quantum technology |
8.2.3. | Critical material chain considerations for superconducting quantum computing |
8.2.4. | Overview of the superconductor value chain in quantum technology |
8.2.5. | Room temperature superconductors - and why they won't necessarily unlock the quantum technology market |
8.2.6. | Superconducting nanowire single photon detector (SNSPD) |
8.2.7. | Superconducting nanowire single photon detectors (SNSPDs) |
8.2.8. | SNSPD applications must value performance highly enough to justify the bulk/cost of cryogenics |
8.2.9. | Research in scaling SNSPD arrays beyond kilopixel |
8.2.10. | Advancements in superconducting materials drives SNSPD development |
8.2.11. | Comparison of commercial SNSPD players |
8.2.12. | SWOT analysis: superconducting nanowire single photon detectors (SNSPDs) |
8.2.13. | Kinetic inductance detector (KID) and transition edge sensor (TES) |
8.2.14. | Kinetic inductance detectors (KIDs) |
8.2.15. | Transition edge sensors (TES) |
8.2.16. | How have SNSPDs gained traction while KIDs and TESs remain in research? |
8.2.17. | Comparison of single photon detector technology |
8.3. | Photonics, Silicon Photonics and Optical Components |
8.3.1. | Overview of photonics, silicon photonics and optics in quantum technology |
8.3.2. | Overview of material considerations for photonic integrated circuits (PICs) |
8.3.3. | Photonic computing demands better electro-optical materials, alternatives to standard silicon and warmer superconductors than niobium (1) |
8.3.4. | Photonic computing demands better electro-optical materials, alternatives to standard silicon and warmer superconductors than niobium (2) |
8.3.5. | VCSELs enable miniaturization of quantum sensors and components |
8.3.6. | Alkali azides used to overcome high-vacuum fabrication requirements of vapor cells for quantum sensing |
8.3.7. | An opportunity for better optical fiber and quantum interconnects materials |
8.3.8. | Semiconductor single photon detectors |
8.4. | Nanomaterials (Graphene, CNTs, Diamond and MOFs) |
8.4.1. | Introduction to 2D Materials for Quantum Technology |
8.4.2. | Interest in TMD based quantum dots as single photon sources for quantum networking |
8.4.3. | Introduction to graphene membranes |
8.4.4. | Research interest in graphene membranes for RAM memory in quantum computers |
8.4.5. | 2.5D Materials pitches as solution to quantum information storage |
8.4.6. | Single Walled Carbon Nanotubes for Quantum Computers and C12 |
8.4.7. | Long term potential in the quantum materials market for Boron Nitride Nanotubes (BNNT) |
8.4.8. | Snapshot of market readiness levels of CNT applications - quantum only at PoC stage |
8.4.9. | Overview of diamond in quantum technology |
8.4.10. | Material advantages and disadvantages of diamond for quantum applications |
8.4.11. | Element Six are leaders in scaling up manufacturing of diamond for quantum applications using chemical vapor deposition (CVD) |
8.4.12. | Overview of the synthetic diamond value chain in quantum technology |
8.4.13. | Chromophore integrated MOFs can stabilize qubits at room temperature for quantum computing |
8.4.14. | Conclusions and Outlook: Summary of material opportunities in quantum technology |
9. | COMPANY PROFILES |
9.1. | Aegiq |
9.2. | BlueFors (Helium) |
9.3. | Classiq |
9.4. | D-Wave |
9.5. | Diatope |
9.6. | Diraq |
9.7. | Element Six (Quantum Technologies) |
9.8. | Hitachi Cambridge Laboratory (HCL) |
9.9. | IBM (Quantum Computing) |
9.10. | Infineon (Quantum Algorithms) |
9.11. | Infleqtion (previously Cold Quanta) |
9.12. | IonQ |
9.13. | nu quantum |
9.14. | ORCA Computing |
9.15. | Powerlase Ltd |
9.16. | PsiQuantum |
9.17. | Q.ANT |
9.18. | Quantinuum |
9.19. | QuantrolOx |
9.20. | Quantum Brilliance |
9.21. | Quantum Computing Inc |
9.22. | Quantum Motion |
9.23. | Quantum XChange |
9.24. | QuEra |
9.25. | QuiX Quantum |
9.26. | River Lane |
9.27. | Schrödinger Update: Batteries and Materials Informatics |
9.28. | SEEQC |
9.29. | SemiWise |
9.30. | Senko Advance Components Ltd |
9.31. | Single Quantum |
9.32. | Siquance |
9.33. | VTT Manufacturing (Quantum Technologies) |
9.34. | XeedQ |