到2034年，量子计算市场规模将达到8亿美元以上，复合年增长率为30%
2024年-2044年量子计算市场：技术、趋势、参与者和预测
量子计算硬件的市场分析。包括未来20年内的量子计算市场预测，涵盖超导、光子学、硅自旋、中性原子、捕获离子、金刚石缺陷、拓扑和退火类别。
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量子计算预示着计算能力领域的一场革命性变革。这份量子计算市场报告不仅评估了这个新兴行业的关键技术、公司、增长驱动因素和采用障碍，还评估了多种相互竞争的量子计算机技术：超导、硅自旋、光子学、捕获离子、中性原子、拓扑、金刚石缺陷和退火。报告中还进行了详细的SWOT分析，探讨了各大公司的战略布局，并为每种模式提供了基准测试。此外，该报告还提供了未来二十年的市场预测，概述了每种量子计算技术的前景。
IDTechEx's report 'Quantum Computing 2024-2044' covers the hardware that promises a revolutionary approach to solving the world's unmet challenges. Quantum computing is pitched as enabling exponentially faster drug discovery, battery chemistry development, multi-variable logistics, vehicle autonomy, accurate asset pricing, and much more. Drawing on extensive primary and secondary research, including interviews with companies and attendance at multiple conferences, this report provides an in-depth evaluation of the competing quantum computing technologies: superconducting, silicon-spin, photonic, trapped-ion, neutral-atom, topological, diamond-defect and annealing.
These competing quantum computing technologies are compared by key benchmarks including qubit number, coherence time and fidelity. The scalability of whole computer systems is appraised - incorporating hardware needs for qubits initialisation, manipulation, and readout. This results in a twenty-year market forecast covering 2024-2044. The total addressable market for quantum computer use is converted to hardware sales over time, accounting for advancing capabilities and the cloud access business model. The entire hardware market is forecast to grow to US$800m by 2034. This growth will be driven by early adopters in pharmaceutical, chemical, aerospace, and finance institutions, leading to increased installation of quantum computers into colocation data centres and private networks alike. Revenue and volume forecasts are split into eight forecast lines for each methodology covered. Historic data on the number of quantum computer start-ups utilizing each methodology, and the qubit milestones achieved, are also included.
Key questions answered in this report include:
- What is quantum computing and what is the state of the industry?
- How is quantum computing benchmarked? What is the current and future status of the key players and competing quantum computing technologies?
- How can the commercial potential of quantum computer hardware be assessed?
- What are the competing quantum computing technologies, how do they work and what are the opportunities and challenges for both qubits and readout systems?
- What are the underlying platforms and infrastructure needs of quantum computers, such as cooling systems and thermal management?
- What are the prospects for revenue generation from quantum computer hardware?
- How will the market evolve both short, medium, and long term - and when are inflexion points for commercial value and on-premises ownership anticipated?
A logical era for Quantum Computers ahead
In the last decade, the number of companies actively developing quantum computer hardware has quadrupled. Between 2022 and 2024 multiple funding rounds surpassing US$100 million have been closed, and the transition from lab-based toys to commercial product has begun. Competition is building not only between different companies but between quantum computing technologies. The focus today has intensified on the need for logical, or error-corrected qubits. The challenge ahead is to scale up hardware and increase qubit number, while reducing errors as well as infrastructure demand - no mean feat.
Whilst all systems depend on the use of qubits - the quantum equivalent to classical bits - the architectures available to create them vary substantially. Many are now familiar with IBM and their superconducting qubits - housed inside large cryostats and cooled to temperatures colder than deep space. Indeed, in 2023 superconducting quantum computers broke the 1000 qubit milestone - with smaller systems made accessible via the cloud for companies to trial out their problems. However, many agree that the highest value problems - such as drug discovery - need many more qubits, perhaps millions more. As such, alternatives to the superconducting design, many proposing more inherent scalability, have received investment. There are now more than eight technology approaches meaningfully competing to reach the million-qubit milestone.
With so many competing quantum computing technologies across a fragmented landscape, determining which approaches are likely to dominate is essential in identifying opportunities within this exciting industry. IDTechEx uses an in-house framework for quantum commercial readiness level to measure how quantum computer hardware is progressing in comparison with its classical predecessor. Furthermore, as the initial hype around quantum computing begins to cool investors will increasingly demand demonstration of practical benefits, such as quantum supremacy for commercially relevant algorithms. As such, hardware developers need to show not only the quality and quantity of qubits but the entire initialization, manipulation, and readout systems. Improving manufacturing scalability and reducing cooling requirements are also important, which will create opportunities for methodology agnostic providers of infrastructure such as speciality materials and cooling systems. By evaluating both the sector and competing quantum computing technologies, this report provides insight into the opportunities provided by this potentially transformative technology.
Key aspects
This report provides the following information:
- A comprehensive introduction to the quantum computing sector, accessible to those with and without a background in quantum technologies.
- Evaluation of how the quantum computing commercial landscape will evolve, including different business models and the role of cloud services.
- A set of benchmarking tools for comparing different quantum computing technologies, including those commonly adopted within the sector, and an additional method specifically developed for assessing commercial potential.
- Explanation of the differences between the main competing quantum computer technologies. Each covers: technical details, operating principles, key companies, SWOT analysis, benchmarking, and specific material requirements. Technologies include superconducting, photonic, silicon-spin, neutral atom, and trapped ion platforms, plus a section on alternatives including annealers and diamond defects.
- Overview of 50+ key companies with historical data on the year founded, qubit number achieved (and projected).
- Overview of infrastructure requirements for quantum computing, including cooling and thermal management.
- Unbiased appraisal of the prospects for revenue generation within the quantum computing industry, balancing hype and funding trends with technology readiness and addressable market.
- Granular twenty-year forecasts, broken down by quantum computing technology.
Market Forecasts & Analysis:
- 20-year market forecasts for quantum computer hardware by volume (i.e., number of systems sold) and revenue. Individual forecast lines are available for eight different technology categories including superconducting, photonic, trapped-ion, neutral atom, silicon spin, topological, diamond defect, and annealers.
- 60-year projections for meta-trends for quantum computer adoption, going beyond the horizon of a realized versatile computer and looking ahead to mass-market adoption.
| Report Metrics | Details |
|---|---|
| CAGR | 34% |
| Forecast Period | 2024 - 2044 |
| Forecast Units | Revenue, volume (1unit = 1 quantum computer) |
| Segments Covered | -Forecasts are segmented by qubit modality. |
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| 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 |
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