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Quantum Computing 2023-2043
Market analysis of hardware enabling quantum computing. Includes twenty-year quantum computing market forecasts, with superconducting, photonic, silicon-spin, neutral-atom, trapped-ion, diamond defect, topological, and annealing categories.
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IDTechEx's report 'Quantum Computing 2023-2043' 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 2023-2043. 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$2.9 billion by 2043. 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 short-, medium-, and long-term - and when are inflexion points for commercial value and on-premises ownership anticipated?
A pivotal year for Quantum Computers ahead
In the last decade, the number of companies actively developing quantum computer hardware has quadrupled. In 2022 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.
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 2022 superconducting quantum computers with over 400 qubits were unveiled - 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. 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.
Analyst access from IDTechEx
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Further information
If you have any questions about this report, please do not hesitate to contact our report team at research@IDTechEx.com or call one of our sales managers:
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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 |
Report Statistics
| Slides | 222 |
|---|---|
| Forecasts to | 2043 |
| ISBN | 9781915514417 |