量子コンピューティング市場 2025-2045年：技術、トレンド、有力企業、予測
量子コンピューティングを実現するハードウェアの市場分析。超伝導、フォトニック、シリコンスピン、中性原子、イオントラップ、ダイヤモンド結晶欠陥、トポロジカル、アニーリングなど技術別20年間の量子コンピューティング市場予測も掲載。
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量子コンピューティングは計算能力に革命をもたらすと期待されています。量子コンピューティング市場を分かりやすく解説した本調査レポートでは、主要技術、企業、成長要因、導入障壁について評価を行っています。超伝導、シリコンスピン、フォトニック、イオントラップ、中性原子、トポロジカル、ダイヤモンド結晶欠陥、アニーリングという競合する複数の量子コンピューター技術を評定し、各方式の詳細なSWOT分析、企業ロードマップ、ベンチマーク評価とともに、それぞれの展望をまとめた20年間の市場予測も提供します。
「量子コンピューティング市場 2025-2045年」が対象とする主なコンテンツ
（詳細は目次のページでご確認ください）
■ 全体概要
■ 市場予測
- 量子コンピューティング市場20年間予測（2025～2045年）
- 数量別（量子コンピューター年間販売台数と設置台数）量子コンピューター市場規模
- 量子コンピューター収益予測（ユーザー数と量子コンピューター販売台数の市場規模）
- 量子コンピューター収益予測（ドル）
- 8技術カテゴリー（超伝導、イオントラップ、中性原子、フォトニック、シリコンスピン、ダイヤモンド結晶欠陥、トポロジカル、アニーリング）の量子コンピューター予測
■ 量子コンピューターベンチマーク評価
- 量子ビットベンチマーク：ノイズ、コヒーレンス時間、忠実度
- コンピューターベンチマーク：量子ビット数、量子ボリューム、アルゴリズム量子ビット、CLOPS（Circuit Layer Operations Per Second）
- 業界基準：ディ・ヴィンセンゾ基準、QCRL（Quantum Commercial Readiness Level）
■ 競合ハードウェア技術・量子ビット方式評価（技術概要、有力企業比較、企業ロードマップ、SWOT分析を含む）
- 超伝導
- フォトニック
- シリコンスピン
- 中性原子
- イオントラップ
- トポロジカル
- ダイヤモンド結晶欠陥
- 量子アニーラー
■ 冷却、材料、インフラなどのインフラ要件評価（単一光子検出器を含む）
■ 自動車分野・金融分野での用途
■ 企業概要（30社以上）
「量子コンピューティング市場 2025-2045年」は以下の情報を提供します
- 量子技術知識がない方も理解しやすい量子コンピューティング分野の包括的入門書
- 各種ビジネスモデル、クラウドサービスの役割、量子コンピューティング商業的状況の今後の発展評価
- 各種量子コンピューティング技術のベンチマークツール一式（一般採用技術と商業的可能性評価用途に特別開発されたその他技術を含む）
- 競合する主な量子コンピューター技術の違いを解説。技術的詳細、動作原理、主要企業、SWOT分析、ベンチマーク評価、特有の材料要件。超伝導、フォトニック、シリコンスピン、中性原子、イオントラップのプラットフォームに加え、アニーラー、ダイヤモンド結晶欠陥などの代替技術も解説。
- 主要企業50社以上の概要（設立年、達成した量子ビット数（および予測数）の過去実績も記載）
- 30社以上の企業概要をIDTechExポータルで提供
- 量子コンピューティングのインフラ要件概要（冷却や熱管理など）
- 喧伝や資金調達動向と技術成熟度や獲得可能市場のバランスを考慮し、量子コンピューティング業界での収益見通しを公平に評価
- 量子コンピューティング技術別20年間予測
市場予測・分析：
- 数量別（システム販売台数）、収益別量子コンピューターハードウェアの20年間市場予測
- 量子コンピューター普及のメタトレンド60年予測（実現した万能コンピューターの領域を超え、大衆市場普及を見据える）
IDTechEx's report 'Quantum Computing Market 2025-2045' covers the hardware that promises a revolutionary approach to solving the world's unmet challenges. The quantum computing market 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. 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 quantum computing market is forecast to surpass US$10B by 2045 with a CAGR of 30%.
These competing technologies in the quantum computing market 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 2025-2045. 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).
- 30+ Company Profiles available on the IDTechEx portal
- 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 | 30% |
| Forecast Period | 2025 - 2045 |
| Forecast Units | Revenue, volume (1unit = 1 quantum computer) |
| Segments Covered | -Forecasts are segmented by qubit modality: -Superconducting o Photonic o Silicon Spin o Neutral Atom o Trapped Ion o Topological o Diamond defect o Quantum Annealers |
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詳細
この調査レポートに関してのご質問は、下記担当までご連絡ください。
アイディーテックエックス株式会社 (IDTechEx日本法人)
電話 03-3216-7209
担当: 村越美和子 m.murakoshi@idtechex.com
| 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 |
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レポート概要
| スライド | 327 |
|---|---|
| 企業数 | 30+ |
| フォーキャスト | 2045 |
| 発行日 | Nov 2024 |
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