Technology, Trends, Players, Forecasts: IDTechEx

1. EXECUTIVE SUMMARY 1.1. The state of the quantum computing market: analyst opinion 1.2. Introduction to quantum computers 1.3. Which Industries Have Problems Quantum Computing Could Solve? 1.4. Data centers complement the quantum as a service (QaaS) business model 1.5. The market for quantum computing hardware could be worth over US$21 billion by 2046, with a CAGR of 26.7% 1.6. National facilities are early customers of on-premises quantum computers 1.7. Four major challenges for quantum hardware 1.8. Blueprint for a quantum computer: Qubits, initialization, readout, manipulation 1.9. How is the industry benchmarked? 1.10. Introduction to the IDTechEx Quantum Commercial Readiness Level (QCRL) 1.11. Roadmap for quantum commercial readiness level (QCRL) over time 1.12. Predicting the tipping point for quantum computing 1.13. Demand for quantum computer hardware will lag user number 1.14. The number of companies commercializing quantum computers rapidly grew over the last 15 years 1.15. Summarizing the promises and challenges of leading quantum hardware 1.16. Summarizing the promises and challenges of alternative quantum hardware 1.17. Competing quantum computer architectures: summary table 1.18. Roadmap for quantum commercial readiness level (QCRL) by technology 1.19. Forecast for installed based of quantum computers by technology, 2026-2046 1.20. Emergence of the mixed quantum stack 1.21. Infrastructure pain points are near universal for quantum computers 1.22. Where will quantum computers be deployed? 1.23. What is a platform for quantum computing? 1.24. Hyperscalers position themselves as platform enablers 1.25. Quantum for AI, AI for Quantum, or Quantum vs AI? 1.26. What will be the first “killer application” for quantum computing? 1.27. Summary of materials opportunities in quantum computing 1.28. 2025 Updates from Key Players and Market Shifts 1.29. Microsoft’s domestic quantum effort – Majorana 1 1.30. IBM: Roadmap to 100 million gates by 2029 1.31. Google Quantum AI study suggests RSA could be broken with only 1 million physical qubits 1.32. Rigetti develops a tiled chip approach & moves towards mixed stack 1.33. IQM complete over a dozen sales 1.34. Oxford Quantum Circuits release new roadmap targeting early commercial advantage in 2028 1.35. Zuchongzhi 3.0 rivals the performance of leading quantum hardware 1.36. Quantinuum: Growing quantum volume and commercial partnerships 1.37. IonQ acquires Oxford Ionics for a record US$1.08 billion 1.38. IonQ makes a spree of acquisitions including Oxford Ionics 1.39. Oxford Ionics reveals development roadmap 1.40. Infleqtion aim to reduce qubit overhead in neutral atom error correction 1.41. Pasqal targets 200 logical qubits by 2029 and acquires PIC specialist 1.42. PsiQuantum reveals new chipset “Omega” 1.43. ORCA Computing: Towards practical quantum accelerators 1.44. Quantum Brilliance: HPC integration & mobile quantum processors 1.45. Riverlane commercializes hardware for quantum error correction 1.46. Main conclusions (I) 1.47. Main conclusions (II) 1.48. Key market shifts for specific qubit modalities in the last 12 months 1.49. Access more with an IDTechEx subscription 2. INTRODUCTION TO QUANTUM COMPUTING 2.1.1. 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. The quantum ecosystem is growing and covers a variety of approaches 2.2.4. The business model for quantum computing – quantum as a service (QaaS) 2.2.5. Value capture in quantum computing 2.2.6. Commercial partnership is driver for growth and a tool for technology development 2.2.7. Business model trends: vertically integrated vs. the ‘quantum stack’ 2.2.8. Emergence of the mixed quantum stack 2.2.9. Four major challenges for quantum hardware 2.2.10. Shortage of quantum talent is a challenge for the industry 2.2.11. Competing forces in the communication of quantum computing 2.3. National Programs and Initiatives 2.3.1. Quantum computing as a national strategic resource 2.3.2. National facilities are early customers of on-premises quantum computers 2.3.3. Government funding in the US, China, and Europe is driving the commercializing of quantum technologies 2.3.4. USA National Quantum Initiative aims to accelerate research and economic development 2.3.5. DARPA Quantum Benchmarking Initiative 2.3.6. Quantum Economic Development Consortium (QED-C) 2.3.7. NATO announced first quantum strategy in 2024 2.3.8. The UK National Quantum Technologies Program 2.3.9. UK strategy update: NQCC and NQTP receive more support 2.3.10. UK strategy update: Partnerships and London Quantum Technology Cluster 2.3.11. Eleven quantum technology innovation hubs now established in Japan 2.3.12. Quantum in South Korea: Ambitions to become a global leader in the 2030s 2.3.13. Quantum in Australia: Creating clear benchmarks of national quantum eco-system success 2.3.14. Collaboration versus quantum nationalism 2.4. Technical Primer 2.4.1. Classical vs. Quantum 2.4.2. Superposition, entanglement, and observation 2.4.3. Classical computers are built on binary logic 2.4.4. Quantum computers replace binary bits with qubits 2.4.5. Blueprint for a quantum computer: qubits, initialization, readout, manipulation 2.4.6. Case study: Shor’s algorithm 2.4.7. Chapter summary – introduction to quantum computing 3. BENCHMARKING QUANTUM HARDWARE 3.1.1. 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. IonQ introduces algorithmic qubits 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 3.3.11. Conclusions: the logical qubit era and returns on investment 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. Roadmap for quantum commercial readiness level (QCRL) over time 4.4. Methodology: Establishing the total addressable market for quantum computing 4.5. Forecast for total addressable market for quantum computing 4.6. Predicting cumulative demand for quantum computers over time (1) 4.7. Predicting cumulative demand for quantum computers over time (2) 4.8. Forecast for installed base of quantum computers, 2026-2046 4.9. Forecast for annual volume of quantum computers, 2026-2046 4.10. Forecast for quantum computer pricing 2026-2046 4.11. Forecast for annual revenue from quantum computer hardware sales, 2026-2046 4.12. Forecast for installed based of quantum computers by technology, 2026-2046 4.13. Forecast for annual revenue from quantum computing hardware sales (breakdown by technology), 2026-2046 4.14. Comparing the install base of quantum computers to the global number of data centers 4.15. Forecast for the volume of quantum computers deployed in data centers, 2026-2046 4.16. Key forecasting changes since the previous report 5. COMPETING QUANTUM COMPUTER ARCHITECTURES 5.1.1. 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: roadmap to 100 million gates by 2029 5.2.8. IQM release new roadmap promising quantum advantage by 2030 5.2.9. IQM complete over a dozen sales and release product dimensions 5.2.10. Rigetti develops a tiled chip approach & moves towards mixed stack 5.2.11. Oxford Quantum Circuits release new roadmap targeting early commercial advantage in 2028 5.2.12. Zuchongzhi 3.0 rivals the performance of leading quantum hardware 5.2.13. Roadmap for superconducting quantum hardware (chart) 5.2.14. Roadmap for superconducting quantum hardware (discussion) 5.2.15. Simplifying superconducting architecture requirements for scale-up 5.2.16. Critical material chain considerations for superconducting quantum computing 5.2.17. SWOT analysis: Superconducting quantum computers 5.2.18. 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. Quantinuum: Growing quantum volume and commercial partnerships 5.3.6. IonQ acquires Oxford Ionics for a record US$1.08 billion 5.3.7. IonQ makes a spree of acquisitions including Oxford Ionics 5.3.8. Oxford Ionics reveals development roadmap 5.3.9. Roadmap for trapped-ion quantum computing hardware (chart) 5.3.10. Roadmap for trapped-ion quantum computing hardware (discussion) 5.3.11. SWOT analysis: Trapped-ion quantum computers 5.3.12. Key conclusions: Trapped ion quantum computers 5.4. Photonic 5.4.1. Introduction to photonic qubits 5.4.2. Comparing photon polarization and squeezed states 5.4.3. Overview of the photonic platform for quantum computing 5.4.4. Initialization, manipulation, and readout of photonic quantum computers 5.4.5. Comparing key players in photonic quantum computing 5.4.6. PsiQuantum receives over AU$1B in government investments and seeks a US$750M private funding round 5.4.7. PsiQuantum reveals new chipset “Omega” 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. Big chip makers are advancing their quantum computing capabilities 5.5.8. Roadmap for silicon-spin quantum computing hardware (chart) 5.5.9. Roadmap for silicon-spin (discussion) 5.5.10. SWOT analysis: Silicon-spin quantum computers 5.5.11. 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. QuEra completes US$230 million funding round including Google investment 5.6.6. Atom Computing partner with Microsoft 5.6.7. Pasqal targets 200 logical qubits by 2029 and acquires PIC specialist 5.6.8. Infleqtion aim to reduce qubit overhead in neutral atom error correction 5.6.9. Roadmap for neutral-atom quantum computing hardware (chart) 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. Quantum Brilliance offer lower power quantum solutions for data centers in the near term, and opportunities on the edge long term 5.7.6. Quantum Brilliance: HPC integration & mobile quantum processors 5.7.7. Roadmap for diamond defect quantum computing hardware (chart) 5.7.8. Roadmap for diamond-defect based quantum computers (discussion) 5.7.9. SWOT analysis: Diamond-defect quantum computers 5.7.10. Key conclusions: Diamond-defect quantum computers 5.8. Topological Qubits (Majorana) 5.8.1. Topological qubits (Majorana modes) 5.8.2. Initialization, manipulation, and readout of topological qubits 5.8.3. Microsoft are the primary company pursuing topological qubits 5.8.4. Microsoft’s domestic quantum effort – Majorana 1 5.8.5. Scaling up arrays of topological qubits 5.8.6. Roadmap for topological quantum computing hardware (chart) 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. D-Wave intensifies focus on increasing production application deployments 5.9.9. Qilimanjaro develops analog QASIC chips & target QaaS by EoY 5.9.10. Roadmap for neutral-atom quantum computing hardware (chart) 5.9.11. Roadmap for quantum annealing hardware (discussion) 5.9.12. SWOT analysis: Quantum annealers 5.9.13. 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 alternative quantum hardware 5.10.3. Competing quantum computer architectures: Summary table 5.10.4. Main conclusions (I) 5.10.5. Main conclusions (II) 5.10.6. Key market shifts for specific qubit modalities in the last 12 months 6. INFRASTRUCTURE FOR QUANTUM COMPUTING 6.1. Chapter overview 6.2. Infrastructure trends: Modular vs. single core 6.3. Hardware agnostic infrastructure platforms for quantum computing represent a new market for established technologies 6.4. Introduction to cryostats for quantum computing 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. Rare Helium-3 supplies could prove decisive for quantum ecosystems 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. DEPLOYMENT OF QUANTUM COMPUTERS 7.1.1. Where will quantum computers be deployed? 7.1.2. Should deployed quantum computers be ‘hands on’ or ‘hands off’? 7.1.3. HPC integration of quantum computers 7.1.4. Challenges in the delivery and commissioning of quantum computers 7.1.5. Case study: Potential sources of disruption in a quantum computing environment and the sensors used to monitor them – IQM 7.2. Quantum Computing in Data Centers 7.2.1. Data centers are key partners for quantum hardware developers to reach more customers 7.2.2. Data centers complement the quantum as a service (QaaS) business model 7.2.3. Hyperscalers position themselves as platform enablers 7.2.4. What is a platform for quantum computing? 7.2.5. OCP Ready for Quantum 7.2.6. Fundamental principle of cooling systems is similar in data centers and (cryogenically cooled) quantum computers (part 1) 7.2.7. However different orders of magnitude of cooling are required in data centers and quantum computers (part 2) 7.2.8. Energy consumption of cooling systems – classical 7.2.9. Energy consumption of cooling systems – quantum 7.2.10. Comparing the energy consumption of quantum and classical computers 7.2.11. Power demand from data centers will increase significantly over the coming decade 7.2.12. Key takeaways for the data center industry 8. QUANTUM COMPUTING AND AI 8.1. Quantum for AI, AI for Quantum, or Quantum vs AI? 8.2. Use cases for AI in quantum computing 8.3. AI tools could assist in interfacing with quantum machines 8.4. Competition with advancements in classical computing 8.5. Two of China’s tech giants move away from quantum and towards AI 8.6. NVIDIA & quantum computing: NVAQC and Quantum Cloud 8.7. ORCA Computing: Quantum processors for machine learning 8.8. Will quantum computers improve or worsen global energy and technology inequality? 8.9. Conclusion – are quantum and AI allies or competitors? 9. APPLICATIONS OF QUANTUM COMPUTING 9.1. Overview of Key Applications 9.1.1. Chapter overview – applications of quantum computing 9.1.2. What will be the first “killer application” for quantum computing? (Part 1) 9.1.3. What will be the first “killer application” for quantum computing? (Part 2) 9.1.4. ‘Hack Now Decrypt Later’ (HNDL) and preparing for Q-Day/Y2Q 9.1.5. Google Quantum AI study suggests RSA could be broken with only 1 million physical qubits 9.1.6. Which Industries Have Problems Quantum Computing Could Solve? 9.2. Automotive Applications of Quantum Computing 9.2.1. Quantum chemistry offers more accurate simulations to aid battery material discovery 9.2.2. Quantum machine learning could make image classification for vehicle autonomy more efficient 9.2.3. Quantum optimization for assembly line and distribution efficiency could save time, money, and energy 9.2.4. Most automotive players are pursuing quantum computing for battery chemistry 9.2.5. The automotive industry is yet to converge on a preferred qubit modality 9.2.6. Partnerships and collaborations for automotive quantum computing 9.2.7. Mercedes: Case study in remaining hardware agnostic 9.2.8. Tesla: Supercomputers not quantum computers 9.2.9. Summary of key conclusions 9.2.10. Analyst opinion on quantum computing for automotive 9.3. Finance Applications of Quantum Computing 9.3.1. Partnerships forming now will shape the future of quantum computing for the financial sector 9.3.2. Despite its early stage, preparing for quantum computing now is a key strategy in the finance industry (1) 9.3.3. Despite its early stage, preparing for quantum computing now is a key strategy in the finance industry (2) 9.3.4. Use cases of quantum computing in finance 9.3.5. HSBC and Quantum Key Distribution 9.3.6. Quantum key distribution – 4 challenges to adoption – BT 10. MATERIALS FOR QUANTUM COMPUTING 10.1.1. Chapter Overview 10.2. Superconductors 10.2.1. Overview of superconductors in quantum technology 10.2.2. Critical temperature plays a key role in superconductor material choice for quantum technology 10.2.3. Critical material chain considerations for superconducting quantum computing 10.2.4. Overview of the superconductor value chain in quantum technology 10.2.5. Room temperature superconductors – and why they won’t necessarily unlock the quantum technology market 10.2.6. Superconducting Nanowire Single Photon Detector (SNSPD) 10.3. Superconducting nanowire single photon detectors (SNSPDs) 10.3.1. SNSPD applications must value performance highly enough to justify the bulk/cost of cryogenics 10.3.2. Research in scaling SNSPD arrays beyond kilopixel 10.3.3. Advancements in superconducting materials drives SNSPD development 10.3.4. Comparison of commercial SNSPD players 10.3.5. SWOT analysis: Superconducting nanowire single photon detectors (SNSPDs) 10.3.6. Kinetic Inductance Detector (KID) and Transition Edge Sensor (TES) 10.4. Kinetic inductance detectors (KIDs) 10.4.1. Transition edge sensors (TES) 10.4.2. How have SNSPDs gained traction while KIDs and TESs remain in research? 10.4.3. Comparison of single photon detector technology 10.5. Photonics, Silicon Photonics and Optical Components 10.5.1. Overview of photonics, silicon photonics and optics in quantum technology 10.5.2. Overview of material considerations for photonic integrated circuits (PICs) 10.5.3. Photonic computing demands better electro-optical materials, alternatives to standard silicon and warmer superconductors than niobium (1) 10.5.4. Photonic computing demands better electro-optical materials, alternatives to standard silicon and warmer superconductors than niobium (2) 10.5.5. An opportunity for better optical fiber and quantum interconnects materials 10.6. Semiconductor Single Photon Detectors 10.6.1. Introduction to semiconductor photon detectors 10.6.2. Operating principles of SPADs: Avalanche photodiode (APD) basics 10.6.3. Operating principles of single-photon avalanche diodes (SPADs) 10.6.4. Arrays of SPADs in series can form silicon photomultipliers (SiPMs) as a solid-state alternative to traditional PMTs 10.6.5. Innovation in the next generation of SPADs 10.6.6. Key players and innovators in the next generation of SPADs 10.6.7. Applications of SPADs formed in a trade-off of resolution and performance 10.6.8. Development trends for groups of key SPAD players 10.6.9. Advanced semiconductor packaging techniques enabling higher pixel counts and timing functionality for SPAD arrays 10.6.10. Alternative semiconductor SPADs unlock infrared wavelengths beyond the range of silicon (1) 10.6.11. Alternative semiconductor SPADs unlock infrared wavelengths beyond the range of silicon (2) 10.6.12. Competition or cooperation for SPADs and SNSPDs in quantum communications and computing? 10.6.13. Emerging SPADs: SWOT analysis 10.7. Nanomaterials (Graphene, CNTs, Diamond and MOFs) 10.7.1. Introduction to 2D Materials for Quantum Technology 10.7.2. Interest in TMD based quantum dots as single photon sources for quantum networking 10.7.3. Introduction to graphene membranes 10.7.4. Research interest in graphene membranes for RAM memory in quantum computers 10.7.5. 2.5D Materials pitches as solution to quantum information storage 10.7.6. Single Walled Carbon Nanotubes for Quantum Computers 10.7.7. Long term potential in the quantum materials market for Boron Nitride Nanotubes (BNNT) 10.7.8. Snapshot of market readiness levels of CNT applications – quantum only at PoC stage 10.7.9. Overview of diamond in quantum technology 10.7.10. Material advantages and disadvantages of diamond for quantum applications 10.7.11. Element Six are leaders in scaling up manufacturing of diamond for quantum applications using chemical vapor deposition (CVD) 10.7.12. Overview of the synthetic diamond value chain in quantum technology 10.7.13. Chromophore integrated MOFs can stabilize qubits at room temperature for quantum computing 10.7.14. Conclusions and outlook: Materials opportunities in quantum computing 11. COMPANY PROFILES 11.1. Aegiq 11.2. BlueFors (Helium) 11.3. Classiq 11.4. D-Wave 11.5. Diatope 11.6. Diraq 11.7. Element Six (Quantum Technologies) 11.8. Hitachi Cambridge Laboratory (HCL) 11.9. IBM (Quantum Computing) 11.10. Infineon (Quantum Algorithms) 11.11. Infleqtion (Cold Quanta) 11.12. IonQ 11.13. IQM 11.14. Microsoft Quantum 11.15. nu quantum 11.16. ORCA Computing 11.17. Oxford Ionics 11.18. Oxford Quantum Circuits 11.19. Pasqal 11.20. Photon Force 11.21. Powerlase Ltd 11.22. PsiQuantum 11.23. Q.ANT 11.24. Qilimanjaro Quantum Tech 11.25. Quantinuum 11.26. QuantrolOx 11.27. Quantum Brilliance 11.28. Quantum Computing Inc 11.29. Quantum Economic Development Consortium (QED-C) 11.30. Quantum Motion 11.31. Quantum XChange 11.32. QuEra 11.33. QuiX Quantum 11.34. Rigetti 11.35. Riverlane 11.36. Schrödinger Update: Batteries and Materials Informatics 11.37. SEEQC 11.38. SemiWise 11.39. Senko Advance Components Ltd 11.40. Single Quantum 11.41. Siquance 11.42. TE Connectivity: Connectors for Quantum Computing 11.43. VTT Manufacturing (Quantum Technologies) 11.44. XeedQ