{"id":264248,"date":"2025-07-14T12:49:09","date_gmt":"2025-07-14T12:49:09","guid":{"rendered":"https:\/\/www.europesays.com\/uk\/264248\/"},"modified":"2025-07-14T12:49:09","modified_gmt":"2025-07-14T12:49:09","slug":"technology-trends-players-forecasts-idtechex","status":"publish","type":"post","link":"https:\/\/www.europesays.com\/uk\/264248\/","title":{"rendered":"Technology, Trends, Players, Forecasts: IDTechEx"},"content":{"rendered":"

<\/p>\n\n1.<\/td>\nEXECUTIVE SUMMARY<\/td>\n<\/tr>\n\n1.1.<\/td>\nThe state of the quantum computing market: analyst opinion<\/td>\n<\/tr>\n\n1.2.<\/td>\nIntroduction to quantum computers<\/td>\n<\/tr>\n\n1.3.<\/td>\nWhich Industries Have Problems Quantum Computing Could Solve?<\/td>\n<\/tr>\n\n1.4.<\/td>\nData centers complement the quantum as a service (QaaS) business model<\/td>\n<\/tr>\n\n1.5.<\/td>\nThe market for quantum computing hardware could be worth over US$21 billion by 2046, with a CAGR of 26.7%<\/td>\n<\/tr>\n\n1.6.<\/td>\nNational facilities are early customers of on-premises quantum computers<\/td>\n<\/tr>\n\n1.7.<\/td>\nFour major challenges for quantum hardware<\/td>\n<\/tr>\n\n1.8.<\/td>\nBlueprint for a quantum computer: Qubits, initialization, readout, manipulation<\/td>\n<\/tr>\n\n1.9.<\/td>\nHow is the industry benchmarked?<\/td>\n<\/tr>\n\n1.10.<\/td>\nIntroduction to the IDTechEx Quantum Commercial Readiness Level (QCRL)<\/td>\n<\/tr>\n\n1.11.<\/td>\nRoadmap for quantum commercial readiness level (QCRL) over time<\/td>\n<\/tr>\n\n1.12.<\/td>\nPredicting the tipping point for quantum computing<\/td>\n<\/tr>\n\n1.13.<\/td>\nDemand for quantum computer hardware will lag user number<\/td>\n<\/tr>\n\n1.14.<\/td>\nThe number of companies commercializing quantum computers rapidly grew over the last 15 years<\/td>\n<\/tr>\n\n1.15.<\/td>\nSummarizing the promises and challenges of leading quantum hardware<\/td>\n<\/tr>\n\n1.16.<\/td>\nSummarizing the promises and challenges of alternative quantum hardware<\/td>\n<\/tr>\n\n1.17.<\/td>\nCompeting quantum computer architectures: summary table<\/td>\n<\/tr>\n\n1.18.<\/td>\nRoadmap for quantum commercial readiness level (QCRL) by technology<\/td>\n<\/tr>\n\n1.19.<\/td>\nForecast for installed based of quantum computers by technology, 2026-2046<\/td>\n<\/tr>\n\n1.20.<\/td>\nEmergence of the mixed quantum stack<\/td>\n<\/tr>\n\n1.21.<\/td>\nInfrastructure pain points are near universal for quantum computers<\/td>\n<\/tr>\n\n1.22.<\/td>\nWhere will quantum computers be deployed?<\/td>\n<\/tr>\n\n1.23.<\/td>\nWhat is a platform for quantum computing?<\/td>\n<\/tr>\n\n1.24.<\/td>\nHyperscalers position themselves as platform enablers<\/td>\n<\/tr>\n\n1.25.<\/td>\nQuantum for AI, AI for Quantum, or Quantum vs AI?<\/td>\n<\/tr>\n\n1.26.<\/td>\nWhat will be the first “killer application” for quantum computing?<\/td>\n<\/tr>\n\n1.27.<\/td>\nSummary of materials opportunities in quantum computing<\/td>\n<\/tr>\n\n1.28.<\/td>\n2025 Updates from Key Players and Market Shifts<\/td>\n<\/tr>\n\n1.29.<\/td>\nMicrosoft’s domestic quantum effort – Majorana 1<\/td>\n<\/tr>\n\n1.30.<\/td>\nIBM: Roadmap to 100 million gates by 2029<\/td>\n<\/tr>\n\n1.31.<\/td>\nGoogle Quantum AI study suggests RSA could be broken with only 1 million physical qubits<\/td>\n<\/tr>\n\n1.32.<\/td>\nRigetti develops a tiled chip approach & moves towards mixed stack<\/td>\n<\/tr>\n\n1.33.<\/td>\nIQM complete over a dozen sales<\/td>\n<\/tr>\n\n1.34.<\/td>\nOxford Quantum Circuits release new roadmap targeting early commercial advantage in 2028<\/td>\n<\/tr>\n\n1.35.<\/td>\nZuchongzhi 3.0 rivals the performance of leading quantum hardware<\/td>\n<\/tr>\n\n1.36.<\/td>\nQuantinuum: Growing quantum volume and commercial partnerships<\/td>\n<\/tr>\n\n1.37.<\/td>\nIonQ acquires Oxford Ionics for a record US$1.08 billion<\/td>\n<\/tr>\n\n1.38.<\/td>\nIonQ makes a spree of acquisitions including Oxford Ionics<\/td>\n<\/tr>\n\n1.39.<\/td>\nOxford Ionics reveals development roadmap<\/td>\n<\/tr>\n\n1.40.<\/td>\nInfleqtion aim to reduce qubit overhead in neutral atom error correction<\/td>\n<\/tr>\n\n1.41.<\/td>\nPasqal targets 200 logical qubits by 2029 and acquires PIC specialist<\/td>\n<\/tr>\n\n1.42.<\/td>\nPsiQuantum reveals new chipset “Omega”<\/td>\n<\/tr>\n\n1.43.<\/td>\nORCA Computing: Towards practical quantum accelerators<\/td>\n<\/tr>\n\n1.44.<\/td>\nQuantum Brilliance: HPC integration & mobile quantum processors<\/td>\n<\/tr>\n\n1.45.<\/td>\nRiverlane commercializes hardware for quantum error correction<\/td>\n<\/tr>\n\n1.46.<\/td>\nMain conclusions (I)<\/td>\n<\/tr>\n\n1.47.<\/td>\nMain conclusions (II)<\/td>\n<\/tr>\n\n1.48.<\/td>\nKey market shifts for specific qubit modalities in the last 12 months<\/td>\n<\/tr>\n\n1.49.<\/td>\nAccess more with an IDTechEx subscription<\/td>\n<\/tr>\n\n2.<\/td>\nINTRODUCTION TO QUANTUM COMPUTING<\/td>\n<\/tr>\n\n2.1.1.<\/td>\nChapter overview<\/td>\n<\/tr>\n\n2.2.<\/td>\nSector Overview<\/td>\n<\/tr>\n\n2.2.1.<\/td>\nIntroduction to quantum computers<\/td>\n<\/tr>\n\n2.2.2.<\/td>\nInvestment in quantum computing is growing<\/td>\n<\/tr>\n\n2.2.3.<\/td>\nThe quantum ecosystem is growing and covers a variety of approaches<\/td>\n<\/tr>\n\n2.2.4.<\/td>\nThe business model for quantum computing – quantum as a service (QaaS)<\/td>\n<\/tr>\n\n2.2.5.<\/td>\nValue capture in quantum computing<\/td>\n<\/tr>\n\n2.2.6.<\/td>\nCommercial partnership is driver for growth and a tool for technology development<\/td>\n<\/tr>\n\n2.2.7.<\/td>\nBusiness model trends: vertically integrated vs. the ‘quantum stack’<\/td>\n<\/tr>\n\n2.2.8.<\/td>\nEmergence of the mixed quantum stack<\/td>\n<\/tr>\n\n2.2.9.<\/td>\nFour major challenges for quantum hardware<\/td>\n<\/tr>\n\n2.2.10.<\/td>\nShortage of quantum talent is a challenge for the industry<\/td>\n<\/tr>\n\n2.2.11.<\/td>\nCompeting forces in the communication of quantum computing<\/td>\n<\/tr>\n\n2.3.<\/td>\nNational Programs and Initiatives<\/td>\n<\/tr>\n\n2.3.1.<\/td>\nQuantum computing as a national strategic resource<\/td>\n<\/tr>\n\n2.3.2.<\/td>\nNational facilities are early customers of on-premises quantum computers<\/td>\n<\/tr>\n\n2.3.3.<\/td>\nGovernment funding in the US, China, and Europe is driving the commercializing of quantum technologies<\/td>\n<\/tr>\n\n2.3.4.<\/td>\nUSA National Quantum Initiative aims to accelerate research and economic development<\/td>\n<\/tr>\n\n2.3.5.<\/td>\nDARPA Quantum Benchmarking Initiative<\/td>\n<\/tr>\n\n2.3.6.<\/td>\nQuantum Economic Development Consortium (QED-C)<\/td>\n<\/tr>\n\n2.3.7.<\/td>\nNATO announced first quantum strategy in 2024<\/td>\n<\/tr>\n\n2.3.8.<\/td>\nThe UK National Quantum Technologies Program<\/td>\n<\/tr>\n\n2.3.9.<\/td>\nUK strategy update: NQCC and NQTP receive more support<\/td>\n<\/tr>\n\n2.3.10.<\/td>\nUK strategy update: Partnerships and London Quantum Technology Cluster<\/td>\n<\/tr>\n\n2.3.11.<\/td>\nEleven quantum technology innovation hubs now established in Japan<\/td>\n<\/tr>\n\n2.3.12.<\/td>\nQuantum in South Korea: Ambitions to become a global leader in the 2030s<\/td>\n<\/tr>\n\n2.3.13.<\/td>\nQuantum in Australia: Creating clear benchmarks of national quantum eco-system success<\/td>\n<\/tr>\n\n2.3.14.<\/td>\nCollaboration versus quantum nationalism<\/td>\n<\/tr>\n\n2.4.<\/td>\nTechnical Primer<\/td>\n<\/tr>\n\n2.4.1.<\/td>\nClassical vs. Quantum<\/td>\n<\/tr>\n\n2.4.2.<\/td>\nSuperposition, entanglement, and observation<\/td>\n<\/tr>\n\n2.4.3.<\/td>\nClassical computers are built on binary logic<\/td>\n<\/tr>\n\n2.4.4.<\/td>\nQuantum computers replace binary bits with qubits<\/td>\n<\/tr>\n\n2.4.5.<\/td>\nBlueprint for a quantum computer: qubits, initialization, readout, manipulation<\/td>\n<\/tr>\n\n2.4.6.<\/td>\nCase study: Shor’s algorithm<\/td>\n<\/tr>\n\n2.4.7.<\/td>\nChapter summary – introduction to quantum computing<\/td>\n<\/tr>\n\n3.<\/td>\nBENCHMARKING QUANTUM HARDWARE<\/td>\n<\/tr>\n\n3.1.1.<\/td>\nChapter overview<\/td>\n<\/tr>\n\n3.2.<\/td>\nQubit Benchmarking<\/td>\n<\/tr>\n\n3.2.1.<\/td>\nNoise effects on qubits<\/td>\n<\/tr>\n\n3.2.2.<\/td>\nComparing coherence times<\/td>\n<\/tr>\n\n3.2.3.<\/td>\nQubit fidelity and error rate<\/td>\n<\/tr>\n\n3.3.<\/td>\nQuantum Computer Benchmarking<\/td>\n<\/tr>\n\n3.3.1.<\/td>\nQuantum supremacy and qubit number<\/td>\n<\/tr>\n\n3.3.2.<\/td>\nLogical qubits and error correction<\/td>\n<\/tr>\n\n3.3.3.<\/td>\nIntroduction to quantum volume<\/td>\n<\/tr>\n\n3.3.4.<\/td>\nError rate and quantum volume<\/td>\n<\/tr>\n\n3.3.5.<\/td>\nSquare circuit tests for quantum volume<\/td>\n<\/tr>\n\n3.3.6.<\/td>\nCritical appraisal of the importance of quantum volume<\/td>\n<\/tr>\n\n3.3.7.<\/td>\nIonQ introduces algorithmic qubits<\/td>\n<\/tr>\n\n3.3.8.<\/td>\nCompanies defining their own benchmarks<\/td>\n<\/tr>\n\n3.3.9.<\/td>\nOperational speed and CLOPS (circuit layer operations per second)<\/td>\n<\/tr>\n\n3.3.10.<\/td>\nConclusions: determining what makes a good computer is hard, and a quantum computer even harder<\/td>\n<\/tr>\n\n3.3.11.<\/td>\nConclusions: the logical qubit era and returns on investment<\/td>\n<\/tr>\n\n3.4.<\/td>\nIndustry Benchmarking<\/td>\n<\/tr>\n\n3.4.1.<\/td>\nThe DiVincenzo criteria<\/td>\n<\/tr>\n\n3.4.2.<\/td>\nCompeting quantum computer architectures: Summary table<\/td>\n<\/tr>\n\n3.4.3.<\/td>\nIDTechEx – Quantum commercial readiness level (QCRL)<\/td>\n<\/tr>\n\n3.4.4.<\/td>\nQCRL scale (1-5, commercial application focused)<\/td>\n<\/tr>\n\n3.4.5.<\/td>\nQCRL scale (6-10, user-volume focused)<\/td>\n<\/tr>\n\n4.<\/td>\nMARKET FORECASTS<\/td>\n<\/tr>\n\n4.1.<\/td>\nForecasting Methodology Overview<\/td>\n<\/tr>\n\n4.2.<\/td>\nMethodology: roadmap for quantum commercial readiness level by technology<\/td>\n<\/tr>\n\n4.3.<\/td>\nRoadmap for quantum commercial readiness level (QCRL) over time<\/td>\n<\/tr>\n\n4.4.<\/td>\nMethodology: Establishing the total addressable market for quantum computing<\/td>\n<\/tr>\n\n4.5.<\/td>\nForecast for total addressable market for quantum computing<\/td>\n<\/tr>\n\n4.6.<\/td>\nPredicting cumulative demand for quantum computers over time (1)<\/td>\n<\/tr>\n\n4.7.<\/td>\nPredicting cumulative demand for quantum computers over time (2)<\/td>\n<\/tr>\n\n4.8.<\/td>\nForecast for installed base of quantum computers, 2026-2046<\/td>\n<\/tr>\n\n4.9.<\/td>\nForecast for annual volume of quantum computers, 2026-2046<\/td>\n<\/tr>\n\n4.10.<\/td>\nForecast for quantum computer pricing 2026-2046<\/td>\n<\/tr>\n\n4.11.<\/td>\nForecast for annual revenue from quantum computer hardware sales, 2026-2046<\/td>\n<\/tr>\n\n4.12.<\/td>\nForecast for installed based of quantum computers by technology, 2026-2046<\/td>\n<\/tr>\n\n4.13.<\/td>\nForecast for annual revenue from quantum computing hardware sales (breakdown by technology), 2026-2046<\/td>\n<\/tr>\n\n4.14.<\/td>\nComparing the install base of quantum computers to the global number of data centers<\/td>\n<\/tr>\n\n4.15.<\/td>\nForecast for the volume of quantum computers deployed in data centers, 2026-2046<\/td>\n<\/tr>\n\n4.16.<\/td>\nKey forecasting changes since the previous report<\/td>\n<\/tr>\n\n5.<\/td>\nCOMPETING QUANTUM COMPUTER ARCHITECTURES<\/td>\n<\/tr>\n\n5.1.1.<\/td>\nIntroduction to competing quantum computer architectures<\/td>\n<\/tr>\n\n5.2.<\/td>\nSuperconducting<\/td>\n<\/tr>\n\n5.2.1.<\/td>\nIntroduction to superconducting qubits (I)<\/td>\n<\/tr>\n\n5.2.2.<\/td>\nIntroduction to superconducting qubits (II)<\/td>\n<\/tr>\n\n5.2.3.<\/td>\nSuperconducting materials and critical temperature<\/td>\n<\/tr>\n\n5.2.4.<\/td>\nInitialization, manipulation, and readout<\/td>\n<\/tr>\n\n5.2.5.<\/td>\nSuperconducting quantum computer schematic<\/td>\n<\/tr>\n\n5.2.6.<\/td>\nComparing key players in superconducting quantum computing (hardware)<\/td>\n<\/tr>\n\n5.2.7.<\/td>\nIBM: roadmap to 100 million gates by 2029<\/td>\n<\/tr>\n\n5.2.8.<\/td>\nIQM release new roadmap promising quantum advantage by 2030<\/td>\n<\/tr>\n\n5.2.9.<\/td>\nIQM complete over a dozen sales and release product dimensions<\/td>\n<\/tr>\n\n5.2.10.<\/td>\nRigetti develops a tiled chip approach & moves towards mixed stack<\/td>\n<\/tr>\n\n5.2.11.<\/td>\nOxford Quantum Circuits release new roadmap targeting early commercial advantage in 2028<\/td>\n<\/tr>\n\n5.2.12.<\/td>\nZuchongzhi 3.0 rivals the performance of leading quantum hardware<\/td>\n<\/tr>\n\n5.2.13.<\/td>\nRoadmap for superconducting quantum hardware (chart)<\/td>\n<\/tr>\n\n5.2.14.<\/td>\nRoadmap for superconducting quantum hardware (discussion)<\/td>\n<\/tr>\n\n5.2.15.<\/td>\nSimplifying superconducting architecture requirements for scale-up<\/td>\n<\/tr>\n\n5.2.16.<\/td>\nCritical material chain considerations for superconducting quantum computing<\/td>\n<\/tr>\n\n5.2.17.<\/td>\nSWOT analysis: Superconducting quantum computers<\/td>\n<\/tr>\n\n5.2.18.<\/td>\nKey conclusions: Superconducting quantum computers<\/td>\n<\/tr>\n\n5.3.<\/td>\nTrapped Ion<\/td>\n<\/tr>\n\n5.3.1.<\/td>\nIntroduction to trapped-ion quantum computing<\/td>\n<\/tr>\n\n5.3.2.<\/td>\nInitialization, manipulation, and readout for trapped ion quantum computers<\/td>\n<\/tr>\n\n5.3.3.<\/td>\nMaterials challenges for a fully integrated trapped-ion chip<\/td>\n<\/tr>\n\n5.3.4.<\/td>\nComparing key players in trapped ion quantum computing (hardware)<\/td>\n<\/tr>\n\n5.3.5.<\/td>\nQuantinuum: Growing quantum volume and commercial partnerships<\/td>\n<\/tr>\n\n5.3.6.<\/td>\nIonQ acquires Oxford Ionics for a record US$1.08 billion<\/td>\n<\/tr>\n\n5.3.7.<\/td>\nIonQ makes a spree of acquisitions including Oxford Ionics<\/td>\n<\/tr>\n\n5.3.8.<\/td>\nOxford Ionics reveals development roadmap<\/td>\n<\/tr>\n\n5.3.9.<\/td>\nRoadmap for trapped-ion quantum computing hardware (chart)<\/td>\n<\/tr>\n\n5.3.10.<\/td>\nRoadmap for trapped-ion quantum computing hardware (discussion)<\/td>\n<\/tr>\n\n5.3.11.<\/td>\nSWOT analysis: Trapped-ion quantum computers<\/td>\n<\/tr>\n\n5.3.12.<\/td>\nKey conclusions: Trapped ion quantum computers<\/td>\n<\/tr>\n\n5.4.<\/td>\nPhotonic<\/td>\n<\/tr>\n\n5.4.1.<\/td>\nIntroduction to photonic qubits<\/td>\n<\/tr>\n\n5.4.2.<\/td>\nComparing photon polarization and squeezed states<\/td>\n<\/tr>\n\n5.4.3.<\/td>\nOverview of the photonic platform for quantum computing<\/td>\n<\/tr>\n\n5.4.4.<\/td>\nInitialization, manipulation, and readout of photonic quantum computers<\/td>\n<\/tr>\n\n5.4.5.<\/td>\nComparing key players in photonic quantum computing<\/td>\n<\/tr>\n\n5.4.6.<\/td>\nPsiQuantum receives over AU$1B in government investments and seeks a US$750M private funding round<\/td>\n<\/tr>\n\n5.4.7.<\/td>\nPsiQuantum reveals new chipset “Omega”<\/td>\n<\/tr>\n\n5.4.8.<\/td>\nAegiq – offering versatility without a universal machine<\/td>\n<\/tr>\n\n5.4.9.<\/td>\nRoadmap for photonic quantum hardware (chart)<\/td>\n<\/tr>\n\n5.4.10.<\/td>\nRoadmap for photonic quantum hardware (discussion)<\/td>\n<\/tr>\n\n5.4.11.<\/td>\nSWOT analysis: Photonic quantum computers<\/td>\n<\/tr>\n\n5.4.12.<\/td>\nKey conclusions: Photonic quantum computers<\/td>\n<\/tr>\n\n5.5.<\/td>\nSilicon Spin<\/td>\n<\/tr>\n\n5.5.1.<\/td>\nIntroduction to silicon-spin qubits<\/td>\n<\/tr>\n\n5.5.2.<\/td>\nQubits from quantum dots – ‘hot’ qubits are still pretty cold<\/td>\n<\/tr>\n\n5.5.3.<\/td>\nCMOS readout using resonators offers a speed advantage<\/td>\n<\/tr>\n\n5.5.4.<\/td>\nThe advantage of silicon-spin is in the scale not the temperature<\/td>\n<\/tr>\n\n5.5.5.<\/td>\nInitialization, manipulation, and readout<\/td>\n<\/tr>\n\n5.5.6.<\/td>\nComparing key players in silicon spin quantum computing<\/td>\n<\/tr>\n\n5.5.7.<\/td>\nBig chip makers are advancing their quantum computing capabilities<\/td>\n<\/tr>\n\n5.5.8.<\/td>\nRoadmap for silicon-spin quantum computing hardware (chart)<\/td>\n<\/tr>\n\n5.5.9.<\/td>\nRoadmap for silicon-spin (discussion)<\/td>\n<\/tr>\n\n5.5.10.<\/td>\nSWOT analysis: Silicon-spin quantum computers<\/td>\n<\/tr>\n\n5.5.11.<\/td>\nKey conclusions: Silicon-spin quantum computers<\/td>\n<\/tr>\n\n5.6.<\/td>\nNeutral Atom (Cold Atom)<\/td>\n<\/tr>\n\n5.6.1.<\/td>\nIntroduction to neutral atom quantum computing<\/td>\n<\/tr>\n\n5.6.2.<\/td>\nEntanglement via Rydberg states in Rubidium\/Strontium<\/td>\n<\/tr>\n\n5.6.3.<\/td>\nInitialization, manipulation and readout for neutral-atom quantum computers<\/td>\n<\/tr>\n\n5.6.4.<\/td>\nComparing key players in neutral atom quantum computing (hardware)<\/td>\n<\/tr>\n\n5.6.5.<\/td>\nQuEra completes US$230 million funding round including Google investment<\/td>\n<\/tr>\n\n5.6.6.<\/td>\nAtom Computing partner with Microsoft<\/td>\n<\/tr>\n\n5.6.7.<\/td>\nPasqal targets 200 logical qubits by 2029 and acquires PIC specialist<\/td>\n<\/tr>\n\n5.6.8.<\/td>\nInfleqtion aim to reduce qubit overhead in neutral atom error correction<\/td>\n<\/tr>\n\n5.6.9.<\/td>\nRoadmap for neutral-atom quantum computing hardware (chart)<\/td>\n<\/tr>\n\n5.6.10.<\/td>\nRoadmap for neutral-atom quantum computing hardware (discussion)<\/td>\n<\/tr>\n\n5.6.11.<\/td>\nSWOT analysis: Neutral-atom quantum computers<\/td>\n<\/tr>\n\n5.6.12.<\/td>\nKey conclusions: Neutral atom quantum computers<\/td>\n<\/tr>\n\n5.7.<\/td>\nDiamond Defect<\/td>\n<\/tr>\n\n5.7.1.<\/td>\nIntroduction to diamond-defect spin-based computing<\/td>\n<\/tr>\n\n5.7.2.<\/td>\nLack of complex infrastructure for diamond defect hardware enables early-stage MVPs<\/td>\n<\/tr>\n\n5.7.3.<\/td>\nSupply chain and materials for diamond-defect spin-based computers<\/td>\n<\/tr>\n\n5.7.4.<\/td>\nComparing key players in diamond defect quantum computing<\/td>\n<\/tr>\n\n5.7.5.<\/td>\nQuantum Brilliance offer lower power quantum solutions for data centers in the near term, and opportunities on the edge long term<\/td>\n<\/tr>\n\n5.7.6.<\/td>\nQuantum Brilliance: HPC integration & mobile quantum processors<\/td>\n<\/tr>\n\n5.7.7.<\/td>\nRoadmap for diamond defect quantum computing hardware (chart)<\/td>\n<\/tr>\n\n5.7.8.<\/td>\nRoadmap for diamond-defect based quantum computers (discussion)<\/td>\n<\/tr>\n\n5.7.9.<\/td>\nSWOT analysis: Diamond-defect quantum computers<\/td>\n<\/tr>\n\n5.7.10.<\/td>\nKey conclusions: Diamond-defect quantum computers<\/td>\n<\/tr>\n\n5.8.<\/td>\nTopological Qubits (Majorana)<\/td>\n<\/tr>\n\n5.8.1.<\/td>\nTopological qubits (Majorana modes)<\/td>\n<\/tr>\n\n5.8.2.<\/td>\nInitialization, manipulation, and readout of topological qubits<\/td>\n<\/tr>\n\n5.8.3.<\/td>\nMicrosoft are the primary company pursuing topological qubits<\/td>\n<\/tr>\n\n5.8.4.<\/td>\nMicrosoft’s domestic quantum effort – Majorana 1<\/td>\n<\/tr>\n\n5.8.5.<\/td>\nScaling up arrays of topological qubits<\/td>\n<\/tr>\n\n5.8.6.<\/td>\nRoadmap for topological quantum computing hardware (chart)<\/td>\n<\/tr>\n\n5.8.7.<\/td>\nRoadmap for topological quantum computing hardware (discussion)<\/td>\n<\/tr>\n\n5.8.8.<\/td>\nSWOT analysis: Topological qubits<\/td>\n<\/tr>\n\n5.8.9.<\/td>\nKey conclusions: Topological qubits<\/td>\n<\/tr>\n\n5.9.<\/td>\nQuantum Annealers<\/td>\n<\/tr>\n\n5.9.1.<\/td>\nIntroduction to quantum annealers<\/td>\n<\/tr>\n\n5.9.2.<\/td>\nHow do quantum processors for annealing work?<\/td>\n<\/tr>\n\n5.9.3.<\/td>\nInitialization and readout of quantum annealers<\/td>\n<\/tr>\n\n5.9.4.<\/td>\nAnnealing is best suited to optimization problems<\/td>\n<\/tr>\n\n5.9.5.<\/td>\nCommercial examples of use-cases for annealing<\/td>\n<\/tr>\n\n5.9.6.<\/td>\nClarity on annealing related terms<\/td>\n<\/tr>\n\n5.9.7.<\/td>\nComparing key players in quantum annealing<\/td>\n<\/tr>\n\n5.9.8.<\/td>\nD-Wave intensifies focus on increasing production application deployments<\/td>\n<\/tr>\n\n5.9.9.<\/td>\nQilimanjaro develops analog QASIC chips & target QaaS by EoY<\/td>\n<\/tr>\n\n5.9.10.<\/td>\nRoadmap for neutral-atom quantum computing hardware (chart)<\/td>\n<\/tr>\n\n5.9.11.<\/td>\nRoadmap for quantum annealing hardware (discussion)<\/td>\n<\/tr>\n\n5.9.12.<\/td>\nSWOT analysis: Quantum annealers<\/td>\n<\/tr>\n\n5.9.13.<\/td>\nKey conclusions: Quantum annealers<\/td>\n<\/tr>\n\n5.10.<\/td>\nChapter Summary<\/td>\n<\/tr>\n\n5.10.1.<\/td>\nSummarizing the promises and challenges of leading quantum hardware<\/td>\n<\/tr>\n\n5.10.2.<\/td>\nSummarizing the promises and challenges of alternative quantum hardware<\/td>\n<\/tr>\n\n5.10.3.<\/td>\nCompeting quantum computer architectures: Summary table<\/td>\n<\/tr>\n\n5.10.4.<\/td>\nMain conclusions (I)<\/td>\n<\/tr>\n\n5.10.5.<\/td>\nMain conclusions (II)<\/td>\n<\/tr>\n\n5.10.6.<\/td>\nKey market shifts for specific qubit modalities in the last 12 months<\/td>\n<\/tr>\n\n6.<\/td>\nINFRASTRUCTURE FOR QUANTUM COMPUTING<\/td>\n<\/tr>\n\n6.1.<\/td>\nChapter overview<\/td>\n<\/tr>\n\n6.2.<\/td>\nInfrastructure trends: Modular vs. single core<\/td>\n<\/tr>\n\n6.3.<\/td>\nHardware agnostic infrastructure platforms for quantum computing represent a new market for established technologies<\/td>\n<\/tr>\n\n6.4.<\/td>\nIntroduction to cryostats for quantum computing<\/td>\n<\/tr>\n\n6.5.<\/td>\nBluefors are the market leaders in cryostat supply for superconducting quantum computers (chart)<\/td>\n<\/tr>\n\n6.6.<\/td>\nBluefors are the market leaders in cryostat supply for superconducting quantum computers (discussion)<\/td>\n<\/tr>\n\n6.7.<\/td>\nOpportunities in the Asian supply chain for cryostats<\/td>\n<\/tr>\n\n6.8.<\/td>\nCryostats need two forms of helium, with different supply chain considerations<\/td>\n<\/tr>\n\n6.9.<\/td>\nRare Helium-3 supplies could prove decisive for quantum ecosystems<\/td>\n<\/tr>\n\n6.10.<\/td>\nSummary of cabling and electronics requirements inside a dilution refrigerator for quantum computing<\/td>\n<\/tr>\n\n6.11.<\/td>\nQubit readout methods: Microwaves and microscopes<\/td>\n<\/tr>\n\n6.12.<\/td>\nPain points for incumbent platform solutions<\/td>\n<\/tr>\n\n7.<\/td>\nDEPLOYMENT OF QUANTUM COMPUTERS<\/td>\n<\/tr>\n\n7.1.1.<\/td>\nWhere will quantum computers be deployed?<\/td>\n<\/tr>\n\n7.1.2.<\/td>\nShould deployed quantum computers be ‘hands on’ or ‘hands off’?<\/td>\n<\/tr>\n\n7.1.3.<\/td>\nHPC integration of quantum computers<\/td>\n<\/tr>\n\n7.1.4.<\/td>\nChallenges in the delivery and commissioning of quantum computers<\/td>\n<\/tr>\n\n7.1.5.<\/td>\nCase study: Potential sources of disruption in a quantum computing environment and the sensors used to monitor them – IQM<\/td>\n<\/tr>\n\n7.2.<\/td>\nQuantum Computing in Data Centers<\/td>\n<\/tr>\n\n7.2.1.<\/td>\nData centers are key partners for quantum hardware developers to reach more customers<\/td>\n<\/tr>\n\n7.2.2.<\/td>\nData centers complement the quantum as a service (QaaS) business model<\/td>\n<\/tr>\n\n7.2.3.<\/td>\nHyperscalers position themselves as platform enablers<\/td>\n<\/tr>\n\n7.2.4.<\/td>\nWhat is a platform for quantum computing?<\/td>\n<\/tr>\n\n7.2.5.<\/td>\nOCP Ready for Quantum<\/td>\n<\/tr>\n\n7.2.6.<\/td>\nFundamental principle of cooling systems is similar in data centers and (cryogenically cooled) quantum computers (part 1)<\/td>\n<\/tr>\n\n7.2.7.<\/td>\nHowever different orders of magnitude of cooling are required in data centers and quantum computers (part 2)<\/td>\n<\/tr>\n\n7.2.8.<\/td>\nEnergy consumption of cooling systems – classical<\/td>\n<\/tr>\n\n7.2.9.<\/td>\nEnergy consumption of cooling systems – quantum<\/td>\n<\/tr>\n\n7.2.10.<\/td>\nComparing the energy consumption of quantum and classical computers<\/td>\n<\/tr>\n\n7.2.11.<\/td>\nPower demand from data centers will increase significantly over the coming decade<\/td>\n<\/tr>\n\n7.2.12.<\/td>\nKey takeaways for the data center industry<\/td>\n<\/tr>\n\n8.<\/td>\nQUANTUM COMPUTING AND AI<\/td>\n<\/tr>\n\n8.1.<\/td>\nQuantum for AI, AI for Quantum, or Quantum vs AI?<\/td>\n<\/tr>\n\n8.2.<\/td>\nUse cases for AI in quantum computing<\/td>\n<\/tr>\n\n8.3.<\/td>\nAI tools could assist in interfacing with quantum machines<\/td>\n<\/tr>\n\n8.4.<\/td>\nCompetition with advancements in classical computing<\/td>\n<\/tr>\n\n8.5.<\/td>\nTwo of China’s tech giants move away from quantum and towards AI<\/td>\n<\/tr>\n\n8.6.<\/td>\nNVIDIA & quantum computing: NVAQC and Quantum Cloud<\/td>\n<\/tr>\n\n8.7.<\/td>\nORCA Computing: Quantum processors for machine learning<\/td>\n<\/tr>\n\n8.8.<\/td>\nWill quantum computers improve or worsen global energy and technology inequality?<\/td>\n<\/tr>\n\n8.9.<\/td>\nConclusion – are quantum and AI allies or competitors?<\/td>\n<\/tr>\n\n9.<\/td>\nAPPLICATIONS OF QUANTUM COMPUTING<\/td>\n<\/tr>\n\n9.1.<\/td>\nOverview of Key Applications<\/td>\n<\/tr>\n\n9.1.1.<\/td>\nChapter overview – applications of quantum computing<\/td>\n<\/tr>\n\n9.1.2.<\/td>\nWhat will be the first “killer application” for quantum computing? (Part 1)<\/td>\n<\/tr>\n\n9.1.3.<\/td>\nWhat will be the first “killer application” for quantum computing? (Part 2)<\/td>\n<\/tr>\n\n9.1.4.<\/td>\n‘Hack Now Decrypt Later’ (HNDL) and preparing for Q-Day\/Y2Q<\/td>\n<\/tr>\n\n9.1.5.<\/td>\nGoogle Quantum AI study suggests RSA could be broken with only 1 million physical qubits<\/td>\n<\/tr>\n\n9.1.6.<\/td>\nWhich Industries Have Problems Quantum Computing Could Solve?<\/td>\n<\/tr>\n\n9.2.<\/td>\nAutomotive Applications of Quantum Computing<\/td>\n<\/tr>\n\n9.2.1.<\/td>\nQuantum chemistry offers more accurate simulations to aid battery material discovery<\/td>\n<\/tr>\n\n9.2.2.<\/td>\nQuantum machine learning could make image classification for vehicle autonomy more efficient<\/td>\n<\/tr>\n\n9.2.3.<\/td>\nQuantum optimization for assembly line and distribution efficiency could save time, money, and energy<\/td>\n<\/tr>\n\n9.2.4.<\/td>\nMost automotive players are pursuing quantum computing for battery chemistry<\/td>\n<\/tr>\n\n9.2.5.<\/td>\nThe automotive industry is yet to converge on a preferred qubit modality<\/td>\n<\/tr>\n\n9.2.6.<\/td>\nPartnerships and collaborations for automotive quantum computing<\/td>\n<\/tr>\n\n9.2.7.<\/td>\nMercedes: Case study in remaining hardware agnostic<\/td>\n<\/tr>\n\n9.2.8.<\/td>\nTesla: Supercomputers not quantum computers<\/td>\n<\/tr>\n\n9.2.9.<\/td>\nSummary of key conclusions<\/td>\n<\/tr>\n\n9.2.10.<\/td>\nAnalyst opinion on quantum computing for automotive<\/td>\n<\/tr>\n\n9.3.<\/td>\nFinance Applications of Quantum Computing<\/td>\n<\/tr>\n\n9.3.1.<\/td>\nPartnerships forming now will shape the future of quantum computing for the financial sector<\/td>\n<\/tr>\n\n9.3.2.<\/td>\nDespite its early stage, preparing for quantum computing now is a key strategy in the finance industry (1)<\/td>\n<\/tr>\n\n9.3.3.<\/td>\nDespite its early stage, preparing for quantum computing now is a key strategy in the finance industry (2)<\/td>\n<\/tr>\n\n9.3.4.<\/td>\nUse cases of quantum computing in finance<\/td>\n<\/tr>\n\n9.3.5.<\/td>\nHSBC and Quantum Key Distribution<\/td>\n<\/tr>\n\n9.3.6.<\/td>\nQuantum key distribution – 4 challenges to adoption – BT<\/td>\n<\/tr>\n\n10.<\/td>\nMATERIALS FOR QUANTUM COMPUTING<\/td>\n<\/tr>\n\n10.1.1.<\/td>\nChapter Overview<\/td>\n<\/tr>\n\n10.2.<\/td>\nSuperconductors<\/td>\n<\/tr>\n\n10.2.1.<\/td>\nOverview of superconductors in quantum technology<\/td>\n<\/tr>\n\n10.2.2.<\/td>\nCritical temperature plays a key role in superconductor material choice for quantum technology<\/td>\n<\/tr>\n\n10.2.3.<\/td>\nCritical material chain considerations for superconducting quantum computing<\/td>\n<\/tr>\n\n10.2.4.<\/td>\nOverview of the superconductor value chain in quantum technology<\/td>\n<\/tr>\n\n10.2.5.<\/td>\nRoom temperature superconductors – and why they won’t necessarily unlock the quantum technology market<\/td>\n<\/tr>\n\n10.2.6.<\/td>\nSuperconducting Nanowire Single Photon Detector (SNSPD)<\/td>\n<\/tr>\n\n10.3.<\/td>\nSuperconducting nanowire single photon detectors (SNSPDs)<\/td>\n<\/tr>\n\n10.3.1.<\/td>\nSNSPD applications must value performance highly enough to justify the bulk\/cost of cryogenics<\/td>\n<\/tr>\n\n10.3.2.<\/td>\nResearch in scaling SNSPD arrays beyond kilopixel<\/td>\n<\/tr>\n\n10.3.3.<\/td>\nAdvancements in superconducting materials drives SNSPD development<\/td>\n<\/tr>\n\n10.3.4.<\/td>\nComparison of commercial SNSPD players<\/td>\n<\/tr>\n\n10.3.5.<\/td>\nSWOT analysis: Superconducting nanowire single photon detectors (SNSPDs)<\/td>\n<\/tr>\n\n10.3.6.<\/td>\nKinetic Inductance Detector (KID) and Transition Edge Sensor (TES)<\/td>\n<\/tr>\n\n10.4.<\/td>\nKinetic inductance detectors (KIDs)<\/td>\n<\/tr>\n\n10.4.1.<\/td>\nTransition edge sensors (TES)<\/td>\n<\/tr>\n\n10.4.2.<\/td>\nHow have SNSPDs gained traction while KIDs and TESs remain in research?<\/td>\n<\/tr>\n\n10.4.3.<\/td>\nComparison of single photon detector technology<\/td>\n<\/tr>\n\n10.5.<\/td>\nPhotonics, Silicon Photonics and Optical Components<\/td>\n<\/tr>\n\n10.5.1.<\/td>\nOverview of photonics, silicon photonics and optics in quantum technology<\/td>\n<\/tr>\n\n10.5.2.<\/td>\nOverview of material considerations for photonic integrated circuits (PICs)<\/td>\n<\/tr>\n\n10.5.3.<\/td>\nPhotonic computing demands better electro-optical materials, alternatives to standard silicon and warmer superconductors than niobium (1)<\/td>\n<\/tr>\n\n10.5.4.<\/td>\nPhotonic computing demands better electro-optical materials, alternatives to standard silicon and warmer superconductors than niobium (2)<\/td>\n<\/tr>\n\n10.5.5.<\/td>\nAn opportunity for better optical fiber and quantum interconnects materials<\/td>\n<\/tr>\n\n10.6.<\/td>\nSemiconductor Single Photon Detectors<\/td>\n<\/tr>\n\n10.6.1.<\/td>\nIntroduction to semiconductor photon detectors<\/td>\n<\/tr>\n\n10.6.2.<\/td>\nOperating principles of SPADs: Avalanche photodiode (APD) basics<\/td>\n<\/tr>\n\n10.6.3.<\/td>\nOperating principles of single-photon avalanche diodes (SPADs)<\/td>\n<\/tr>\n\n10.6.4.<\/td>\nArrays of SPADs in series can form silicon photomultipliers (SiPMs) as a solid-state alternative to traditional PMTs<\/td>\n<\/tr>\n\n10.6.5.<\/td>\nInnovation in the next generation of SPADs<\/td>\n<\/tr>\n\n10.6.6.<\/td>\nKey players and innovators in the next generation of SPADs<\/td>\n<\/tr>\n\n10.6.7.<\/td>\nApplications of SPADs formed in a trade-off of resolution and performance<\/td>\n<\/tr>\n\n10.6.8.<\/td>\nDevelopment trends for groups of key SPAD players<\/td>\n<\/tr>\n\n10.6.9.<\/td>\nAdvanced semiconductor packaging techniques enabling higher pixel counts and timing functionality for SPAD arrays<\/td>\n<\/tr>\n\n10.6.10.<\/td>\nAlternative semiconductor SPADs unlock infrared wavelengths beyond the range of silicon (1)<\/td>\n<\/tr>\n\n10.6.11.<\/td>\nAlternative semiconductor SPADs unlock infrared wavelengths beyond the range of silicon (2)<\/td>\n<\/tr>\n\n10.6.12.<\/td>\nCompetition or cooperation for SPADs and SNSPDs in quantum communications and computing?<\/td>\n<\/tr>\n\n10.6.13.<\/td>\nEmerging SPADs: SWOT analysis<\/td>\n<\/tr>\n\n10.7.<\/td>\nNanomaterials (Graphene, CNTs, Diamond and MOFs)<\/td>\n<\/tr>\n\n10.7.1.<\/td>\nIntroduction to 2D Materials for Quantum Technology<\/td>\n<\/tr>\n\n10.7.2.<\/td>\nInterest in TMD based quantum dots as single photon sources for quantum networking<\/td>\n<\/tr>\n\n10.7.3.<\/td>\nIntroduction to graphene membranes<\/td>\n<\/tr>\n\n10.7.4.<\/td>\nResearch interest in graphene membranes for RAM memory in quantum computers<\/td>\n<\/tr>\n\n10.7.5.<\/td>\n2.5D Materials pitches as solution to quantum information storage<\/td>\n<\/tr>\n\n10.7.6.<\/td>\nSingle Walled Carbon Nanotubes for Quantum Computers<\/td>\n<\/tr>\n\n10.7.7.<\/td>\nLong term potential in the quantum materials market for Boron Nitride Nanotubes (BNNT)<\/td>\n<\/tr>\n\n10.7.8.<\/td>\nSnapshot of market readiness levels of CNT applications – quantum only at PoC stage<\/td>\n<\/tr>\n\n10.7.9.<\/td>\nOverview of diamond in quantum technology<\/td>\n<\/tr>\n\n10.7.10.<\/td>\nMaterial advantages and disadvantages of diamond for quantum applications<\/td>\n<\/tr>\n\n10.7.11.<\/td>\nElement Six are leaders in scaling up manufacturing of diamond for quantum applications using chemical vapor deposition (CVD)<\/td>\n<\/tr>\n\n10.7.12.<\/td>\nOverview of the synthetic diamond value chain in quantum technology<\/td>\n<\/tr>\n\n10.7.13.<\/td>\nChromophore integrated MOFs can stabilize qubits at room temperature for quantum computing<\/td>\n<\/tr>\n\n10.7.14.<\/td>\nConclusions and outlook: Materials opportunities in quantum computing<\/td>\n<\/tr>\n\n11.<\/td>\nCOMPANY PROFILES<\/td>\n<\/tr>\n\n11.1.<\/td>\nAegiq<\/td>\n<\/tr>\n\n11.2.<\/td>\nBlueFors (Helium)<\/td>\n<\/tr>\n\n11.3.<\/td>\nClassiq<\/td>\n<\/tr>\n\n11.4.<\/td>\nD-Wave<\/td>\n<\/tr>\n\n11.5.<\/td>\nDiatope<\/td>\n<\/tr>\n\n11.6.<\/td>\nDiraq<\/td>\n<\/tr>\n\n11.7.<\/td>\nElement Six (Quantum Technologies)<\/td>\n<\/tr>\n\n11.8.<\/td>\nHitachi Cambridge Laboratory (HCL)<\/td>\n<\/tr>\n\n11.9.<\/td>\nIBM (Quantum Computing)<\/td>\n<\/tr>\n\n11.10.<\/td>\nInfineon (Quantum Algorithms)<\/td>\n<\/tr>\n\n11.11.<\/td>\nInfleqtion (Cold Quanta)<\/td>\n<\/tr>\n\n11.12.<\/td>\nIonQ<\/td>\n<\/tr>\n\n11.13.<\/td>\nIQM<\/td>\n<\/tr>\n\n11.14.<\/td>\nMicrosoft Quantum<\/td>\n<\/tr>\n\n11.15.<\/td>\nnu quantum<\/td>\n<\/tr>\n\n11.16.<\/td>\nORCA Computing<\/td>\n<\/tr>\n\n11.17.<\/td>\nOxford Ionics<\/td>\n<\/tr>\n\n11.18.<\/td>\nOxford Quantum Circuits<\/td>\n<\/tr>\n\n11.19.<\/td>\nPasqal<\/td>\n<\/tr>\n\n11.20.<\/td>\nPhoton Force<\/td>\n<\/tr>\n\n11.21.<\/td>\nPowerlase Ltd<\/td>\n<\/tr>\n\n11.22.<\/td>\nPsiQuantum<\/td>\n<\/tr>\n\n11.23.<\/td>\nQ.ANT<\/td>\n<\/tr>\n\n11.24.<\/td>\nQilimanjaro Quantum Tech<\/td>\n<\/tr>\n\n11.25.<\/td>\nQuantinuum<\/td>\n<\/tr>\n\n11.26.<\/td>\nQuantrolOx<\/td>\n<\/tr>\n\n11.27.<\/td>\nQuantum Brilliance<\/td>\n<\/tr>\n\n11.28.<\/td>\nQuantum Computing Inc<\/td>\n<\/tr>\n\n11.29.<\/td>\nQuantum Economic Development Consortium (QED-C)<\/td>\n<\/tr>\n\n11.30.<\/td>\nQuantum Motion<\/td>\n<\/tr>\n\n11.31.<\/td>\nQuantum XChange<\/td>\n<\/tr>\n\n11.32.<\/td>\nQuEra<\/td>\n<\/tr>\n\n11.33.<\/td>\nQuiX Quantum<\/td>\n<\/tr>\n\n11.34.<\/td>\nRigetti<\/td>\n<\/tr>\n\n11.35.<\/td>\nRiverlane<\/td>\n<\/tr>\n\n11.36.<\/td>\nSchr\u00f6dinger Update: Batteries and Materials Informatics<\/td>\n<\/tr>\n\n11.37.<\/td>\nSEEQC<\/td>\n<\/tr>\n\n11.38.<\/td>\nSemiWise<\/td>\n<\/tr>\n\n11.39.<\/td>\nSenko Advance Components Ltd<\/td>\n<\/tr>\n\n11.40.<\/td>\nSingle Quantum<\/td>\n<\/tr>\n\n11.41.<\/td>\nSiquance<\/td>\n<\/tr>\n\n11.42.<\/td>\nTE Connectivity: Connectors for Quantum Computing<\/td>\n<\/tr>\n\n11.43.<\/td>\nVTT Manufacturing (Quantum Technologies)<\/td>\n<\/tr>\n\n11.44.<\/td>\nXeedQ<\/td>\n<\/tr>\n

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