1.EXECUTIVE SUMMARY1.1.The state of the quantum computing market: analyst opinion1.2.Introduction to quantum computers1.3.Which Industries Have Problems Quantum Computing Could Solve?1.4.Data centers complement the quantum as a service (QaaS) business model1.5.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 computers1.7.Four major challenges for quantum hardware1.8.Blueprint for a quantum computer: Qubits, initialization, readout, manipulation1.9.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 time1.12.Predicting the tipping point for quantum computing1.13.Demand for quantum computer hardware will lag user number1.14.The number of companies commercializing quantum computers rapidly grew over the last 15 years1.15.Summarizing the promises and challenges of leading quantum hardware1.16.Summarizing the promises and challenges of alternative quantum hardware1.17.Competing quantum computer architectures: summary table1.18.Roadmap for quantum commercial readiness level (QCRL) by technology1.19.Forecast for installed based of quantum computers by technology, 2026-20461.20.Emergence of the mixed quantum stack1.21.Infrastructure pain points are near universal for quantum computers1.22.Where will quantum computers be deployed?1.23.What is a platform for quantum computing?1.24.Hyperscalers position themselves as platform enablers1.25.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 computing1.28.2025 Updates from Key Players and Market Shifts1.29.Microsoft’s domestic quantum effort – Majorana 11.30.IBM: Roadmap to 100 million gates by 20291.31.Google Quantum AI study suggests RSA could be broken with only 1 million physical qubits1.32.Rigetti develops a tiled chip approach & moves towards mixed stack1.33.IQM complete over a dozen sales1.34.Oxford Quantum Circuits release new roadmap targeting early commercial advantage in 20281.35.Zuchongzhi 3.0 rivals the performance of leading quantum hardware1.36.Quantinuum: Growing quantum volume and commercial partnerships1.37.IonQ acquires Oxford Ionics for a record US$1.08 billion1.38.IonQ makes a spree of acquisitions including Oxford Ionics1.39.Oxford Ionics reveals development roadmap1.40.Infleqtion aim to reduce qubit overhead in neutral atom error correction1.41.Pasqal targets 200 logical qubits by 2029 and acquires PIC specialist1.42.PsiQuantum reveals new chipset “Omega”1.43.ORCA Computing: Towards practical quantum accelerators1.44.Quantum Brilliance: HPC integration & mobile quantum processors1.45.Riverlane commercializes hardware for quantum error correction1.46.Main conclusions (I)1.47.Main conclusions (II)1.48.Key market shifts for specific qubit modalities in the last 12 months1.49.Access more with an IDTechEx subscription2.INTRODUCTION TO QUANTUM COMPUTING2.1.1.Chapter overview2.2.Sector Overview2.2.1.Introduction to quantum computers2.2.2.Investment in quantum computing is growing2.2.3.The quantum ecosystem is growing and covers a variety of approaches2.2.4.The business model for quantum computing – quantum as a service (QaaS)2.2.5.Value capture in quantum computing2.2.6.Commercial partnership is driver for growth and a tool for technology development2.2.7.Business model trends: vertically integrated vs. the ‘quantum stack’2.2.8.Emergence of the mixed quantum stack2.2.9.Four major challenges for quantum hardware2.2.10.Shortage of quantum talent is a challenge for the industry2.2.11.Competing forces in the communication of quantum computing2.3.National Programs and Initiatives2.3.1.Quantum computing as a national strategic resource2.3.2.National facilities are early customers of on-premises quantum computers2.3.3.Government funding in the US, China, and Europe is driving the commercializing of quantum technologies2.3.4.USA National Quantum Initiative aims to accelerate research and economic development2.3.5.DARPA Quantum Benchmarking Initiative2.3.6.Quantum Economic Development Consortium (QED-C)2.3.7.NATO announced first quantum strategy in 20242.3.8.The UK National Quantum Technologies Program2.3.9.UK strategy update: NQCC and NQTP receive more support2.3.10.UK strategy update: Partnerships and London Quantum Technology Cluster2.3.11.Eleven quantum technology innovation hubs now established in Japan2.3.12.Quantum in South Korea: Ambitions to become a global leader in the 2030s2.3.13.Quantum in Australia: Creating clear benchmarks of national quantum eco-system success2.3.14.Collaboration versus quantum nationalism2.4.Technical Primer2.4.1.Classical vs. Quantum2.4.2.Superposition, entanglement, and observation2.4.3.Classical computers are built on binary logic2.4.4.Quantum computers replace binary bits with qubits2.4.5.Blueprint for a quantum computer: qubits, initialization, readout, manipulation2.4.6.Case study: Shor’s algorithm2.4.7.Chapter summary – introduction to quantum computing3.BENCHMARKING QUANTUM HARDWARE3.1.1.Chapter overview3.2.Qubit Benchmarking3.2.1.Noise effects on qubits3.2.2.Comparing coherence times3.2.3.Qubit fidelity and error rate3.3.Quantum Computer Benchmarking3.3.1.Quantum supremacy and qubit number3.3.2.Logical qubits and error correction3.3.3.Introduction to quantum volume3.3.4.Error rate and quantum volume3.3.5.Square circuit tests for quantum volume3.3.6.Critical appraisal of the importance of quantum volume3.3.7.IonQ introduces algorithmic qubits3.3.8.Companies defining their own benchmarks3.3.9.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 harder3.3.11.Conclusions: the logical qubit era and returns on investment3.4.Industry Benchmarking3.4.1.The DiVincenzo criteria3.4.2.Competing quantum computer architectures: Summary table3.4.3.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 FORECASTS4.1.Forecasting Methodology Overview4.2.Methodology: roadmap for quantum commercial readiness level by technology4.3.Roadmap for quantum commercial readiness level (QCRL) over time4.4.Methodology: Establishing the total addressable market for quantum computing4.5.Forecast for total addressable market for quantum computing4.6.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, 2026-20464.9.Forecast for annual volume of quantum computers, 2026-20464.10.Forecast for quantum computer pricing 2026-20464.11.Forecast for annual revenue from quantum computer hardware sales, 2026-20464.12.Forecast for installed based of quantum computers by technology, 2026-20464.13.Forecast for annual revenue from quantum computing hardware sales (breakdown by technology), 2026-20464.14.Comparing the install base of quantum computers to the global number of data centers4.15.Forecast for the volume of quantum computers deployed in data centers, 2026-20464.16.Key forecasting changes since the previous report5.COMPETING QUANTUM COMPUTER ARCHITECTURES5.1.1.Introduction to competing quantum computer architectures5.2.Superconducting5.2.1.Introduction to superconducting qubits (I)5.2.2.Introduction to superconducting qubits (II)5.2.3.Superconducting materials and critical temperature5.2.4.Initialization, manipulation, and readout5.2.5.Superconducting quantum computer schematic5.2.6.Comparing key players in superconducting quantum computing (hardware)5.2.7.IBM: roadmap to 100 million gates by 20295.2.8.IQM release new roadmap promising quantum advantage by 20305.2.9.IQM complete over a dozen sales and release product dimensions5.2.10.Rigetti develops a tiled chip approach & moves towards mixed stack5.2.11.Oxford Quantum Circuits release new roadmap targeting early commercial advantage in 20285.2.12.Zuchongzhi 3.0 rivals the performance of leading quantum hardware5.2.13.Roadmap for superconducting quantum hardware (chart)5.2.14.Roadmap for superconducting quantum hardware (discussion)5.2.15.Simplifying superconducting architecture requirements for scale-up5.2.16.Critical material chain considerations for superconducting quantum computing5.2.17.SWOT analysis: Superconducting quantum computers5.2.18.Key conclusions: Superconducting quantum computers5.3.Trapped Ion5.3.1.Introduction to trapped-ion quantum computing5.3.2.Initialization, manipulation, and readout for trapped ion quantum computers5.3.3.Materials challenges for a fully integrated trapped-ion chip5.3.4.Comparing key players in trapped ion quantum computing (hardware)5.3.5.Quantinuum: Growing quantum volume and commercial partnerships5.3.6.IonQ acquires Oxford Ionics for a record US$1.08 billion5.3.7.IonQ makes a spree of acquisitions including Oxford Ionics5.3.8.Oxford Ionics reveals development roadmap5.3.9.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 computers5.3.12.Key conclusions: Trapped ion quantum computers5.4.Photonic5.4.1.Introduction to photonic qubits5.4.2.Comparing photon polarization and squeezed states5.4.3.Overview of the photonic platform for quantum computing5.4.4.Initialization, manipulation, and readout of photonic quantum computers5.4.5.Comparing key players in photonic quantum computing5.4.6.PsiQuantum receives over AU$1B in government investments and seeks a US$750M private funding round5.4.7.PsiQuantum reveals new chipset “Omega”5.4.8.Aegiq – offering versatility without a universal machine5.4.9.Roadmap for photonic quantum hardware (chart)5.4.10.Roadmap for photonic quantum hardware (discussion)5.4.11.SWOT analysis: Photonic quantum computers5.4.12.Key conclusions: Photonic quantum computers5.5.Silicon Spin5.5.1.Introduction to silicon-spin qubits5.5.2.Qubits from quantum dots – ‘hot’ qubits are still pretty cold5.5.3.CMOS readout using resonators offers a speed advantage5.5.4.The advantage of silicon-spin is in the scale not the temperature5.5.5.Initialization, manipulation, and readout5.5.6.Comparing key players in silicon spin quantum computing5.5.7.Big chip makers are advancing their quantum computing capabilities5.5.8.Roadmap for silicon-spin quantum computing hardware (chart)5.5.9.Roadmap for silicon-spin (discussion)5.5.10.SWOT analysis: Silicon-spin quantum computers5.5.11.Key conclusions: Silicon-spin quantum computers5.6.Neutral Atom (Cold Atom)5.6.1.Introduction to neutral atom quantum computing5.6.2.Entanglement via Rydberg states in Rubidium/Strontium5.6.3.Initialization, manipulation and readout for neutral-atom quantum computers5.6.4.Comparing key players in neutral atom quantum computing (hardware)5.6.5.QuEra completes US$230 million funding round including Google investment5.6.6.Atom Computing partner with Microsoft5.6.7.Pasqal targets 200 logical qubits by 2029 and acquires PIC specialist5.6.8.Infleqtion aim to reduce qubit overhead in neutral atom error correction5.6.9.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 computers5.6.12.Key conclusions: Neutral atom quantum computers5.7.Diamond Defect5.7.1.Introduction to diamond-defect spin-based computing5.7.2.Lack of complex infrastructure for diamond defect hardware enables early-stage MVPs5.7.3.Supply chain and materials for diamond-defect spin-based computers5.7.4.Comparing key players in diamond defect quantum computing5.7.5.Quantum Brilliance offer lower power quantum solutions for data centers in the near term, and opportunities on the edge long term5.7.6.Quantum Brilliance: HPC integration & mobile quantum processors5.7.7.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 computers5.7.10.Key conclusions: Diamond-defect quantum computers5.8.Topological Qubits (Majorana)5.8.1.Topological qubits (Majorana modes)5.8.2.Initialization, manipulation, and readout of topological qubits5.8.3.Microsoft are the primary company pursuing topological qubits5.8.4.Microsoft’s domestic quantum effort – Majorana 15.8.5.Scaling up arrays of topological qubits5.8.6.Roadmap for topological quantum computing hardware (chart)5.8.7.Roadmap for topological quantum computing hardware (discussion)5.8.8.SWOT analysis: Topological qubits5.8.9.Key conclusions: Topological qubits5.9.Quantum Annealers5.9.1.Introduction to quantum annealers5.9.2.How do quantum processors for annealing work?5.9.3.Initialization and readout of quantum annealers5.9.4.Annealing is best suited to optimization problems5.9.5.Commercial examples of use-cases for annealing5.9.6.Clarity on annealing related terms5.9.7.Comparing key players in quantum annealing5.9.8.D-Wave intensifies focus on increasing production application deployments5.9.9.Qilimanjaro develops analog QASIC chips & target QaaS by EoY5.9.10.Roadmap for neutral-atom quantum computing hardware (chart)5.9.11.Roadmap for quantum annealing hardware (discussion)5.9.12.SWOT analysis: Quantum annealers5.9.13.Key conclusions: Quantum annealers5.10.Chapter Summary5.10.1.Summarizing the promises and challenges of leading quantum hardware5.10.2.Summarizing the promises and challenges of alternative quantum hardware5.10.3.Competing quantum computer architectures: Summary table5.10.4.Main conclusions (I)5.10.5.Main conclusions (II)5.10.6.Key market shifts for specific qubit modalities in the last 12 months6.INFRASTRUCTURE FOR QUANTUM COMPUTING6.1.Chapter overview6.2.Infrastructure trends: Modular vs. single core6.3.Hardware agnostic infrastructure platforms for quantum computing represent a new market for established technologies6.4.Introduction to cryostats for quantum computing6.5.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 cryostats6.8.Cryostats need two forms of helium, with different supply chain considerations6.9.Rare Helium-3 supplies could prove decisive for quantum ecosystems6.10.Summary of cabling and electronics requirements inside a dilution refrigerator for quantum computing6.11.Qubit readout methods: Microwaves and microscopes6.12.Pain points for incumbent platform solutions7.DEPLOYMENT OF QUANTUM COMPUTERS7.1.1.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 computers7.1.4.Challenges in the delivery and commissioning of quantum computers7.1.5.Case study: Potential sources of disruption in a quantum computing environment and the sensors used to monitor them – IQM7.2.Quantum Computing in Data Centers7.2.1.Data centers are key partners for quantum hardware developers to reach more customers7.2.2.Data centers complement the quantum as a service (QaaS) business model7.2.3.Hyperscalers position themselves as platform enablers7.2.4.What is a platform for quantum computing?7.2.5.OCP Ready for Quantum7.2.6.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 – classical7.2.9.Energy consumption of cooling systems – quantum7.2.10.Comparing the energy consumption of quantum and classical computers7.2.11.Power demand from data centers will increase significantly over the coming decade7.2.12.Key takeaways for the data center industry8.QUANTUM COMPUTING AND AI8.1.Quantum for AI, AI for Quantum, or Quantum vs AI?8.2.Use cases for AI in quantum computing8.3.AI tools could assist in interfacing with quantum machines8.4.Competition with advancements in classical computing8.5.Two of China’s tech giants move away from quantum and towards AI8.6.NVIDIA & quantum computing: NVAQC and Quantum Cloud8.7.ORCA Computing: Quantum processors for machine learning8.8.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 COMPUTING9.1.Overview of Key Applications9.1.1.Chapter overview – applications of quantum computing9.1.2.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/Y2Q9.1.5.Google Quantum AI study suggests RSA could be broken with only 1 million physical qubits9.1.6.Which Industries Have Problems Quantum Computing Could Solve?9.2.Automotive Applications of Quantum Computing9.2.1.Quantum chemistry offers more accurate simulations to aid battery material discovery9.2.2.Quantum machine learning could make image classification for vehicle autonomy more efficient9.2.3.Quantum optimization for assembly line and distribution efficiency could save time, money, and energy9.2.4.Most automotive players are pursuing quantum computing for battery chemistry9.2.5.The automotive industry is yet to converge on a preferred qubit modality9.2.6.Partnerships and collaborations for automotive quantum computing9.2.7.Mercedes: Case study in remaining hardware agnostic9.2.8.Tesla: Supercomputers not quantum computers9.2.9.Summary of key conclusions9.2.10.Analyst opinion on quantum computing for automotive9.3.Finance Applications of Quantum Computing9.3.1.Partnerships forming now will shape the future of quantum computing for the financial sector9.3.2.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 finance9.3.5.HSBC and Quantum Key Distribution9.3.6.Quantum key distribution – 4 challenges to adoption – BT10.MATERIALS FOR QUANTUM COMPUTING10.1.1.Chapter Overview10.2.Superconductors10.2.1.Overview of superconductors in quantum technology10.2.2.Critical temperature plays a key role in superconductor material choice for quantum technology10.2.3.Critical material chain considerations for superconducting quantum computing10.2.4.Overview of the superconductor value chain in quantum technology10.2.5.Room temperature superconductors – and why they won’t necessarily unlock the quantum technology market10.2.6.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 cryogenics10.3.2.Research in scaling SNSPD arrays beyond kilopixel10.3.3.Advancements in superconducting materials drives SNSPD development10.3.4.Comparison of commercial SNSPD players10.3.5.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 technology10.5.Photonics, Silicon Photonics and Optical Components10.5.1.Overview of photonics, silicon photonics and optics in quantum technology10.5.2.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 materials10.6.Semiconductor Single Photon Detectors10.6.1.Introduction to semiconductor photon detectors10.6.2.Operating principles of SPADs: Avalanche photodiode (APD) basics10.6.3.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 PMTs10.6.5.Innovation in the next generation of SPADs10.6.6.Key players and innovators in the next generation of SPADs10.6.7.Applications of SPADs formed in a trade-off of resolution and performance10.6.8.Development trends for groups of key SPAD players10.6.9.Advanced semiconductor packaging techniques enabling higher pixel counts and timing functionality for SPAD arrays10.6.10.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 analysis10.7.Nanomaterials (Graphene, CNTs, Diamond and MOFs)10.7.1.Introduction to 2D Materials for Quantum Technology10.7.2.Interest in TMD based quantum dots as single photon sources for quantum networking10.7.3.Introduction to graphene membranes10.7.4.Research interest in graphene membranes for RAM memory in quantum computers10.7.5.2.5D Materials pitches as solution to quantum information storage10.7.6.Single Walled Carbon Nanotubes for Quantum Computers10.7.7.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 stage10.7.9.Overview of diamond in quantum technology10.7.10.Material advantages and disadvantages of diamond for quantum applications10.7.11.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 technology10.7.13.Chromophore integrated MOFs can stabilize qubits at room temperature for quantum computing10.7.14.Conclusions and outlook: Materials opportunities in quantum computing11.COMPANY PROFILES11.1.Aegiq11.2.BlueFors (Helium)11.3.Classiq11.4.D-Wave11.5.Diatope11.6.Diraq11.7.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.IonQ11.13.IQM11.14.Microsoft Quantum11.15.nu quantum11.16.ORCA Computing11.17.Oxford Ionics11.18.Oxford Quantum Circuits11.19.Pasqal11.20.Photon Force11.21.Powerlase Ltd11.22.PsiQuantum11.23.Q.ANT11.24.Qilimanjaro Quantum Tech11.25.Quantinuum11.26.QuantrolOx11.27.Quantum Brilliance11.28.Quantum Computing Inc11.29.Quantum Economic Development Consortium (QED-C)11.30.Quantum Motion11.31.Quantum XChange11.32.QuEra11.33.QuiX Quantum11.34.Rigetti11.35.Riverlane11.36.Schrödinger Update: Batteries and Materials Informatics11.37.SEEQC11.38.SemiWise11.39.Senko Advance Components Ltd11.40.Single Quantum11.41.Siquance11.42.TE Connectivity: Connectors for Quantum Computing11.43.VTT Manufacturing (Quantum Technologies)11.44.XeedQ
