The Global Quantum Technology Market 2026-2046

TABLE OF CONTENTS

1 EXECUTIVE SUMMARY

• 1.1 Quantum Technologies Market in 2026
  • 1.1.1 Q1 2025: The Surge That Set the Tone
  • 1.1.2 Q2 2025: Momentum Builds Across the Stack
  • 1.1.3 Q3 2025: Mega-Rounds and a New Valuation Era
  • 1.1.4 Q4 2025: Going Public and Consolidation Accelerates
  • 1.1.5 Into 2026: The Public Market Era Begins
  • 1.1.6 The Strategic Picture: What $10 Billion Means
  • 1.1.7 2025 as Quantum Technology's Commercial Watershed
• 1.2 First and second quantum revolutions
• 1.3 Current quantum technology market landscape
  • 1.3.1 Key developments
• 1.4 Technology Readiness Assessment
• 1.5 Quantum Technologies Investment Landscape
  • 1.5.1 Total market investments 2012-2026
  • 1.5.2 By Technology
  • 1.5.3 By Company
  • 1.5.4 By Application
  • 1.5.5 By Region
    • 1.5.5.1 The Quantum Market in North America
    • 1.5.5.2 The Quantum Market in Asia
    • 1.5.5.3 The Quantum Market in Europe
  • 1.5.6 Key Investment Trends 2025–2026
• 1.6 Global government initiatives and funding
  • 1.6.1 United States
  • 1.6.2 China
  • 1.6.3 European Union
  • 1.6.4 Germany
  • 1.6.5 United Kingdom
  • 1.6.6 France
  • 1.6.7 Canada
  • 1.6.8 Australia
  • 1.6.9 Japan
  • 1.6.10 India
  • 1.6.11 Cross-Cutting Themes in Government Quantum Investment
  • 1.6.12 Supply Chain Concentration and Geopolitical Exposure
• 1.7 Challenges for quantum technologies adoption
• 1.8 Critical Supply Chain Bottlenecks
• 1.9 Quantum Technology Market Map
• 1.10 SWOT Analysis
• 1.11 Quantum Technology Value Chain
• 1.12 Global Market Forecast 2026–2046
  • 1.12.1 Total Market Revenues
  • 1.12.2 By Technology Segment
  • 1.12.3 By End-Use Industry
  • 1.12.4 By Region

2 INTRODUCTION TO QUANTUM TECHNOLOGY

• 2.1 First and Second Quantum Revolutions
• 2.2 Quantum Mechanics Principles
  • 2.2.1 Superposition
  • 2.2.2 Entanglement
  • 2.2.3 Quantum Coherence
  • 2.2.4 Quantum Tunnelling
• 2.3 The Quantum Technology Ecosystem
• 2.4 Enabling Technologies and Infrastructure
• 2.5 Standards Development

3 QUANTUM COMPUTING

• 3.1 What is quantum computing?
  • 3.1.1 Operating principle
  • 3.1.2 Classical vs quantum computing
  • 3.1.3 Quantum computing technology
    • 3.1.3.1 Quantum emulators
    • 3.1.3.2 Quantum inspired computing
    • 3.1.3.3 Quantum annealing computers
    • 3.1.3.4 Quantum simulators
    • 3.1.3.5 Digital quantum computers
    • 3.1.3.6 Continuous variables quantum computers
    • 3.1.3.7 Measurement Based Quantum Computing (MBQC)
    • 3.1.3.8 Topological quantum computing
    • 3.1.3.9 Quantum Accelerator
• 3.2 Benchmarking and Performance Metrics
  • 3.2.1 Qubit Count
  • 3.2.2 Gate Fidelity
  • 3.2.3 Coherence Times
  • 3.2.4 Quantum Volume
  • 3.2.5 Competition from other technologies
  • 3.2.6 Quantum algorithms
    • 3.2.6.1 Quantum Software Stack
    • 3.2.6.2 Quantum Machine Learning
    • 3.2.6.3 Quantum Simulation
    • 3.2.6.4 Quantum Optimization
    • 3.2.6.5 Quantum Cryptography
      • 3.2.6.5.1 Quantum Key Distribution (QKD)
      • 3.2.6.5.2 Post-Quantum Cryptography
  • 3.2.7 Architectural Approaches
    • 3.2.7.1 Modular vs. Single Core
    • 3.2.7.2 Heterogeneous Multi-Qubit Architectures
  • 3.2.8 Hardware
    • 3.2.8.1 Qubit Technologies
      • 3.2.8.1.1 Superconducting Qubits
        • 3.2.8.1.1.1 Technology description
        • 3.2.8.1.1.2 Materials
        • 3.2.8.1.1.3 Hardware Architecture
        • 3.2.8.2.1.4 Market players
        • 3.2.8.2.1.5 Swot analysis
        • 3.2.8.2.1.6 Superconducting Hardware Roadmap
      • 3.2.8.1.2 Trapped Ion Qubits
        • 3.2.8.2.2.1 Technology description
        • 3.2.8.2.2.2 Ion Species Comparison
        • 3.2.8.2.2.3 Trap Architectures
        • 3.2.8.2.2.4 Materials
          • 3.2.8.2.2.4.1 Integrating optical components
          • 3.2.8.2.2.4.2 Incorporating high-quality mirrors and optical cavities
          • 3.2.8.2.2.4.3 Engineering the vacuum packaging and encapsulation
          • 3.2.8.2.2.4.4 Removal of waste heat
        • 3.2.8.2.2.5 Market players
        • 3.2.8.2.2.6 Swot analysis
        • 3.2.8.2.2.7 Trapped Ion Hardware Roadmap
      • 3.2.8.2.3 Silicon Spin Qubits
        • 3.2.8.2.3.1 Technology description
        • 3.2.8.2.3.2 Quantum dots
        • 3.2.8.2.3.3 Market players
        • 3.2.8.2.3.4 SWOT analysis
        • 3.2.8.2.3.5 Silicon Spin Hardware Roadmap
      • 3.2.8.2.4 Topological Qubits
        • 3.2.8.2.4.1 Technology description
          • 3.2.8.2.4.1.1 Cryogenic cooling
        • 3.2.8.2.4.2 Market players
        • 3.2.8.2.4.3 SWOT analysis
      • 3.2.8.2.5 Photonic Qubits
        • 3.2.8.2.5.1 Technology description
          • 3.2.8.2.5.1.1 Architectural Classes
          • 3.2.8.2.5.1.2 Initialization, Manipulation, and Readout
          • 3.2.8.2.5.1.3 Hardware Architecture
        • 3.2.8.2.5.2 Race to Photonic Fault Tolerance: Tier Analysis
        • 3.2.8.2.5.3 Market players
        • 3.2.8.2.5.4 Swot analysis
        • 3.2.8.2.5.5 Photonic Hardware Roadmap
        • 3.2.8.2.5.6 Race to Photonic Fault Tolerance: Tier Analysis
      • 3.2.8.2.6 Neutral atom (cold atom) qubits
        • 3.2.8.2.6.1 Technology description
        • 3.2.8.2.6.2 Market players
        • 3.2.8.2.6.3 Swot analysis
        • 3.2.8.2.6.4 Neutral Atom Hardware Roadmap
      • 3.2.8.2.7 Diamond-defect qubits
        • 3.2.8.2.7.1 Technology description
        • 3.2.8.2.7.2 SWOT analysis
        • 3.2.8.2.7.3 Market players
        • 3.2.8.2.7.4 Diamond-Defect Hardware Roadmap
      • 3.2.8.2.8 Quantum annealers
        • 3.2.8.2.8.1 Technology description
        • 3.2.8.2.8.2 SWOT analysis
        • 3.2.8.2.8.3 Market players
        • 3.2.8.2.8.4 Quantum Annealing Hardware Roadmap
    • 3.2.8.3 Architectural Approaches
    • 3.2.8.4 Quantum Computing Infrastructure Requirements
  • 3.2.9 Software
    • 3.2.9.1 Technology description
    • 3.2.9.2 Cloud-based services- QCaaS (Quantum Computing as a Service).
      • 3.2.9.2.1 The Cloud-First Reality of Quantum Computing
      • 3.2.9.2.2 Platform Architecture Models
      • 3.2.9.2.3 Major Quantum Cloud Platforms
      • 3.2.9.2.4 Pricing Models
      • 3.2.9.2.5 Quantum Cloud Platform Comparison
      • 3.2.9.2.6 Cloud Platform Market Forecast
    • 3.2.9.3 Market players
• 3.3 Market challenges
• 3.4 SWOT analysis
• 3.5 Business Models
• 3.6 Quantum Error Correction and Fault Tolerance
  • 3.6.1 Why Error Correction Matters
  • 3.6.2 Quantum Error Correction Code Families
  • 3.6.3 Fault Tolerance Requirements and Logical Qubit Demonstrations
  • 3.6.4 Magic State Distillation and Logical Gate Sets
  • 3.6.5 Hardware-Aware Error Correction
  • 3.6.6 QEC-Specific Vendors and Software Stack
  • 3.6.7 Resource Estimation for Fault-Tolerant Algorithms
  • 3.6.8 Market Forecast — QEC-Related Spending
• 3.7 Quantum Computing in Data Centres
  • 3.7.1 Overview
  • 3.7.2 Photonic Deployment Models in Data Centres
• 3.8 Quantum computing value chain
• 3.9 Markets and applications for quantum computing
  • 3.9.1 Pharmaceuticals
    • 3.9.1.1 Market overview
      • 3.9.1.1.1 Drug discovery
      • 3.9.1.1.2 Diagnostics
      • 3.9.1.1.3 Molecular simulations
      • 3.9.1.1.4 Genomics
      • 3.9.1.1.5 Proteins and RNA folding
    • 3.9.1.2 Market players
  • 3.9.2 Chemicals
    • 3.9.2.1 Market overview
    • 3.9.2.2 Market players
  • 3.9.3 Transportation
    • 3.9.3.1 Market overview
    • 3.9.3.2 Market players
  • 3.9.4 Financial services
    • 3.9.4.1 Market overview
    • 3.9.4.2 Market players
• 3.10 Opportunity analysis
• 3.11 Technology roadmap
• 3.12 Quantum-Inspired Classical Computing
  • 3.12.1 What is Quantum-Inspired Computing?
  • 3.12.2 Quantum-Inspired Algorithms
  • 3.12.3 Quantum-Inspired Hardware Architectures
  • 3.12.4 Commercial Applications
  • 3.12.5 Major Quantum-Inspired Vendors
  • 3.12.6 Quantum vs Quantum-Inspired: Strategic Positioning
  • 3.12.7 Market Forecast — Quantum-Inspired Computing

4 QUANTUM CHEMISTRY AND ARTIFICAL INTELLIGENCE (AI)

• 4.1 Technology description
• 4.2 Applications
• 4.3 SWOT analysis
• 4.4 Market challenges
• 4.5 Market players
• 4.6 Opportunity analysis
• 4.7 Technology roadmap

5 QUANTUM MACHINE LEARNING

• 5.1 What is Quantum Machine Learning?
• 5.2 Classical vs. Quantum Computing Paradigms for ML
• 5.3 Quantum Mechanical Principles for ML
• 5.4 Machine Learning Fundamentals
• 5.5 The Intersection — Why Combine Quantum and ML?
• 5.6 QML Phases and Evolution
  • 5.6.1 The First Phase of QML
  • 5.6.2 The Second Phase of QML
• 5.7 Algorithms and Software for QML
• 5.8 Quantum Neural Networks
• 5.9 Variational Quantum Classifiers
• 5.10 Quantum Kernel Methods
• 5.11 Advantages of QML
  • 5.11.1 Improved Optimisation and Generalisation
  • 5.11.2 Quantum Advantage in ML
  • 5.11.3 Training Advantages and Opportunities
  • 5.11.4 Improved Accuracy
• 5.12 Challenges and Limitations
  • 5.12.1 Hardware Constraints
  • 5.12.2 Costs
  • 5.12.3 Nascent Technology
• 5.13 QML Applications
• 5.14 QML Roadmap
• 5.15 Market Players
• 5.16 Market Forecasts 2026–2036

6 QUANTUM SIMULATION

• 6.1 What is Quantum Simulation?
• 6.2 Analog vs. Digital Quantum Simulation
• 6.3 Quantum Simulation Platforms
  • 6.3.1 Neutral Atom Simulators
  • 6.3.2 Trapped Ion Simulators
  • 6.3.3 Superconducting Circuit Simulators
  • 6.3.4 Photonic Simulators
• 6.4 Applications of Quantum Simulation
  • 6.4.1 Molecular and Chemical Simulation
  • 6.4.2 Materials Discovery
  • 6.4.3 High-Energy Physics
  • 6.4.4 Condensed Matter Physics
  • 6.4.5 Drug Discovery and Protein Folding
• 6.5 Quantum Chemistry Simulation
• 6.6 Market Players
• 6.7 SWOT Analysis
• 6.8 Market Forecasts 2026–2036

7 QUANTUM COMMUNICATIONS

• 7.1 Technology description
• 7.2 Types
• 7.3 Applications
• 7.4 Quantum Random Numbers Generators (QRNG)
  • 7.4.1 Overview
  • 7.4.2 QRNG Product Design and Technology Evolution
  • 7.4.3 Entropy Sources
  • 7.4.4 High Throughput as Key Differentiator
  • 7.4.5 Standards Development
  • 7.4.6 Applications
    • 7.4.6.1 Encryption for Data Centers
    • 7.4.6.2 Consumer Electronics
    • 7.4.6.3 Automotive/Connected Vehicle
    • 7.4.6.4 Gambling and Gaming
    • 7.4.6.5 Monte Carlo Simulations
    • 7.4.6.6 Government and Defense Applications
    • 7.4.6.7 Enterprise Networks and Data Centers
    • 7.4.6.8 Automotive Applications
    • 7.4.6.9 Online Gaming
  • 7.4.7 Advantages
  • 7.4.8 Principle of Operation of Optical QRNG Technology
  • 7.4.9 Non-optical approaches to QRNG technology
  • 7.4.10 SWOT Analysis
  • 7.4.11 Market Forecasts
• 7.5 Quantum Key Distribution (QKD)
  • 7.5.1 Overview
  • 7.5.2 Asymmetric and Symmetric Keys
  • 7.5.3 Principle behind QKD
  • 7.5.4 Why is QKD More Secure Than Other Key Exchange Mechanisms?
  • 7.5.5 Discrete Variable vs. Continuous Variable QKD Protocols
  • 7.5.6 MDI-QKD (Measurement Device Independent QKD)
  • 7.5.7 Fiber-Based QKD
  • 7.5.8 Free-Space and Satellite QKD
  • 7.5.9 Key Players
  • 7.5.10 Challenges
  • 7.5.11 SWOT Analysis
  • 7.5.12 Market Forecasts
• 7.6 Post-quantum cryptography (PQC)
  • 7.6.1 Overview
  • 7.6.2 Security systems integration
  • 7.6.3 PQC standardization
    • 7.6.3.1 NIST Standardisation Process and Outcomes
    • 7.6.3.2 Migration Implications
  • 7.6.4 Transitioning cryptographic systems to PQC
  • 7.6.5 Market players
  • 7.6.6 SWOT Analysis
  • 7.6.7 Market Forecasts
    • 7.6.7.1 Beyond Algorithms: The Migration Reality
    • 7.6.7.2 The Migration Stack
    • 7.6.7.3 Industry-Specific Migration Programs
    • 7.6.7.4 Migration Services and Consulting Market
    • 7.6.7.5 Market Forecast — Quantum-Safe Migration
    • 7.6.7.6 Y2Q Timeline and Strategic Implications
• 7.7 Quantum homomorphic cryptography
• 7.8 Quantum Teleportation
• 7.9 Quantum Networks
  • 7.9.1 Overview
  • 7.9.2 Advantages
  • 7.9.3 Role of Trusted Nodes and Trusted Relays
  • 7.9.4 Entanglement Swapping and Optical Switches
  • 7.9.5 Multiplexing quantum signals with classical channels in the O-band
    • 7.9.5.1 Wavelength-division multiplexing (WDM) and time-division multiplexing (TDM)
  • 7.9.6 Twin-Field Quantum Key Distribution (TF-QKD)
  • 7.9.7 Enabling global-scale quantum communication
  • 7.9.8 Advanced optical fibers and interconnects
  • 7.9.9 Photodetectors in quantum networks
    • 7.9.9.1 Avalanche photodetectors (APDs)
    • 7.9.9.2 Single-photon avalanche diodes (SPADs)
    • 7.9.9.3 Silicon Photomultipliers (SiPMs)
  • 7.9.10 Cryostats
    • 7.9.10.1 Cryostat architectures
  • 7.9.11 Infrastructure requirements
  • 7.9.12 Global activity
    • 7.9.12.1 China
    • 7.9.12.2 Europe
    • 7.9.12.3 The Netherlands
    • 7.9.12.4 The United Kingdom
    • 7.9.12.5 US
    • 7.9.12.6 Japan
  • 7.9.13 SWOT analysis
• 7.10 Quantum Memory
• 7.11 Quantum Internet
• 7.12 Global Market for Quantum Communications by Technology Type 2026–2036
• 7.13 Market challenges
• 7.14 Market players
• 7.15 Opportunity analysis
• 7.16 Technology roadmap

8 QUANTUM SENSORS

• 8.1 Technology description
  • 8.1.1 Quantum Sensing Principles
  • 8.1.2 SWOT analysis
  • 8.1.3 Atomic Clocks
    • 8.1.3.1 High frequency oscillators
      • 8.1.3.1.1 Emerging oscillators
    • 8.1.3.2 Caesium atoms
    • 8.1.3.3 Self-calibration
    • 8.1.3.4 Optical atomic clocks
      • 8.1.3.4.1 Chip-scale optical clocks
    • 8.1.3.5 Bench/Rack-Scale Atomic Clocks
    • 8.1.3.6 Chip-Scale Atomic Clocks (CSAC)
    • 8.1.3.7 Atomic Clocks Market Forecasts — Total
    • 8.1.3.8 Companies
    • 8.1.3.9 SWOT analysis
  • 8.1.4 Quantum Magnetic Field Sensors
    • 8.1.4.1 Introduction
    • 8.1.4.2 Motivation for use
    • 8.1.4.3 Market opportunity
    • 8.1.4.4 Superconducting Quantum Interference Devices (Squids)
      • 8.1.4.4.1 Applications
      • 8.1.4.4.2 Key players
      • 8.1.4.4.3 SWOT analysis
    • 8.1.4.5 Optically Pumped Magnetometers (OPMs)
      • 8.1.4.5.1 Applications
      • 8.1.4.5.2 Key players
      • 8.1.4.5.3 SWOT analysis
    • 8.1.4.6 Tunneling Magneto Resistance Sensors (TMRs)
      • 8.1.4.6.1 Applications
      • 8.1.4.6.2 Key players
      • 8.1.4.6.3 SWOT analysis
    • 8.1.4.7 Nitrogen Vacancy Centers (N-V Centers)
      • 8.1.4.7.1 Applications
      • 8.1.4.7.2 Key players
      • 8.1.4.7.3 SWOT analysis
  • 8.1.5 Quantum Gravimeters
    • 8.1.5.1 Technology description
    • 8.1.5.2 Applications
    • 8.1.5.3 Key players
    • 8.1.5.4 SWOT analysis
  • 8.1.6 Quantum Gyroscopes
    • 8.1.6.1 Technology description
      • 8.1.6.1.1 Inertial Measurement Units (IMUs)
      • 8.1.6.1.2 Atomic quantum gyroscopes
    • 8.1.6.2 Applications
    • 8.1.6.3 Key players
    • 8.1.6.4 SWOT analysis
  • 8.1.7 Quantum Image Sensors
    • 8.1.7.1 Technology description
    • 8.1.7.2 Applications
    • 8.1.7.3 SWOT analysis
    • 8.1.7.4 Key players
  • 8.1.8 Quantum Radar
    • 8.1.8.1 Technology description
    • 8.1.8.2 Applications
  • 8.1.9 Quantum Navigation
  • 8.1.10 Quantum Sensor Components
  • 8.1.11 Quantum Chemical Sensors
    • 8.1.11.1 Technology overview
    • 8.1.11.2 Commercial activities
  • 8.1.12 Quantum Radio Frequency Field Sensors
    • 8.1.12.1 Overview
    • 8.1.12.2 Rydberg Atom Based Electric Field Sensors and Radio Receivers
      • 8.1.12.2.1 Principles
      • 8.1.12.2.2 Commercialization
    • 8.1.12.3 Nitrogen-Vacancy Centre Diamond Electric Field Sensors and Radio Receivers
      • 8.1.12.3.1 Principles
      • 8.1.12.3.2 Applications
    • 8.1.12.4 Market
  • 8.1.13 Quantum NEM and MEMs
    • 8.1.13.1 Technology description
• 8.2 Market and technology challenges
• 8.3 Market forecasts
  • 8.3.1 By Sensor Type
  • 8.3.2 By Volume
  • 8.3.3 By Sensor Price
  • 8.3.4 By End-Use Industry
• 8.4 Technology roadmap

9 QUANTUM BATTERIES

• 9.1 Technology description
• 9.2 Types
• 9.3 Applications
• 9.4 SWOT analysis
• 9.5 Market challenges
• 9.6 Market players
• 9.7 Opportunity analysis
• 9.8 Technology roadmap

10 END-USE MARKETS AND APPLICATIONS

• 10.1 Overview
• 10.2 Pharmaceuticals and Drug Discovery
  • 10.2.1 Market Overview
  • 10.2.2 Drug Discovery Applications
• 10.3 Financial Services
  • 10.3.1 Market Overview
  • 10.3.2 Portfolio Optimisation
  • 10.3.3 Risk Assessment
  • 10.3.4 Algorithmic Trading
  • 10.3.5 Fraud Detection
• 10.4 Aerospace and Defence
  • 10.4.1 Market Overview
  • 10.4.2 Navigation and Positioning
  • 10.4.3 Secure Communications
  • 10.4.4 Simulation and Optimisation
• 10.5 Energy and Utilities
  • 10.5.1 Market Overview
  • 10.5.2 Grid Optimisation
  • 10.5.3 Renewable Energy Integration
  • 10.5.4 Carbon Capture Optimisation
• 10.6 Healthcare and Medical
  • 10.6.1 Market Overview
  • 10.6.2 Medical Imaging
  • 10.6.3 Diagnostics
  • 10.6.4 Personalized Medicine
• 10.7 Telecommunications
  • 10.7.1 Market Overview
  • 10.7.2 Network Optimisation
  • 10.7.3 Quantum-Secure Networks
• 10.8 Government and Public Sector
  • 10.8.1 Market Overview

11 MATERIALS FOR QUANTUM TECHNOLOGIES

• 11.1 Superconductors
  • 11.1.1 Overview
  • 11.1.2 Types and Properties
  • 11.1.3 Critical Temperature and Material Selection
    • 11.1.3.1 Critical Material Supply Chain Considerations
  • 11.1.4 Superconducting Quantum Circuits
    • 11.1.4.1 Introduction
    • 11.1.4.2 Fabricating Superconducting Qubits
  • 11.1.5 Defects and Sources of Noise
  • 11.1.6 Superconducting Nanowire Single-Photon Detectors (SNSPDs) — Materials and Fabrication
  • 11.1.7 Opportunities
• 11.2 Photonics, Silicon Photonics and Optical Components
  • 11.2.1 Overview
  • 11.2.2 Types and Properties
  • 11.2.3 Photonic Integrated Circuits for Quantum Technology
    • 11.2.3.1 Overview
  • 11.2.4 PICs for Quantum Sensing
  • 11.2.5 Opportunities
• 11.3 Nanomaterials
  • 11.3.1 Overview
  • 11.3.2 Types and Properties
  • 11.3.3 Opportunities
• 11.4 Artificial Diamond for Quantum Technology
  • 11.4.1 Overview
  • 11.4.2 Supply Chain and Materials for Diamond-Based Quantum Computers
  • 11.4.3 Quantum Grade Diamond
  • 11.4.4 Silicon-Vacancy in Diamond Quantum Memory
• 11.5 Cryogenic Infrastructure
  • 11.5.1 The Role of Cryogenics in Quantum Computing
  • 11.5.2 Operating Temperature Requirements by Modality
  • 11.5.3 Dilution Refrigerators
    • 11.5.3.1 Cryogen-Free vs. Wet Systems
      • 11.5.3.1.1.1 Modular and Cube-Format Architectures
  • 11.5.4 Pulse Tube and Cryocoolers
  • 11.5.5 Alternative Cooling Technologies
  • 11.5.6 Dilution Refrigerator Vendor Landscape
  • 11.5.7 Partnership Models
  • 11.5.8 Cryogenic System Lead Times and Capacity Constraints
  • 11.5.9 Ten-Year Forecast — Installed Base of Dilution Refrigerators
• 11.6 Helium-3 Supply Chain
  • 11.6.1 Why Helium-3 Matters for Quantum Computing
  • 11.6.2 ³He Production from Tritium Decay
  • 11.6.3 ³He Supply Sources and Annual Production Estimates
  • 11.6.4 Demand-Supply Gap Modelling, 2026–2046
  • 11.6.5 Lunar Regolith Harvesting (Interlune)
  • 11.6.6 Helium-4 Industrial Supply Risk
  • 11.6.7 Strategic Stockpiling and Mitigation
• 11.7 Cryogenic Control Electronics and Cryo-CMOS
  • 11.7.1 The Wiring Crisis — Why Room-Temperature Control Cannot Scale
  • 11.7.2 Architectural Approaches
  • 11.7.3 NVQLink and the Quantum-Classical Data Centre Convergence
  • 11.7.4 Cryo-CMOS Devices and Process Technology
  • 11.7.5 Vendor Landscape
  • 11.7.6 Cryogenic Amplifiers — TWPAs, HEMT and Parametric
  • 11.7.7 Heat Load Budgets and Power Dissipation Constraints
  • 11.7.8 Ten-Year Forecast — Cryo-CMOS Market and Penetration
• 11.8 Lasers and Photonic Components by Modality
  • 11.8.1 The Laser Bill of Materials in a Quantum System
  • 11.8.2 Wavelengths Required by Atomic and Solid-State Modalities
  • 11.8.3 Laser Technology Platforms
  • 11.8.4 Linewidth, Stability and Phase Noise Requirements
  • 11.8.5 Photonic Component Suppliers
  • 11.8.6 Laser Vendor Capability Matrix
  • 11.8.7 Single-Photon Detection
  • 11.8.8 Photonic Integrated Circuits and Foundry Access
• 11.9 Ultra-High Vacuum (UGV) Systems
  • 11.9.1 Vacuum Pressure Requirements by Modality
  • 11.9.2 UHV Chamber Design and Materials
  • 11.9.3 Vacuum Pumps and Hardware
  • 11.9.4 Vacuum Feedthroughs and Hermetic Seals
  • 11.9.5 Vapour Cell Technology and Atomic Sources
  • 11.9.6 UHV Vendor Capability Matrix
• 11.10 Microwave and Optical Interconnects
  • 11.10.1 Cryogenic Microwave Cabling
  • 11.10.2 High-Density Cryogenic Connectors
  • 11.10.3 Cryogenic Attenuators and Filters
  • 11.10.4 Circulators, Isolators and Switches
  • 11.10.5 Optical Interconnects for Photonic and Modular Quantum Systems
  • 11.10.6 Microwave-to-Optical Transducers
  • 11.10.7 Vendor Landscape
• 11.11 Supply Chain Bottleneck Assessment
  • 11.11.1 Methodology — Severity, Probability and Time-to-Resolution Framework
  • 11.11.2 Critical Bottlenecks
  • 11.11.3 High-Severity Bottlenecks
  • 11.11.4 Bottleneck Heat-Map by Modality
  • 11.11.5 Mitigation Strategies
• 11.12 Materials Market Forecasts
  • 11.12.1 Forecasting Methodology and Scenario Definitions
  • 11.12.2 Superconducting Chips and Substrates
  • 11.12.3 Photonic Integrated Circuits and Optical Components
  • 11.12.4 Cryogenic Infrastructure
  • 11.12.5 Helium-3 and Helium-4 Supply
  • 11.12.6 Cryogenic Control Electronics and Cryo-CMOS
  • 11.12.7 Lasers and Single-Photon Detectors
  • 11.12.8 Ultra-High Vacuum Systems
  • 11.12.9 Microwave and Optical Interconnects
  • 11.12.10 Diamond and Quantum Materials
  • 11.12.11 Nanomaterials for Quantum Applications
• 11.13 North America
  • 11.13.1 United States
  • 11.13.2 Canada
• 11.14 Europe
  • 11.14.1 European Union Initiatives
  • 11.14.2 United Kingdom
  • 11.14.3 Germany
  • 11.14.4 France
  • 11.14.5 Netherlands
• 11.15 Asia-Pacific
  • 11.15.1 China
  • 11.15.2 Japan
  • 11.15.3 South Korea
  • 11.15.4 Australia
  • 11.15.5 Singapore
• 11.16 Rest of World
• 11.17 Government Initiatives Comparison

12 GLOBAL MARKET ANALYSIS

• 12.1 Market map
• 12.2 Key industry players
  • 12.2.1 Start-ups
  • 12.2.2 Tech Giants
  • 12.2.3 National Initiatives
• 12.3 Global market revenues 2018-2046
  • 12.3.1 Quantum Computing
  • 12.3.2 Quantum Sensors
  • 12.3.3 QKD Systems
  • 12.3.4 Quantum Random Number Generators (QRNG)
  • 12.3.5 Post-Quantum Cryptography (PQC)
  • 12.3.6 Quantum Machine Learning
  • 12.3.7 Quantum Simulation
  • 12.3.8 Quantum Batteries
  • 12.3.9 Total Quantum TechnologyMarket — Consolidated Forecast
  • 12.3.10 Quantum Hardware Supply Chain Market
    • 12.3.10.1 Geographic Distribution of Supply Chain Revenue
  • 12.3.11 Total Quantum Technology Market Including Supply Chain
• 12.4 Quantum Workforce and Talent Market
  • 12.4.1 Why Workforce Matters
  • 12.4.2 The Quantum Talent Pyramid
  • 12.4.3 University Programs and Degrees
  • 12.4.4 Industry Training Programs
  • 12.4.5 Government Workforce Initiatives
  • 12.4.6 Compensation Benchmarks
  • 12.4.7 Workforce Market Forecast

13 COMPANY PROFILES 435 (345 company profiles)

14 RESEARCH METHODOLOGY

15 TERMS AND DEFINITIONS

16 REFERENCES

List of Tables

• Table 1. 2025–2026 Quantum Technology Investment
• Table 2. First and second quantum revolutions.
• Table 3. Technology Readiness Level (TRL) assessment by quantum platform
• Table 4. Quantum Technology Total Investments 2012–2026 (millions USD)
• Table 5. Major Quantum Technologies Investments 2024–H1 2026
• Table 6. Quantum Technology Investments 2012–2026 by Technology Subsector (millions USD)
• Table 7. Quantum Technology Funding 2022–2026 by Company (USD)
• Table 8. Quantum Technology Investment by Application 2012–2026 (millions USD)
• Table 9. Quantum Technology Investments 2012–2026 by Region (millions USD)
• Table 10. Key Quantum Investment Trends 2025–2026
• Table 11. Global Government Quantum Commitments (2022–2026)
• Table 12. Challenges for quantum technologies adoption.
• Table 13. Top Ten Most Severe Supply Chain Bottlenecks, 2026
• Table 14. Quantum Technologyvalue chain
• Table 15. Total Quantum Technology Market Forecast 2026–2046 (billions USD)
• Table 16. Quantum Technology Market by Segment — Revenue, Share, and Growth Rate, 2026–2046 (billions USD, %)
• Table 17. Quantum Technology Market by End-Use Industry 2026–2046 (billions USD)
• Table 18. Quantum Technology Market by Region 2026–2046 (billions USD)
• Table 19. First and second quantum revolutions
• Table 20. Comparison — Classical vs. Quantum Technologies
• Table 21. Applications for quantum computing
• Table 22. Comparison of classical versus quantum computing.
• Table 23. Key quantum mechanical phenomena utilized in quantum computing.
• Table 24. Types of quantum computers.
• Table 25. Qubit performance benchmarking by platform
• Table 26. Coherence times for different qubit implementations
• Table 27. Quantum computer benchmarking metrics
• Table 28. Logical qubit progress
• Table 29. Comparative analysis of quantum computing with classical computing, quantum-inspired computing, and neuromorphic computing.
• Table 30. Different computing paradigms beyond conventional CMOS.
• Table 31. Applications of quantum algorithms.
• Table 32. QML approaches.
• Table 33. Modular vs. single core architectures
• Table 34. Heterogeneous architectural approaches by provider
• Table 35. Coherence times for different qubit implementations.
• Table 36. Superconducting Qubit Vendor Material Choices, 2026
• Table 37. Superconducting qubit market players.
• Table 38. Initialization, manipulation and readout for trapped ion quantum computers.
• Table 39. Trapped Ion Species Comparison, 2026
• Table 40. Trapped Ion Vendor Architecture Comparison, 2026
• Table 41. Ion trap market players.
• Table 42. Initialization, manipulation, and readout methods for silicon-spin qubits.
• Table 43. Silicon spin qubits market players.
• Table 44. Initialization, manipulation and readout of topological qubits.
• Table 45. Topological qubits market players.
• Table 46. Pros and cons of photon qubits.
• Table 47. Photonic Quantum Computing Architectural Classes, 2026
• Table 48. Photonic Qubit Initialization, Manipulation and Readout
• Table 49. Photonic Quantum Computing Race to Fault Tolerance — Tier Analysis
• Table 50. Photonic qubit market players.
• Table 51. Initialization, manipulation and readout for neutral-atom quantum computers.
• Table 52. Pros and cons of cold atoms quantum computers and simulators
• Table 53. Neural atom qubit market players.
• Table 54. Initialization, manipulation and readout of Diamond-Defect Spin-Based Computing.
• Table 55. Key materials for developing diamond-defect spin-based quantum computers.
• Table 56. Diamond-defect qubits market players.
• Table 57. Pros and cons of quantum annealers.
• Table 58. Quantum annealers market players.
• Table 59. Quantum computing infrastructure requirements
• Table 60. Major Commercial Quantum Cloud Platforms, 2026
• Table 61. Quantum Cloud Platform Market Forecast, 2026–2036 (millions USD)
• Table 62. Quantum computing software market players.
• Table 63. Market challenges in quantum computing.
• Table 64. Business models in quantum computing
• Table 65. Quantum Error Correcting Code Family Comparison
• Table 66. Recent Logical Qubit Demonstrations
• Table 67. Logical Qubit Roadmap by Vendor, 2026–2032
• Table 68. Magic State Distillation Resource Estimates
• Table 69. Resource Estimates for Reference Fault-Tolerant Algorithms (Current Best Estimates)
• Table 70. QEC-Related Market Forecast, 2026–2036 (millions USD)
• Table 71. Photonic Quantum Computing Deployment Models
• Table 72. Quantum computing value chain.
• Table 73. Markets and applications for quantum computing.
• Table 74. Market players in quantum technologies for pharmaceuticals.
• Table 75. Market players in quantum computing for chemicals.
• Table 76. Automotive applications of quantum computing,
• Table 77. Market players in quantum computing for transportation.
• Table 78. Market players in quantum computing for financial services
• Table 79. Market opportunities in quantum computing.
• Table 80. Major Quantum-Inspired Computing Vendors, 2026
• Table 81. Quantum vs Quantum-Inspired Comparison
• Table 82. Quantum-Inspired Computing Market Forecast, 2026–2036 (millions USD)
• Table 83. Applications in quantum chemistry and artificial intelligence (AI).
• Table 84. Market challenges in quantum chemistry and Artificial Intelligence (AI).
• Table 85. Market players in quantum chemistry and AI.
• Table 86. Market opportunities in quantum chemistry and AI.
• Table 87. Classical vs. quantum computing paradigms for machine learning
• Table 88. QML phases and evolution
• Table 89. QML approaches
• Table 90. Advantages of quantum machine learning
• Table 91. Challenges and limitations of QML
• Table 92. QML applications by industry
• Table 93. QML market players
• Table 94. QML market forecasts 2026–2036 (millions USD)
• Table 95. Comparison of analog and digital quantum simulation approaches
• Table 96. Quantum simulation platforms comparison
• Table 97. Applications of quantum simulation by industry
• Table 98. Applications in quantum chemistry and artificial intelligence
• Table 99. Market challenges in quantum chemistry simulation
• Table 100. Quantum simulation market players
• Table 101. Quantum simulation market forecasts 2026–2036 (millions USD)
• Table 102. Main types of quantum communications.
• Table 103. Applications in quantum communications.
• Table 104. QRNG entropy sources comparison
• Table 105. QRNG standards development
• Table 106. QRNG applications.
• Table 107. Key Players Developing QRNG Products.
• Table 108. Optical QRNG by company.
• Table 109. QRNG market forecasts 2026–2036 by application segment (millions USD)
• Table 110. QKD protocols comparison
• Table 111. Markets for QKD systems by end-use industry and delivery method 2026–2036 (millions USD)
• Table 112. Market players in post-quantum cryptography.
• Table 113. PQC market forecasts by cryptographic approach 2026–2036 (millions USD)
• Table 114. Quantum-Safe Migration Market Forecast, 2026–2036 (millions USD)
• Table 115. Reference Q-Day Estimates by Source, 2026
• Table 116. Global market for quantum communications by technology type 2026–2036 (millions USD)
• Table 117. Market challenges in quantum communications.
• Table 118. Market players in quantum communications.
• Table 119. Market opportunities in quantum communications.
• Table 120. Comparison between classical and quantum sensors.
• Table 121. Applications in quantum sensors.
• Table 122. Technology approaches for enabling quantum sensing
• Table 123. Value proposition for quantum sensors.
• Table 124. Key challenges and limitations of quartz crystal clocks vs. atomic clocks.
• Table 125. New modalities being researched to improve the fractional uncertainty of atomic clocks.
• Table 126. Global market for bench/rack-scale atomic clocks 2026–2036 (millions USD)
• Table 127. Global market for chip-scale atomic clocks 2026–2036 (millions USD)
• Table 128. Global market for atomic clocks 2026–2036 (billions USD)
• Table 129. Companies developing high-precision quantum time measurement
• Table 130. Key players in atomic clocks.
• Table 131. Comparative analysis of key performance parameters and metrics of magnetic field sensors.
• Table 132. Types of magnetic field sensors.
• Table 133. Market opportunity for different types of quantum magnetic field sensors.
• Table 134. Applications of SQUIDs.
• Table 135. Market opportunities for SQUIDs (Superconducting Quantum Interference Devices).
• Table 136. Key players in SQUIDs.
• Table 137. Applications of optically pumped magnetometers (OPMs).
• Table 138. Key players in Optically Pumped Magnetometers (OPMs).
• Table 139. Applications for TMR (Tunneling Magnetoresistance) sensors.
• Table 140. Market players in TMR (Tunneling Magnetoresistance) sensors.
• Table 141. Applications of N-V center magnetic field centers
• Table 142. Key players in N-V center magnetic field sensors.
• Table 143. Applications of quantum gravimeters
• Table 144. Comparative table between quantum gravity sensing and some other technologies commonly used for underground mapping.
• Table 145. Key players in quantum gravimeters.
• Table 146. Comparison of quantum gyroscopes with MEMs gyroscopes and optical gyroscopes.
• Table 147. Markets and applications for quantum gyroscopes.
• Table 148. Key players in quantum gyroscopes.
• Table 149. Types of quantum image sensors and their key features/.
• Table 150. Applications of quantum image sensors.
• Table 151. Key players in quantum image sensors.
• Table 152. Comparison of quantum radar versus conventional radar and lidar technologies.
• Table 153. Applications of quantum radar.
• Table 154. Single-photon detector technology comparison
• Table 155. SNSPD market players
• Table 156. Quantum sensor component categories and functions
• Table 157. Challenges for quantum sensor components
• Table 158. Value Proposition of Quantum RF Sensors
• Table 159. Types of Quantum RF Sensors
• Table 160. Markets for Quantum RF Sensors
• Table 161. Technology Transition Milestones.
• Table 162. Market and technology challenges in quantum sensing.
• Table 163. Global market for quantum sensors by sensor type 2018–2036 (Millions USD)
• Table 164. Extended forecast to 2046 (Millions USD)
• Table 165. Global market for quantum sensors by volume 2018–2046 (Units)
• Table 166. Global market for quantum sensors by sensor price 2025–2046 (Units)
• Table 167. Extended price segmentation to 2046 (Units — selected years)
• Table 168. Global market for quantum sensors by end-use industry 2018–2036 (Millions USD)
• Table 169. Extended forecast to 2046 (Millions USD)
• Table 170. Comparison between quantum batteries and other conventional battery types.
• Table 171. Types of quantum batteries.
• Table 172. Applications of quantum batteries.
• Table 173. Market challenges in quantum batteries.
• Table 174. Market players in quantum batteries.
• Table 175. Market opportunities in quantum batteries.
• Table 176. Total addressable market (TAM) for quantum technologies by sector
• Table 177. End-user industry investment in quantum readiness
• Table 178. Market players in quantum technologies for pharmaceuticals
• Table 179. Market players in quantum computing for financial services
• Table 180. Materials in Quantum Technology.
• Table 181. Superconductors in quantum technology.
• Table 182. Critical temperature of superconducting materials for quantum technology
• Table 183. Transmon superconducting qubit structure and materials
• Table 184. Summary of manufacturing processes for superconducting quantum chips
• Table 185. Defects and sources of noise for superconducting quantum circuits
• Table 186. Fabrication methods for SNSPDs
• Table 187. Photonics, silicon photonics and optics in quantum technology.
• Table 188. Quantum PIC material platforms benchmarked
• Table 189. PIC materials used by quantum technology companies
• Table 190. Nanomaterials in quantum technology.
• Table 191. Material advantages and disadvantages of diamond for quantum applications
• Table 192. Synthetic diamond value chain for quantum technology
• Table 193. Cryogenic Operating Temperature Requirements by Quantum Computing Modality
• Table 194. Dilution Refrigerator Pricing Bands by Configuration, 2026
• Table 195. Dilution Refrigerator Vendor Comparison, 2026
• Table 196. Dilution Refrigerator Lead Times, 2022 vs. 2026
• Table 197. Installed Base Forecast — Dilution Refrigerators by Region 2026–2036 (units, cumulative)
• Table 198. Helium-3 Annual Production by Source, 2026
• Table 199. Helium-3 Demand Forecast for Quantum Computing, 2026–2046
• Table 200. Helium-3 Supply-Demand Balance Forecast, 2026–2046 (litres STP per year)
• Table 201. Wiring Density Requirements vs. Cryogenic Cooling Budget
• Table 202. NVQLink Ecosystem Participation, 2026
• Table 203. Cryo-CMOS and Cryogenic Control Vendor Capabilities, 2026
• Table 204. Cryogenic Amplifier Performance Benchmarks
• Table 205. Cryo-CMOS Market Forecast, 2026–2036 (millions USD)
• Table 206. Required Laser Wavelengths by Quantum Computing Modality
• Table 207. Laser Linewidth Requirements by Application
• Table 208. Laser Vendor Capability Matrix, 2026
• Table 209. Single-Photon Detector Technology Comparison, 2026
• Table 210. PIC Material Platform Comparison for Quantum Applications
• Table 211. Vacuum Pressure Requirements by Modality
• Table 212. Optical Viewport Specifications and Suppliers
• Table 213. UHV Pump Type Selection Matrix
• Table 214. Vapour Cell and Atomic Source Suppliers
• Table 215. UHV Vendor Capability Matrix, 2026
• Table 216. Cryogenic Cable Type Comparison
• Table 217. High-Density Cryogenic Connector Comparison
• Table 218. Cryogenic Attenuator Pricing and Specifications
• Table 219. Cryogenic Interconnect Vendor Comparison, 2026
• Table 220. Bottleneck Heat-Map by Quantum Computing Modality
• Table 221. Bottleneck Mitigation Pathways
• Table 222. Superconducting Chip and Substrate Market Forecast, 2026–2036 (millions USD)
• Table 223. PIC and Optical Component Market Forecast, 2026–2036 (millions USD)
• Table 224. Cryogenic Infrastructure Market Forecast, 2026–2036 (millions USD)
• Table 225. Helium-3 and Helium-4 Market Forecast, 2026–2036 (millions USD, quantum applications only)
• Table 226. Cryogenic Control Electronics Market Forecast, 2026–2036 (millions USD)
• Table 227. Lasers and Single-Photon Detectors Market Forecast, 2026–2036 (millions USD)
• Table 228. UHV Systems Market Forecast, 2026–2036 (millions USD)
• Table 229. Cryogenic and Optical Interconnect Market Forecast, 2026–2036 (millions USD)
• Table 230. Diamond and Specialty Materials Market Forecast, 2026–2036 (millions USD)
• Table 231. Nanomaterials Market Forecast, 2026–2036 (millions USD)
• Table 232. Total Materials and Components Market Forecast, 2026–2036 (millions USD)
• Table 233. Global government quantum initiatives comparison
• Table 234. Global Market for Quantum Computing — Hardware, Software & Services 2025–2046 (billions USD)
• Table 235. Markets for Quantum Sensors by Type 2025–2046 (millions USD)
• Table 236. Markets for QKD Systems 2025–2046 (millions USD)
• Table 237. Global Market for Quantum Random Number Generators by Application 2025–2046 (millions USD)
• Table 238. Global Market for Post-Quantum Cryptography by Approach 2025–2046 (millions USD)
• Table 239. Global Market for Quantum Machine Learning by Segment 2025–2046 (millions USD)
• Table 240. Global Market for Quantum Simulation by Application 2025–2046 (millions USD)
• Table 241. Global Market for Quantum Batteries by Application 2025–2046 (millions USD)
• Table 242. Total Quantum Technology Market by Segment 2026–2046 (billions USD)
• Table 243. Quantum Technology Market by End-Use Industry 2026–2046 (billions USD)
• Table 244. Quantum Technology Market by Region 2026–2046 (billions USD)
• Table 245. Quantum Hardware Supply Chain Market by Category, 2026–2046 (millions USD)
• Table 246. Quantum Hardware Supply Chain Revenue by Region, 2026–2046 (millions USD)
• Table 247. Total Quantum Technology Market Including Supply Chain, 2026–2046 (billions USD)
• Table 248. Quantum Technology Compensation Benchmarks, 2026 (USD, total compensation including equity)
• Table 249. Quantum Workforce Market Forecast, 2026–2036 (millions USD)

List of Figures

• Figure 1. Quantum computing development timeline.
• Figure 2. Quantum Technology Market Map.
• Figure 3. Quantum computing architectures.
• Figure 4. An early design of an IBM 7-qubit chip based on superconducting technology.
• Figure 5. Various 2D to 3D chips integration techniques into chiplets.
• Figure 6. IBM Q System One quantum computer.
• Figure 7. Unconventional computing approaches.
• Figure 8. 53-qubit Sycamore processor.
• Figure 9. Interior of IBM quantum computing system. The quantum chip is located in the small dark square at center bottom.
• Figure 10. Superconducting quantum computer.
• Figure 11. Superconducting quantum computer schematic.
• Figure 12. Components and materials used in a superconducting qubit.
• Figure 13. SWOT analysis for superconducting quantum computers:.
• Figure 14. Ion-trap quantum computer.
• Figure 15. Various ways to trap ions.
• Figure 16. Universal Quantum’s shuttling ion architecture in their Penning traps.
• Figure 17. SWOT analysis for trapped-ion quantum computing.
• Figure 18. CMOS silicon spin qubit.
• Figure 19. Silicon quantum dot qubits.
• Figure 20. SWOT analysis for silicon spin quantum computers.
• Figure 21. SWOT analysis for topological qubits
• Figure 22 . SWOT analysis for photonic quantum computers.
• Figure 23. Neutral atoms (green dots) arranged in various configurations
• Figure 24. SWOT analysis for neutral-atom quantum computers.
• Figure 25. NV center components.
• Figure 26. SWOT analysis for diamond-defect quantum computers.
• Figure 27. D-Wave quantum annealer.
• Figure 28. SWOT analysis for quantum annealers.
• Figure 29. Quantum software development platforms.
• Figure 30. SWOT analysis for quantum computing.
• Figure 31. Technology roadmap for quantum computing 2025-2046.
• Figure 32. SWOT analysis for quantum chemistry and AI.
• Figure 33. Technology roadmap for quantum chemistry and AI 2025-2046.
• Figure 34. IDQ quantum number generators.
• Figure 35. SWOT Analysis of Quantum Random Number Generator Technology.
• Figure 36. SWOT Analysis of Quantum Key Distribution Technology.
• Figure 37. SWOT Analysis: Post Quantum Cryptography (PQC).
• Figure 38. SWOT analysis for networks.
• Figure 39. Technology roadmap for quantum communications 2025-2046.
• Figure 40. Q.ANT quantum particle sensor.
• Figure 41. SWOT analysis for quantum sensors market.
• Figure 42. NIST's compact optical clock.
• Figure 43. SWOT analysis for atomic clocks.
• Figure 44.Principle of SQUID magnetometer.
• Figure 45. SWOT analysis for SQUIDS.
• Figure 46. SWOT analysis for OPMs
• Figure 47. Tunneling magnetoresistance mechanism and TMR ratio formats.
• Figure 48. SWOT analysis for TMR (Tunneling Magnetoresistance) sensors.
• Figure 49. SWOT analysis for N-V Center Magnetic Field Sensors.
• Figure 50. Quantum Gravimeter.
• Figure 51. SWOT analysis for Quantum Gravimeters.
• Figure 52. SWOT analysis for Quantum Gyroscopes.
• Figure 53. SWOT analysis for Quantum image sensing.
• Figure 54. Principle of quantum radar.
• Figure 55. Illustration of a quantum radar prototype.
• Figure 56. Quantum RF Sensors Market Roadmap (2023-2046).
• Figure 57. Technology roadmap for quantum sensors 2025-2046.
• Figure 58. Schematic of the flow of energy (blue) from a source to a battery made up of multiple cells. (left)
• Figure 59. SWOT analysis for quantum batteries.
• Figure 60. Technology roadmap for quantum batteries 2025-2046.
• Figure 61. Market map for quantum technologies industry.
• Figure 62. Tech Giants quantum technologies activities.
• Figure 63. Archer-EPFL spin-resonance circuit.
• Figure 64. IBM Q System One quantum computer.
• Figure 65. ColdQuanta Quantum Core (left), Physics Station (middle) and the atoms control chip (right).
• Figure 66. Intel Tunnel Falls 12-qubit chip.
• Figure 67. IonQ's ion trap
• Figure 68. 20-qubit quantum computer.
• Figure 69. Maybell Big Fridge.
• Figure 70. PsiQuantum’s modularized quantum computing system networks.
• Figure 71. Quantum Brilliance device
• Figure 72. The Ez-Q Engine 2.0 superconducting quantum measurement and control system.
• Figure 73. Conceptual illustration (left) and physical mockup (right, at OIST) of Qubitcore’s distributed ion-trap quantum computer, visualizing quantum entanglement via optical fiber links between traps.
• Figure 74. Quobly's processor.
• Figure 75. SemiQ first chip prototype.
• Figure 76. SpinMagIC quantum sensor.
• Figure 77. Toshiba QKD Development Timeline.
• Figure 78. Toshiba Quantum Key Distribution technology.