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PUBLISHER: Future Markets, Inc. | PRODUCT CODE: 1814240

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PUBLISHER: Future Markets, Inc. | PRODUCT CODE: 1814240

Advanced Electronics Technologies for AI 2026-2036

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PAGES: 692 Pages, 173 Tables, 146 Figures
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Table of Contents

List of Tables

The artificial intelligence revolution stands at a critical inflection point. As AI applications proliferate across every sector of the global economy-from autonomous vehicles navigating complex urban environments to personalized medical diagnostics processing vast genomic datasets-the computational demands have outstripped the capabilities of traditional silicon-based architectures. The convergence of neuromorphic computing, quantum computing, and edge AI processors represents not merely an evolutionary advancement, but a fundamental paradigm shift that will determine the trajectory of artificial intelligence for the next decade and beyond. This technological convergence emerges from the recognition that different AI workloads require fundamentally different computational approaches. Traditional von Neumann architectures, which have powered the digital revolution for over half a century, face insurmountable challenges in meeting the diverse requirements of modern AI systems: the massive parallel processing demands of training large language models, the ultra-low latency requirements of autonomous systems, the energy constraints of mobile and IoT devices, and the real-time adaptation capabilities needed for dynamic environments.

The semiconductor industry's adherence to Moore's Law-the observation that transistor density doubles approximately every two years-has reached fundamental physical limits. As transistors approach atomic dimensions, quantum effects, manufacturing costs, and power density challenges have made continued scaling increasingly difficult. This limitation has profound implications for AI development, as the exponential growth in model complexity and data volumes can no longer be supported through traditional scaling approaches. The response has been a decisive shift toward domain-specific architectures optimized for particular AI workloads. Graphics Processing Units (GPUs) initiated this transformation by providing massively parallel processing capabilities for training deep neural networks. Tensor Processing Units (TPUs) followed, offering specialized acceleration for matrix operations core to machine learning algorithms. However, these solutions represent only the beginning of a more profound architectural revolution.

Neuromorphic computing draws inspiration from the human brain's remarkable efficiency and adaptability, implementing spiking neural networks that process information only when events occur, dramatically reducing power consumption compared to traditional continuously-operating processors. This event-driven processing paradigm proves particularly valuable for applications requiring always-on sensing and real-time adaptation, such as autonomous vehicles processing sensor data or IoT devices monitoring environmental conditions. The technology's commercial viability has been demonstrated through pioneering implementations including Intel's Loihi 2 neuromorphic research chip and IBM's TrueNorth processor. Startups like BrainChip have commercialized neuromorphic accelerators for edge AI applications, while companies like Prophesee have developed neuromorphic vision sensors capable of capturing high-speed motion with microsecond temporal resolution and minimal power consumption. Beyond energy efficiency, neuromorphic systems offer unique advantages in handling temporal data, performing in-memory computation, and enabling continuous learning without extensive retraining. These capabilities prove essential for applications ranging from industrial predictive maintenance to augmented reality systems requiring real-time environmental understanding.

Quantum computing represents perhaps the most revolutionary advancement in computational capability since the invention of digital computers. By leveraging quantum phenomena including superposition and entanglement, quantum systems can potentially solve certain classes of problems exponentially faster than classical computers. For artificial intelligence, this capability promises transformative advances in optimization, pattern recognition, and machine learning algorithm development. Quantum machine learning algorithms like quantum support vector machines and quantum neural networks demonstrate the potential for processing vast datasets more efficiently than classical approaches. Quantum optimization algorithms show particular promise for solving complex combinatorial problems common in AI applications, from drug discovery molecular simulations to financial portfolio optimization and supply chain management. Major technology companies including IBM, Google, and IonQ have developed increasingly sophisticated quantum processors, while cloud-based quantum computing services democratize access to quantum capabilities for AI researchers and developers. The integration of quantum and classical computing through hybrid architectures enables practical applications that leverage quantum advantages while maintaining compatibility with existing AI workflows. The proliferation of connected devices and the need for real-time AI processing has driven the development of specialized edge AI processors capable of running sophisticated algorithms directly on mobile devices, IoT sensors, and embedded systems. This distributed intelligence paradigm addresses critical limitations of cloud-based AI processing: network latency, bandwidth constraints, privacy concerns, and the need for autonomous operation in connectivity-challenged environments.

Edge AI processors employ diverse architectural approaches including dedicated neural processing units (NPUs), analog computing techniques, and neuromorphic processing elements optimized for specific workloads. Companies like NVIDIA with their Jetson ecosystem, Qualcomm with integrated AI accelerators, and startups like Mythic with analog matrix processors are pioneering solutions that deliver increasingly sophisticated AI capabilities within the power and size constraints of edge devices.

The convergence of these three technological domains creates unprecedented opportunities for solving AI's most challenging problems. Neuromorphic principles could enhance quantum error correction and control systems. Quantum algorithms might accelerate neuromorphic network training and optimization. Edge processors could enable hybrid quantum-classical computing workflows and distribute neuromorphic processing capabilities across IoT networks. This technological convergence is reshaping not only the capabilities of AI systems but also the economic dynamics of the technology industry. The market represents a fundamental shift from general-purpose computing platforms to specialized architectures optimized for specific AI workloads, creating new competitive dynamics and investment opportunities across the entire technology ecosystem.

"Advanced Electronics Technologies for AI 2026-2036" analyzes the convergence of three revolutionary electronics technologies reshaping the artificial intelligence landscape: neuromorphic computing, quantum computing, and edge AI processors. The report provides detailed market forecasts spanning 2026-2036, examining market dynamics across multiple technology vectors that collectively represent a transformative shift from conventional von Neumann architectures to specialized, brain-inspired, quantum-enhanced, and edge-distributed computing platforms. Our analysis reveals a rapidly accelerating market trajectory driven by exponential demand for energy-efficient, real-time AI processing capabilities across autonomous systems, healthcare applications, industrial automation, and smart city infrastructures.

Technology convergence analysis examines synergistic interactions between these three domains, identifying cross-platform opportunities where quantum algorithms enhance neuromorphic training, where edge processors enable hybrid quantum-classical workflows, and where neuromorphic principles improve quantum error correction systems. The report provides detailed assessments of hybrid computing architectures, multi-modal AI processing systems, and ecosystem standardization requirements driving interoperability across diverse computing platforms. Market segmentation delivers granular analysis across vertical applications including automotive (autonomous vehicles, ADAS), healthcare (medical devices, diagnostics, prosthetics), industrial IoT (predictive maintenance, quality control), smart cities (traffic management, environmental monitoring), aerospace/defense (UAVs, satellite imaging, cybersecurity), and data center infrastructure (high-performance computing, cloud services). Regional market analysis covers North America, Europe, Asia-Pacific, and emerging markets, examining technology adoption patterns, government initiatives, and investment landscapes.

Competitive landscape intelligence provides comprehensive profiles of >400 companies across all three technology domains. Neuromorphic computing profiles span chip manufacturers, sensor developers, memory technology providers, and software framework developers. Quantum computing coverage includes platform providers, specialized hardware companies, software developers, and materials suppliers. Edge AI processor analysis encompasses established semiconductor companies alongside innovative start-ups.

Investment analysis evaluates funding trends, strategic partnerships, and market opportunities across $2+ trillion in combined market potential through 2036. The report includes detailed venture capital analysis, government funding initiatives, corporate R&D investments, and strategic acquisition activity shaping competitive dynamics. Manufacturing capacity analysis addresses supply chain vulnerabilities, quality control procedures, and fabrication process requirements for next-generation computing architectures.

Report contents include:

  • Neuromorphic Computing
    • Market overview with global revenues 2024-2036 and segmentation analysis
    • Moore's Law limitations driving neuromorphic adoption
    • Technology architectures: spiking neural networks, memory approaches, hardware processors
    • Sensing technologies: event-based sensors, hybrid approaches, bio-inspired designs
    • Application markets: mobile/consumer, automotive, industrial, healthcare, aerospace/defense, datacenters
    • Competitive landscape with 144 company profiles
    • Regional market analysis and forecasts
    • Technology roadmaps and emerging trends
    • Investment landscape and strategic partnerships
    • Regulatory considerations and sustainability impact
  • Quantum Computing
    • First and second quantum revolution context
    • Current market landscape with technical progress assessment
    • Investment analysis covering $billions in funding 2024-2025
    • Global government initiatives across major economies
    • Business models and market dynamics
    • Hardware technologies: superconducting, trapped ion, silicon spin, photonic, topological qubits
    • Software stack and quantum algorithms
    • Infrastructure requirements and cloud services
    • Applications across pharmaceuticals, chemicals, transportation, financial services, automotive
    • Materials requirements: superconductors, photonics, nanomaterials
    • 200+ company profiles spanning entire value chain
  • Edge AI Processors
    • Market size evolution and geographic distribution
    • Technology architectures: NPUs, SoC integration, power optimization
    • Application analysis: industrial IoT, smartphones, automotive, smart cities, healthcare
    • Competitive landscape covering established players and startups
    • Market drivers: latency requirements, privacy imperatives, bandwidth limitations
    • 49 detailed company profiles
    • Technology trends and future roadmaps
  • Profiles of 401 companies. Companies profiled include ABR (Applied Brain Research), AiM Future, AI Storm, AlpsenTek, Amazon Web Services, Ambarella, Ambient Scientific, AMD, ANAFLASH, Analog Inference, AnotherBrain, Apple, ARM, Aryballe Technologies, Aspinity, Avalanche Technology, Axelera AI, Baidu, Beijing Xinzhida Neurotechnology, A* Quantum, AbaQus, Aegiq, Agnostiq, Airbus, Alice&Bob, Aliro Quantum, Alpine Quantum Technologies, Anyon Systems, Archer Materials, Arclight Quantum, Arctic Instruments, ARQUE Systems, Atlantic Quantum, Atom Computing, Atom Quantum Labs, Atos Quantum, Baidu, BEIT, Bifrost Electronics, Advanced Micro Devices, Alpha ICs, Amazon Web Services, Ambarella, Anaflash, Apple, Axelera AI, Axera Semiconductor, Blaize, BrainChip Holdings, Cerebras Systems, Corerain Technologies, DEEPX, DeGirum, EdgeCortix, Efinix, Enerzai, Google, Graphcore, GreenWaves Technologies and more.....
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TABLE OF CONTENTS

1. INTRODUCTION

  • 1.1. Neuromorphic-Quantum Computing Convergence Potential
  • 1.2. Edge AI and Neuromorphic System Integration
  • 1.3. Hybrid Computing Architecture Development
  • 1.4. Multi-Modal AI Processing System Evolution
  • 1.5. Ecosystem Standardization Requirements

2. NEUROMORPHIC COMPUTING

  • 2.1. Overview of the neuromorphic computing and sensing market
    • 2.1.1. Global Market Revenues 2024-2036
    • 2.1.2. Market segmentation
    • 2.1.3. Ending of Moore's Law
    • 2.1.4. Historical market
    • 2.1.5. Key market trends and growth drivers
    • 2.1.6. Market challenges and limitations
    • 2.1.7. Future outlook and opportunities
      • 2.1.7.1. Emerging trends
        • 2.1.7.1.1. Hybrid Neuromorphic-Conventional Computing and Sensing Systems
        • 2.1.7.1.2. Edge AI and IoT
        • 2.1.7.1.3. Quantum Computing
        • 2.1.7.1.4. Explainable AI
        • 2.1.7.1.5. Brain-Computer Interfaces
        • 2.1.7.1.6. Energy-efficient AI at scale
        • 2.1.7.1.7. Real-time learning and adaptation
        • 2.1.7.1.8. Enhanced Perception Systems
        • 2.1.7.1.9. Large-scale Neuroscience Simulations
        • 2.1.7.1.10. Secure, Decentralized AI
        • 2.1.7.1.11. Robotics that mimic humans
        • 2.1.7.1.12. Neural implants for healthcare
        • 2.1.7.1.13. New Application Areas and Use Cases
        • 2.1.7.1.14. Disruptive Business Models and Services
        • 2.1.7.1.15. Collaborative Ecosystem Development
        • 2.1.7.1.16. Skill Development and Workforce Training
      • 2.1.7.2. Technology roadmap
  • 2.2. Neuromorphic computing and generative AI
  • 2.3. Market value chain
  • 2.4. Market map
  • 2.5. Funding and investments
  • 2.6. Strategic Partnerships and Collaborations
  • 2.7. Regulatory and Ethical Considerations
    • 2.7.1. Data Privacy and Security
    • 2.7.2. Bias and Fairness in Neuromorphic Systems
    • 2.7.3. Intellectual Property and Patent Landscape
  • 2.8. Sustainability and Environmental Impact
    • 2.8.1. Carbon Footprint Analysis of Neuromorphic Systems
    • 2.8.2. Energy Efficiency Metrics and Benchmarking
    • 2.8.3. Green Manufacturing Practices
    • 2.8.4. End-of-life and Recycling Considerations
    • 2.8.5. Environmental Regulations Compliance
  • 2.9. Introduction
    • 2.9.1. Definition and concept of neuromorphic computing and sensing
    • 2.9.2. Main neuromorphic approaches
      • 2.9.2.1. Large-scale hardware neuromorphic computing systems
      • 2.9.2.2. Non-volatile memory technologies
      • 2.9.2.3. Advanced memristive materials and devices
    • 2.9.3. Fabrication Processes for Neuromorphic Systems
    • 2.9.4. Key Material Suppliers
    • 2.9.5. Supply Chain Vulnerabilities and Mitigation
    • 2.9.6. Manufacturing Capacity Analysis
    • 2.9.7. Quality Control and Testing Procedures
    • 2.9.8. Comparison with traditional computing and sensing approaches
    • 2.9.9. Neuromorphic computing vs. quantum computing
    • 2.9.10. Key features and advantages
      • 2.9.10.1. Low latency and real-time processing
      • 2.9.10.2. Power efficiency and energy savings
      • 2.9.10.3. Scalability and adaptability
      • 2.9.10.4. Online learning and autonomous decision-making
    • 2.9.11. Markets and Applications
      • 2.9.11.1. Edge AI and IoT
      • 2.9.11.2. Autonomous Vehicles and Robotics
      • 2.9.11.3. Cybersecurity and Anomaly Detection
      • 2.9.11.4. Smart Sensors and Monitoring Systems
      • 2.9.11.5. Datacenter and High-Performance Computing
  • 2.10. Neuromorphic Computing Technologies and Architecture
    • 2.10.1. Spiking Neural Networks (SNNs)
      • 2.10.1.1. Biological inspiration and principles
      • 2.10.1.2. Types of SNNs and their characteristics
      • 2.10.1.3. Advantages and limitations of SNNs
    • 2.10.2. Memory Architectures for Neuromorphic Computing
      • 2.10.2.1. Conventional memory approaches (SRAM, DRAM)
      • 2.10.2.2. Emerging non-volatile memory (eNVM) technologies
        • 2.10.2.2.1. Phase-Change Memory (PCM)
        • 2.10.2.2.2. Resistive RAM (RRAM)
        • 2.10.2.2.3. Magnetoresistive RAM (MRAM)
        • 2.10.2.2.4. Ferroelectric RAM (FeRAM)
      • 2.10.2.3. In-memory computing and near-memory computing
      • 2.10.2.4. Hybrid memory architectures
    • 2.10.3. Neuromorphic Hardware and Processors
      • 2.10.3.1. Digital neuromorphic processors
      • 2.10.3.2. Analog neuromorphic processors
      • 2.10.3.3. Mixed-signal neuromorphic processors
      • 2.10.3.4. FPGA-based neuromorphic systems
      • 2.10.3.5. Neuromorphic accelerators and co-processors
    • 2.10.4. Software and Frameworks for Neuromorphic Computing
      • 2.10.4.1. Neuromorphic programming languages and tools
      • 2.10.4.2. Neuromorphic simulation platforms and frameworks
      • 2.10.4.3. Neuromorphic algorithm libraries and repositories
      • 2.10.4.4. Neuromorphic software development kits (SDKs)
  • 2.11. Neuromorphic Sensing Technologies and Architectures
    • 2.11.1. Event-Based Sensors and Processing
      • 2.11.1.1. Neuromorphic vision sensors
      • 2.11.1.2. Neuromorphic auditory sensors
      • 2.11.1.3. Neuromorphic olfactory sensors
      • 2.11.1.4. Event-driven processing and algorithms
    • 2.11.2. Hybrid Sensing Approaches
      • 2.11.2.1. Combination of conventional and event-based sensors
      • 2.11.2.2. Fusion of multiple sensing modalities
      • 2.11.2.3. Advantages and challenges of hybrid sensing
    • 2.11.3. Neuromorphic Sensor Architectures and Designs
      • 2.11.3.1. Pixel-level processing and computation
      • 2.11.3.2. Sensor-processor co-design and integration
      • 2.11.3.3. Bio-inspired sensor designs and materials
    • 2.11.4. Signal Processing and Feature Extraction Techniques
      • 2.11.4.1. Spike-based Encoding and Decoding
      • 2.11.4.2. Temporal and Spatiotemporal Feature Extraction
      • 2.11.4.3. Neuromorphic Filtering and Denoising
      • 2.11.4.4. Adaptive and Learning-Based Processing
  • 2.12. Market Analysis and Forecasts
    • 2.12.1. Mobile and Consumer Applications
      • 2.12.1.1. Smartphones and wearables
      • 2.12.1.2. Smart home and IoT devices
      • 2.12.1.3. Consumer health and wellness
      • 2.12.1.4. Entertainment and gaming
    • 2.12.2. Automotive and Transportation
      • 2.12.2.1. Advanced Driver Assistance Systems (ADAS)
      • 2.12.2.2. Autonomous vehicles and robotaxis
      • 2.12.2.3. Vehicle infotainment and user experience
      • 2.12.2.4. Smart traffic management and infrastructure
    • 2.12.3. Industrial and Manufacturing
      • 2.12.3.1. Industrial IoT and smart factories
      • 2.12.3.2. Predictive maintenance and anomaly detection
      • 2.12.3.3. Quality control and inspection
      • 2.12.3.4. Logistics and supply chain optimization
    • 2.12.4. Healthcare and Medical Devices
      • 2.12.4.1. Medical imaging and diagnostics
      • 2.12.4.2. Wearable health monitoring devices
      • 2.12.4.3. Personalized medicine and drug discovery
      • 2.12.4.4. Assistive technologies and prosthetics
    • 2.12.5. Aerospace and Defense
      • 2.12.5.1. Unmanned Aerial Vehicles (UAVs) and drones
      • 2.12.5.2. Satellite imaging and remote sensing
      • 2.12.5.3. Missile guidance and target recognition
      • 2.12.5.4. Cybersecurity and threat detection:
    • 2.12.6. Datacenters and Cloud Services
      • 2.12.6.1. High-performance computing and scientific simulations:
      • 2.12.6.2. Big data analytics and machine learning
      • 2.12.6.3. Cloud-based AI services and platforms
      • 2.12.6.4. Energy-efficient datacenter infrastructure
    • 2.12.7. Regional Market Analysis and Forecasts
    • 2.12.8. Competitive Landscape and Key Players
      • 2.12.8.1. Overview of the Neuromorphic Computing and Sensing Ecosystem
      • 2.12.8.2. Neuromorphic Chip Manufacturers and Processors
      • 2.12.8.3. Neuromorphic Sensor Manufacturers
      • 2.12.8.4. Emerging Non-Volatile Memory (eNVM) Manufacturers
      • 2.12.8.5. Neuromorphic Software and Framework Providers
      • 2.12.8.6. Research Institutions and Academia
    • 2.12.9. Competing Emerging Technologies
      • 2.12.9.1. Quantum Computing
      • 2.12.9.2. Photonic Computing
      • 2.12.9.3. DNA Computing
      • 2.12.9.4. Spintronic Computing
      • 2.12.9.5. Chemical Computing
      • 2.12.9.6. Superconducting Computing
      • 2.12.9.7. Analog AI Chips
      • 2.12.9.8. In-Memory Computing
      • 2.12.9.9. Reversible Computing
      • 2.12.9.10. Quantum Dot Computing
      • 2.12.9.11. Technology Substitution Analysis
      • 2.12.9.12. Migration Pathways
      • 2.12.9.13. Comparative Advantages/Disadvantages
  • 2.13. Neuromorphic Computing Company Profiles (144 company profiles)

3. QUANTUM COMPUTING

  • 3.1. First and Second quantum revolutions
  • 3.2. Current quantum computing market landscape
    • 3.2.1. Technical Progress and Persistent Challenges
    • 3.2.2. Key developments
  • 3.3. Investment Landscape
    • 3.3.1. Quantum Technologies Investments 2024-2025
  • 3.4. Global Government Initiatives
  • 3.5. Market Landscape
  • 3.6. Recent Quantum Computing Industry Developments 2023-2025
  • 3.7. End Use Markets and Benefits of Quantum Computing
  • 3.8. Business Models
  • 3.9. Roadmap
  • 3.10. Challenges for Quantum Technologies Adoption
  • 3.11. SWOT analysis
  • 3.12. Quantum Computing Value Chain
  • 3.13. Quantum Computing and Artificial Intelligence
  • 3.14. Global market forecast 2025-2046
    • 3.14.1. Revenues
    • 3.14.2. Installed Base Forecast
      • 3.14.2.1. By system
      • 3.14.2.2. By technology
    • 3.14.3. Pricing
    • 3.14.4. Hardware
      • 3.14.4.1. By system
      • 3.14.4.2. By technology
    • 3.14.5. Quantum Computing in Data centres
  • 3.15. Introduction
    • 3.15.1. What is quantum computing?
    • 3.15.2. Operating principle
    • 3.15.3. Classical vs quantum computing
    • 3.15.4. Quantum computing technology
      • 3.15.4.1. Quantum emulators
      • 3.15.4.2. Quantum inspired computing
      • 3.15.4.3. Quantum annealing computers
      • 3.15.4.4. Quantum simulators
      • 3.15.4.5. Digital quantum computers
      • 3.15.4.6. Continuous variables quantum computers
      • 3.15.4.7. Measurement Based Quantum Computing (MBQC)
      • 3.15.4.8. Topological quantum computing
      • 3.15.4.9. Quantum Accelerator
    • 3.15.5. Competition from other technologies
    • 3.15.6. Market Overview
      • 3.15.6.1. Investment in Quantum Computing
      • 3.15.6.2. Business Models
        • 3.15.6.2.1. Quantum as a Service (QaaS)
        • 3.15.6.2.2. Strategic partnerships
        • 3.15.6.2.3. Vertically integrated and modular
        • 3.15.6.2.4. Mixed quantum stacks
      • 3.15.6.3. Semiconductor Manufacturers
  • 3.16. Quantum Algorithms
    • 3.16.1. Quantum Software Stack
      • 3.16.1.1. Quantum Machine Learning
      • 3.16.1.2. Quantum Simulation
      • 3.16.1.3. Quantum Optimization
      • 3.16.1.4. Quantum Cryptography
        • 3.16.1.4.1. Quantum Key Distribution (QKD)
        • 3.16.1.4.2. Post-Quantum Cryptography
  • 3.17. Quantum Computing Hardware
    • 3.17.1. Qubit Technologies
      • 3.17.1.1. Overview
      • 3.17.1.2. Noise effects
      • 3.17.1.3. Logical qubits
      • 3.17.1.4. Quantum Volume
      • 3.17.1.5. Algorithmic Qubits
      • 3.17.1.6. Superconducting Qubits
        • 3.17.1.6.1. Technology description
        • 3.17.1.6.2. Initialization, Manipulation, and Readout
        • 3.17.1.6.3. Materials
        • 3.17.1.6.4. Market players
        • 3.17.1.6.5. Roadmap
        • 3.17.1.6.6. Swot analysis
      • 3.17.1.7. Trapped Ion Qubits
        • 3.17.1.7.1. Technology description
        • 3.17.1.7.2. Initialization, Manipulation, and Readout
        • 3.17.1.7.3. Hardware
        • 3.17.1.7.4. Materials
          • 3.17.1.7.4.1. Integrating optical components
          • 3.17.1.7.4.2. Incorporating high-quality mirrors and optical cavities
          • 3.17.1.7.4.3. Engineering the vacuum packaging and encapsulation
          • 3.17.1.7.4.4. Removal of waste heat
        • 3.17.1.7.5. Roadmap
        • 3.17.1.7.6. Market players
        • 3.17.1.7.7. Swot analysis
      • 3.17.1.8. Silicon Spin Qubits
        • 3.17.1.8.1. Technology description
        • 3.17.1.8.2. Initialization, Manipulation, and Readout
        • 3.17.1.8.3. Integration with CMOS Electronics
        • 3.17.1.8.4. Quantum dots
        • 3.17.1.8.5. Market players
        • 3.17.1.8.6. SWOT analysis
      • 3.17.1.9. Topological Qubits
        • 3.17.1.9.1. Technology description
          • 3.17.1.9.1.1. Cryogenic cooling
        • 3.17.1.9.2. Initialization, Manipulation, and Readout of Topological Qubits
        • 3.17.1.9.3. Scaling topological qubit arrays
        • 3.17.1.9.4. Roadmap
        • 3.17.1.9.5. Market players
        • 3.17.1.9.6. SWOT analysis
      • 3.17.1.10. Photonic Qubits
        • 3.17.1.10.1. Photonics for Quantum Computing
        • 3.17.1.10.2. Technology description
        • 3.17.1.10.3. Initialization, Manipulation, and Readout
        • 3.17.1.10.4. Hardware Architecture
        • 3.17.1.10.5. Roadmap
        • 3.17.1.10.6. Market players
        • 3.17.1.10.7. Swot analysis
      • 3.17.1.11. Neutral atom (cold atom) qubits
        • 3.17.1.11.1. Technology description
        • 3.17.1.11.2. Market players
        • 3.17.1.11.3. Swot analysis
      • 3.17.1.12. Diamond-defect qubits
        • 3.17.1.12.1. Technology description
        • 3.17.1.12.2. SWOT analysis
        • 3.17.1.12.3. Market players
      • 3.17.1.13. Quantum annealers
        • 3.17.1.13.1. Technology description
        • 3.17.1.13.2. Initialization and Readout of Quantum Annealers
        • 3.17.1.13.3. Solving combinatorial optimization
        • 3.17.1.13.4. Applications
        • 3.17.1.13.5. Roadmap
        • 3.17.1.13.6. SWOT analysis
        • 3.17.1.13.7. Market players
    • 3.17.2. Architectural Approaches
  • 3.18. Quantum Computing Infrastructure
    • 3.18.1. Infrastructure Requirements
    • 3.18.2. Hardware agnostic platforms
    • 3.18.3. Cryostats
    • 3.18.4. Qubit readout
  • 3.19. Quantum Computing Software
    • 3.19.1. Technology description
    • 3.19.2. Cloud-based services- QCaaS (Quantum Computing as a Service)
    • 3.19.3. Market players
  • 3.20. Markets and Applications for Quantum Computing
    • 3.20.1. Pharmaceuticals
      • 3.20.1.1. Market overview
        • 3.20.1.1.1. Drug discovery
        • 3.20.1.1.2. Diagnostics
        • 3.20.1.1.3. Molecular simulations
        • 3.20.1.1.4. Genomics
        • 3.20.1.1.5. Proteins and RNA folding
      • 3.20.1.2. Market players
    • 3.20.2. Chemicals
      • 3.20.2.1.1. Market overview
      • 3.20.2.2. Market players
    • 3.20.3. Transportation
      • 3.20.3.1. Market overview
      • 3.20.3.2. Market players
    • 3.20.4. Financial services
      • 3.20.4.1. Market overview
      • 3.20.4.2. Market players
    • 3.20.5. Automotive
      • 3.20.5.1. Market overview
      • 3.20.5.2. Market players
    • 3.20.6. Other Crossover Technologies
      • 3.20.6.1. Quantum chemistry and AI
        • 3.20.6.1.1. Technology description
        • 3.20.6.1.2. Applications
        • 3.20.6.1.3. Market players
      • 3.20.6.2. Quantum Communications
        • 3.20.6.2.1. Technology description
        • 3.20.6.2.2. Types
        • 3.20.6.2.3. Applications
        • 3.20.6.2.4. Market players
      • 3.20.6.3. Quantum Sensors
        • 3.20.6.3.1. Technology description
        • 3.20.6.3.2. Applications
        • 3.20.6.3.3. Companies
    • 3.20.7. Quantum Computing and AI
      • 3.20.7.1. Introduction
      • 3.20.7.2. Applications
      • 3.20.7.3. AI Interfacing with Quantum Computing
      • 3.20.7.4. AI in Classical Computing
      • 3.20.7.5. Market Players and Strategies
      • 3.20.7.6. Relationship between quantum computing and artificial intelligence
    • 3.20.8. Materials for Quantum Computing
      • 3.20.8.1. Superconductors
        • 3.20.8.1.1. Overview
        • 3.20.8.1.2. Types and Properties
        • 3.20.8.1.3. Temperature (Tc) of superconducting materials
        • 3.20.8.1.4. Superconducting Nanowire Single Photon Detectors (SNSPD)
        • 3.20.8.1.5. Kinetic Inductance Detectors (KIDs)
        • 3.20.8.1.6. Transition Edge Sensors (TES)
        • 3.20.8.1.7. Opportunities
      • 3.20.8.2. Photonics, Silicon Photonics and Optical Components
        • 3.20.8.2.1. Overview
        • 3.20.8.2.2. Types and Properties
        • 3.20.8.2.3. Vertical-Cavity Surface-Emitting Lasers (VCSELs)
        • 3.20.8.2.4. Alkali azides
        • 3.20.8.2.5. Optical Fiber and Quantum Interconnects
        • 3.20.8.2.6. Semiconductor Single Photon Detectors
        • 3.20.8.2.7. Opportunities
      • 3.20.8.3. Nanomaterials
        • 3.20.8.3.1. Overview
        • 3.20.8.3.2. Types and Properties
          • 3.20.8.3.2.1. 2D Materials
          • 3.20.8.3.2.2. Transition metal dichalcogenide quantum dots
          • 3.20.8.3.2.3. Graphene Membranes
          • 3.20.8.3.2.4. 2.5D materials
          • 3.20.8.3.2.5. Carbon nanotubes
            • 3.20.8.3.2.5.1. Single Walled Carbon Nanotubes
            • 3.20.8.3.2.5.2. Boron Nitride Nanotubes
          • 3.20.8.3.2.6. Diamond
          • 3.20.8.3.2.7. Metal-Organic Frameworks (MOFs)
        • 3.20.8.3.3. Opportunities
    • 3.20.9. Market Analysis
      • 3.20.9.1. Key industry players
        • 3.20.9.1.1. Start-ups
        • 3.20.9.1.2. Tech Giants
        • 3.20.9.1.3. National Initiatives
  • 3.21. Quantum Computing Company Profiles (218 company profiles)

4. EDGE AI PROCESSORS

  • 4.1. Market overview
    • 4.1.1. Market Size
    • 4.1.2. Geographic Market
    • 4.1.3. Technology Architecture Evolution Timeline
  • 4.2. Edge AI Technology Architectures
    • 4.2.1. Neural Processing Unit (NPU) Implementations
    • 4.2.2. System-on-Chip (SoC) Integration Strategies
    • 4.2.3. Power Efficiency and Performance Optimization
      • 4.2.3.1. Sub-7W Thermal Envelope Requirements
      • 4.2.3.2. TOPS/W Optimization Methodologies
      • 4.2.3.3. Model Compression and Quantization
    • 4.2.4. Analog Computing and In-Memory Processing
    • 4.2.5. Dedicated Neural Processing Unit Architectures
    • 4.2.6. GPU-Based Edge Solutions vs. Specialized DPUs
  • 4.3. Application Market Analysis
    • 4.3.1. Industrial IoT and Manufacturing Applications
      • 4.3.1.1. Predictive Maintenance Systems
      • 4.3.1.2. Quality Control and Inspection
      • 4.3.1.3. Real-time Analytics and Optimization
    • 4.3.2. Smartphone and Mobile Device Integration
      • 4.3.2.1. AI-Capable CPU Integration
      • 4.3.2.2. Specialized AI Accelerator Implementation
      • 4.3.2.3. Always-On Processing Capabilities
    • 4.3.3. Automotive and Transportation Systems
    • 4.3.4. Smart Cities and Infrastructure Applications
    • 4.3.5. Healthcare and Wearable Device Integration
    • 4.3.6. Consumer Electronics and Home Automation
  • 4.4. Competitive Landscape and Market Players
    • 4.4.1. Established Semiconductor Giants
      • 4.4.1.1. NVIDIA
      • 4.4.1.2. Intel
      • 4.4.1.3. Qualcomm
      • 4.4.1.4. Xilinx
    • 4.4.2. AI-Focused Startup Companies
      • 4.4.2.1. Mythic
      • 4.4.2.2. Syntiant
      • 4.4.2.3. Kneron
      • 4.4.2.4. DeepX
    • 4.4.3. Cloud Provider Edge Solutions
      • 4.4.3.1. Google Edge TPU
      • 4.4.3.2. AWS Inferentia
  • 4.5. Market Drivers and Technology Trends
    • 4.5.1. Latency Requirements and Real-Time Processing Demands
    • 4.5.2. Data Privacy and Security Imperative Analysis
    • 4.5.3. Bandwidth Limitation and Connectivity Challenge Solutions
    • 4.5.4. IoT Device Proliferation Impact Assessment
    • 4.5.5. Edge-Cloud Computing Architecture Evolution
    • 4.5.6. Power Efficiency and Battery Life Optimization
    • 4.5.7. Autonomous System Processing Requirements
  • 4.6. Edge AI Processor Company Profiles (49 company profiles)

5. REFERENCES

Show more

List of Tables

  • Table 1. Overview of the neuromorphic computing and sensing market
  • Table 2. Global market for neuromorphic computing and sensors, 2024-2036 (Millions USD)
  • Table 3. Neuromorphic Computing and Sensing Market Segmentation 2020-2036
  • Table 4. Key market trends and growth drivers
  • Table 5. Market challenges and limitations
  • Table 6. Emerging Trends in Neuromorphic Computing and Sensing
  • Table 7. Neuromorphic computing and generative AI strategies
  • Table 8. Funding and investments in neuromorphic computing and sensing
  • Table 9. Strategic Partnerships and Collaborations in the Neuromorphic Industry
  • Table 10. Regulatory and Ethical Considerations of neuromorphic computing & sensing
  • Table 11. Main neuromorphic sensing approaches
  • Table 12. Main Neuromorphic Computing Approaches
  • Table 13. Resistive Non-Volatile Memory (NVM) Technologies
  • Table 14. Advanced Memristive Materials, Devices, and Novel Computation Concepts
  • Table 15. Fabrication Processes for Neuromorphic Systems
  • Table 16. Key Material Suppliers and Dependencies
  • Table 17. Comparison with traditional computing and sensing approaches
  • Table 18. Comparison between neuromorphic and quantum computing
  • Table 19. Key features and advantages of neuromorphic computing and sensing
  • Table 20. Markets and Applications of Neuromorphic Computing and Sensing
  • Table 21. Von neumann architecture versus neuromorphic architecture
  • Table 22. Types of SNNs and their characteristics
  • Table 23. Advantages and limitations of SNNs
  • Table 24. Conventional memory approaches (SRAM, DRAM)
  • Table 25. Emerging non-volatile memory (eNVM) technologies
  • Table 26. Hybrid memory architectures
  • Table 27. Neuromorphic accelerators and co-processors
  • Table 28. Neuromorphic programming languages and tools
  • Table 29. Neuromorphic simulation platforms and frameworks
  • Table 30. Neuromorphic algorithm libraries and repositories
  • Table 31. Neuromorphic software development kits (SDKs)
  • Table 32. Hybrid sensing approaches
  • Table 33. Advantages and challenges of hybrid sensing
  • Table 34. Bio-inspired sensor designs and materials
  • Table 35. Signal Processing and Feature Extraction Techniques
  • Table 36. Applications of neuromorphic computing and sensing in smartphones and wearables-advantages, limitations and likelihood of market penetration by application
  • Table 37. Applications of neuromorphic computing and sensing in smart homes and IoT devices- advantages, limitations and likelihood of market penetration by application
  • Table 38. Applications of neuromorphic computing and sensing in Consumer Health and Wellness-- advantages, limitations and likelihood of market penetration by application
  • Table 39. Applications of neuromorphic computing and sensing in Entertainment and Gaming-advantages, limitations and likelihood of market penetration by application
  • Table 40. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Mobile and Consumer Applications (2024-2036), millions USD
  • Table 41. Applications of neuromorphic computing and sensing in Advanced Driver Assistance Systems (ADAS) -advantages, limitations and likelihood of market penetration by application
  • Table 42. Applications of neuromorphic computing and sensing in Autonomous Vehicles and Robotaxis-advantages, limitations and likelihood of market penetration by application
  • Table 43. Applications of neuromorphic computing and sensing in Vehicle infotainment and user experience-advantages, limitations and likelihood of market penetration by application
  • Table 44. Applications of neuromorphic computing and sensing in Vehicle infotainment and user experience-advantages, limitations and likelihood of market penetration by application
  • Table 45. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Automotive and Transportation (2024-2036), millions USD
  • Table 46. Applications of neuromorphic computing and sensing in Industrial IoT and smart factories-advantages, limitations and likelihood of market penetration by application
  • Table 47. Applications of neuromorphic computing and sensing in Industrial IoT and smart factories-advantages, limitations and likelihood of market penetration by application
  • Table 48. Applications of neuromorphic computing and sensing in Quality control and inspection-advantages, limitations and likelihood of market penetration by application
  • Table 49. Applications of neuromorphic computing and sensing in Logistics and supply chain optimization-advantages, limitations and likelihood of market penetration by application
  • Table 50. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Industrial and Manufacturing (2024-2036), millions USD
  • Table 51. Applications of neuromorphic computing and sensing in medical imaging and diagnostics-advantages, limitations and likelihood of market penetration by application
  • Table 52. Applications of neuromorphic computing and sensing in Wearable health monitoring devices-advantages, limitations and likelihood of market penetration by application
  • Table 53. Applications of neuromorphic computing and sensing in Personalized medicine and drug discovery-advantages, limitations and likelihood of market penetration by application
  • Table 54. Applications of neuromorphic computing and sensing in Assistive technologies and prosthetics -advantages, limitations and likelihood of market penetration by application
  • Table 55. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Healthcare and Medical Devices (2024-2036), millions USD
  • Table 56. Applications of neuromorphic computing and sensing in Unmanned Aerial Vehicles (UAVs) and drones-advantages, limitations and likelihood of market penetration by application
  • Table 57. Applications of neuromorphic computing and sensing in Satellite imaging and remote sensing:-advantages, limitations and likelihood of market penetration by application
  • Table 58. Applications of neuromorphic computing and sensing in Missile guidance and target recognition -advantages, limitations and likelihood of market penetration by application
  • Table 59. Applications of neuromorphic computing and sensing in Cybersecurity and threat detection -advantages, limitations and likelihood of market penetration by application
  • Table 60. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Aerospace and Defence (2024-2036), millions USD
  • Table 61. Applications of neuromorphic computing and sensing in High-performance computing and scientific simulations-advantages, limitations and likelihood of market penetration by application
  • Table 62. Applications of neuromorphic computing and sensing in Big data analytics and machine learning-advantages, limitations and likelihood of market penetration by application
  • Table 63. Applications of neuromorphic computing and sensing in Cloud-based AI services and platforms -advantages, limitations and likelihood of market penetration by application
  • Table 64. Applications of neuromorphic computing and sensing in Energy-efficient datacenter infrastructure-advantages, limitations and likelihood of market penetration by application
  • Table 65. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Datacenters and Cloud Services (2024-2036), millions USD
  • Table 66. Market revenues for neuromorphic computing and sensing by region from 2024-2036 in millions USD
  • Table 71. Neuromorphic Chip Manufacturers and Their Product Offerings
  • Table 72. Neuromorphic Sensor Manufacturers and Their Product Offerings
  • Table 73. Emerging Non-Volatile Memory (eNVM) Manufacturers and Their Product Offerings
  • Table 74. Neuromorphic Software and Framework Providers and Their Solutions
  • Table 75. Key Research Institutions and Academia in Neuromorphic Computing and Sensing
  • Table 76. Competing Emerging Technologies for Neuromorphic Computing and Sensing
  • Table 77. Technology Substitution Analysis
  • Table 78. Comparative Advantages/Disadvantages
  • Table 79. Evolution of Apple Neural Engine
  • Table 80. Dynex subscription plans
  • Table 81. First and second quantum revolutions
  • Table 82. Applications for Quantum Computing
  • Table 83. Quantum Computing Business Models
  • Table 84. Quantum Computing Investments 2024-2025
  • Table 85. Global government initiatives in quantum technologies
  • Table 86. Quantum computing industry developments 2023-2025
  • Table 87. End Use Markets and Benefits of Quantum Computing
  • Table 88. Business Models in Quantum Computing
  • Table 89. Market challenges in quantum computing
  • Table 90. Quantum computing value chain
  • Table 91. Global market for quantum computing-by category, 2023-2046 (billions USD)
  • Table 92. Global Revenue from Quantum Computing Hardware (Billions USD)
  • Table 93. Quantum Computer Installed Base Forecast (2025-2046)-Units
  • Table 94. Forecast for Installed Base of Quantum Computers by Technology, 2025-2046-Units
  • Table 95. Quantum Computer Pricing Forecast (Millions USD) by system type
  • Table 96. Forecast for Quantum Computer Pricing 2026-2046 by system category
  • Table 97. Forecast for Annual Revenue from Quantum Computer Hardware Sales, 2025-2046 (billions USD)
  • Table 98. Forecast for Annual Revenue from Quantum Computing Hardware Sales (by Technology), 2025-2046
  • Table 99. Install Base of Quantum Computers vs Global Number of Data Centres to 2046
  • Table 100. Forecast for Volume of Quantum Computers Deployed in Data Centres, 2025-2046
  • Table 101. Quantum Computing Approaches
  • Table 102. Quantum Computer Architectures
  • Table 103. Applications for quantum computing
  • Table 104. Comparison of classical versus quantum computing
  • Table 105. Key quantum mechanical phenomena utilized in quantum computing
  • Table 106. Types of quantum computers
  • Table 107. Comparison of Quantum Computer Technologies
  • Table 108. Comparative analysis of quantum computing with classical computing, quantum-inspired computing, and neuromorphic computing
  • Table 109. Different computing paradigms beyond conventional CMOS
  • Table 110. Applications of quantum algorithms
  • Table 111. QML approaches
  • Table 112. Commercial Readiness Level by Technology
  • Table 113. Qubit Performance Benchmarking
  • Table 114. Coherence times for different qubit implementations
  • Table 115. Quantum Computer Benchmarking Metrics
  • Table 116. Logical Qubit Progress
  • Table 117. Superconducting Materials Properties
  • Table 118. Superconducting qubit market players
  • Table 119. Initialization, manipulation and readout for trapped ion quantum computers
  • Table 120. Ion trap market players
  • Table 121. Initialization, manipulation, and readout methods for silicon-spin qubits
  • Table 122. Silicon spin qubits market players
  • Table 123. Initialization, manipulation and readout of topological qubits
  • Table 124. Topological qubits market players
  • Table 125. Pros and cons of photon qubits
  • Table 126. Comparison of photon polarization and squeezed states
  • Table 127. Initialization, manipulation and readout of photonic platform quantum computers
  • Table 128. Photonic qubit market players
  • Table 129. Initialization, manipulation and readout for neutral-atom quantum computers
  • Table 130. Pros and cons of cold atoms quantum computers and simulators
  • Table 131. Neural atom qubit market players
  • Table 132. Initialization, manipulation and readout of Diamond-Defect Spin-Based Computing
  • Table 133. Key materials for developing diamond-defect spin-based quantum computers
  • Table 134. Diamond-defect qubits market players
  • Table 135. Commercial Applications for Quantum Annealing
  • Table 136. Pros and cons of quantum annealers
  • Table 137. Quantum annealers market players
  • Table 138. Quantum Computing Infrastructure Requirements
  • Table 139. Modular vs. Single Core
  • Table 140. Quantum computing software market players
  • Table 141. Markets and applications for quantum computing
  • Table 142. Total Addressable Market (TAM) for Quantum Computing
  • Table 143. Market players in quantum technologies for pharmaceuticals
  • Table 144. Market players in quantum computing for chemicals
  • Table 145. Automotive applications of quantum computing,
  • Table 146. Market players in quantum computing for transportation
  • Table 147. Quantum Computing in Finance
  • Table 148. Market players in quantum computing for financial services
  • Table 149. Automotive Applications of Quantum Computing
  • Table 150. Applications in quantum chemistry and artificial intelligence (AI)
  • Table 151. Market players in quantum chemistry and AI
  • Table 152. Main types of quantum communications
  • Table 153. Applications in quantum communications
  • Table 154. Market players in quantum communications
  • Table 155. Comparison between classical and quantum sensors
  • Table 156. Applications in quantum sensors
  • Table 157. Companies developing high-precision quantum time measurement
  • Table 158. Materials in Quantum Technology
  • Table 159. Superconductor Value Chain in Quantum Technology
  • Table 160. Superconductors in quantum technology
  • Table 161. SNSPD Players companies
  • Table 162. Single Photon Detector Technology Comparison
  • Table 163. Photonics, silicon photonics and optics in quantum technology
  • Table 164. Materials for Quantum Photonic Applications
  • Table 165. Nanomaterials in quantum technology
  • Table 166. Synthetic Diamond Value Chain for Quantum Technology
  • Table 169. Platform-Specific Revenue Analysis
  • Table 170. TOPS/W Optimization Methodologies
  • Table 171. AMD AI chip range
  • Table 172. Applications of CV3-AD685 in autonomous driving
  • Table 173. Evolution of Apple Neural Engine

List of Figures

  • Figure 1. Global market for neuromorphic computing and sensors, 2023-2036 (Millions USD)
  • Figure 2. Neuromorphic Computing and Sensing Market Segmentation 2020-2036
  • Figure 3. Neuromorphic computing and sensing technology roadmap
  • Figure 4. Market value chain for neuromorphic computing and sensing
  • Figure 5. Neuromorphic computing and sensing market map
  • Figure 6. Evolution of the main hardware technologies for neuromorphic computing
  • Figure 7. Key materials in NVM technology for neuromorphic computing
  • Figure 8. Advanced memristive materials for neuromorphic computing
  • Figure 9. Neural networks in autonomous vehicles
  • Figure 10. Concept illustration of centralized and decentralized intelligence in robotics
  • Figure 11. Neuromorphic programmable robot with dynamic vision developed by SynSense
  • Figure 12. Comparison of High-Level Conventional and Neuromorphic Memory Architectures
  • Figure 13. Spiking Neural Network (SNN) Structure and Operation
  • Figure 14. IBM TrueNorth Processor
  • Figure 15. Event-Based Sensor Operation and Data Processing Flow
  • Figure 16. Conventional sensor vs. Event-based sensor
  • Figure 17. Operation of neuromorphic vision sensors
  • Figure 18. Cyranose 320 Electronic Nose
  • Figure 19. Alpix-Pilatus platform, an integrated event-based vision sensor that combines static and dynamic information
  • Figure 20. Technology roadmap for neuromorphic computing and sensing in mobile and consumer applications
  • Figure 21. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Mobile and Consumer Applications (2024-2036), millions USD
  • Figure 22. Technology Roadmap for Neuromorphic Computing and Sensing in Automotive and Transportation
  • Figure 23. Sensors used by the ADAS (Advanced Driver-Assistance System)
  • Figure 24. Enabling technologies for autonomous vehicles
  • Figure 25. Autonomous Vehicle Architecture with Neuromorphic Computing and Sensing
  • Figure 26. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Automotive and Transportation (2024-2036), millions USD
  • Figure 27. Technology roadmap for neuromorphic computing and sensing in industrial and manufacturing
  • Figure 28. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Industrial and Manufacturing (2024-2036), millions USD
  • Figure 29. Technology roadmap for neuromorphic computing and sensing in healthcare and medical devices
  • Figure 30. Wearable Medical Devices with Neuromorphic Computing and Sensing Capabilities
  • Figure 31. Flexible neuromorphic electronics for neuromorphic computing, humanoid robotics, and neuroprosthetics
  • Figure 32. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Healthcare and Medical Devices (2024-2036), millions USD
  • Figure 33. Technology roadmap for neuromorphic computing and sensing in aerospace and defense
  • Figure 34. Schematic route from bio-inspired behaviours toward neuromorphic sensors for autonomous flight
  • Figure 35. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Aerospace and Defence (2024-2036), millions USD
  • Figure 36. Technology roadmap for neuromorphic computing and sensing in Datacenters and Cloud Services
  • Figure 37. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Datacenters and Cloud Services (2024-2036), millions USD
  • Figure 42. Neuromorphic Computing and Sensing Ecosystem Overview
  • Figure 43. Cerebas WSE-2
  • Figure 44. DeepX NPU DX-GEN1
  • Figure 45. Google TPU
  • Figure 46. GrAI VIP
  • Figure 47. Groq Tensor Streaming Processor (TSP)
  • Figure 48. DVL-5000 neuromorphic laser profiler
  • Figure 49. Spiking Neural Processor
  • Figure 50. TROOPER robot
  • Figure 51. 11th Gen Intel-R Core(TM) S-Series
  • Figure 52. Intel Loihi 2 chip
  • Figure 53. Envise
  • Figure 54. Pentonic 2000
  • Figure 55. Azure Maia 100 and Cobalt 100 chips
  • Figure 56. Mythic MP10304 Quad-AMP PCIe Card
  • Figure 57. Nvidia H200 AI chip
  • Figure 58. Grace Hopper Superchip
  • Figure 59. Prophesee Metavision starter kit - AMD Kria KV260 and active marker LED board
  • Figure 60. Cloud AI 100
  • Figure 61. Overview of SpiNNaker2 architecture for the "SpiNNcloud" cloud system and edge systems
  • Figure 62. Untether AI chip
  • Figure 63. Quantum computing development timeline
  • Figure 64. National quantum initiatives and funding 2015-2023
  • Figure 65. Quantum Computing Market Map
  • Figure 66. Roadmap for Quantum Commercial Readiness Level (QCRL) Over Time
  • Figure 67. SWOT analysis for quantum computing
  • Figure 68. Global market for quantum computing-Hardware, Software & Services, 2023-2046 (billions USD)
  • Figure 69. Global Revenue from Quantum Computing Hardware (Billions USD)
  • Figure 70. Quantum Computer Installed Base Forecast (2025-2046)-Units
  • Figure 71. Forecast for Installed Base of Quantum Computers by Technology, 2025-2046-Units
  • Figure 72. Forecast for Annual Revenue from Quantum Computer Hardware Sales, 2025-2046 (billions USD)
  • Figure 73. Forecast for Annual Revenue from Quantum Computing Hardware Sales (by Technology), 2025-2046
  • Figure 74. An early design of an IBM 7-qubit chip based on superconducting technology
  • Figure 75. Various 2D to 3D chips integration techniques into chiplets
  • Figure 76. IBM Q System One quantum computer
  • Figure 77. Unconventional computing approaches
  • Figure 78. 53-qubit Sycamore processor
  • Figure 79. Interior of IBM quantum computing system. The quantum chip is located in the small dark square at center bottom
  • Figure 80. Superconducting quantum computer
  • Figure 81. Superconducting quantum computer schematic
  • Figure 82. Components and materials used in a superconducting qubit
  • Figure 83. Superconducting Hardware Roadmap
  • Figure 84. Superconducting Quantum Hardware Roadmap
  • Figure 85. SWOT analysis for superconducting quantum computers:
  • Figure 86. Ion-trap quantum computer
  • Figure 87. Various ways to trap ions
  • Figure 88. Trapped-Ion Hardware Roadmap
  • Figure 89. Universal Quantum's shuttling ion architecture in their Penning traps
  • Figure 90. Trapped-Ion Quantum Computing Hardware Roadmap
  • Figure 91. SWOT analysis for trapped-ion quantum computing
  • Figure 92. CMOS silicon spin qubit
  • Figure 93. Silicon quantum dot qubits
  • Figure 94. Silicon-Spin Hardware Roadmap
  • Figure 95. SWOT analysis for silicon spin quantum computers
  • Figure 96. Topological Quantum Computing Roadmap
  • Figure 97. Topological Quantum Computing Hardware Roadmap
  • Figure 98. SWOT analysis for topological qubits
  • Figure 99. Photonic Quantum Hardware Roadmap
  • Figure 100 . SWOT analysis for photonic quantum computers
  • Figure 101. Neutral atoms (green dots) arranged in various configurations
  • Figure 102. Neutral Atom Hardware Roadmap
  • Figure 103. SWOT analysis for neutral-atom quantum computers
  • Figure 104. NV center components
  • Figure 105. Diamond Defect Supply Chain
  • Figure 106. Diamond Defect Hardware Roadmap
  • Figure 107. SWOT analysis for diamond-defect quantum computers
  • Figure 108. D-Wave quantum annealer
  • Figure 109. Roadmap for Quantum Annealing Hardware
  • Figure 110. SWOT analysis for quantum annealers
  • Figure 111. Quantum software development platforms
  • Figure 112. Tech Giants quantum technologies activities
  • Figure 115. Archer-EPFL spin-resonance circuit
  • Figure 116. IBM Q System One quantum computer
  • Figure 117. ColdQuanta Quantum Core (left), Physics Station (middle) and the atoms control chip (right)
  • Figure 118. Intel Tunnel Falls 12-qubit chip
  • Figure 119. IonQ's ion trap
  • Figure 120. IonQ product portfolio
  • Figure 121. 20-qubit quantum computer
  • Figure 122. Maybell Big Fridge
  • Figure 123. PsiQuantum's modularized quantum computing system networks
  • Figure 124. 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 125. SemiQ first chip prototype
  • Figure 126. Toshiba QKD Development Timeline
  • Figure 127. Toshiba Quantum Key Distribution technology
  • Figure 128. AMD Radeon Instinct
  • Figure 129. AMD Ryzen 7040
  • Figure 130. Alveo V70
  • Figure 131. Versal Adaptive SOC
  • Figure 132. AMD's MI300 chip
  • Figure 133. Cerebas WSE-2
  • Figure 134. DeepX NPU DX-GEN1
  • Figure 135. Google TPU
  • Figure 136. Colossus(TM) MK2 GC200 IPU
  • Figure 137. GreenWave's GAP8 and GAP9 processors
  • Figure 138. 11th Gen Intel-R Core(TM) S-Series
  • Figure 139. Pentonic 2000
  • Figure 140. Azure Maia 100 and Cobalt 100 chips
  • Figure 141. Mythic MP10304 Quad-AMP PCIe Card
  • Figure 142. Nvidia H200 AI chip
  • Figure 143. Grace Hopper Superchip
  • Figure 144. Cloud AI 100
  • Figure 145. MLSoC(TM)
  • Figure 146. Grayskull
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