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1996644

全球光計算市場（2026-2036 年）

The Global Optical Computing Market 2026-2036

出版日期: 2026年03月24日 | 出版商:

Future Markets, Inc. | 英文 441 Pages, 168 Tables, 24 Figures | 訂單完成後即時交付

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簡介目錄圖表

全球光計算市場正處於運算基礎設施發展史上最關鍵的轉捩點之一。經過數十年的逐步演進，三個獨立又互補的結構性因素 - 人工智慧帶來的頻寬和能源需求指數級成長、光量子計算的穩步商業化以及半導體晶圓代工廠規模的矽光電製造技術的成熟 - 共同促成了資訊在計算堆棧各個層面上處理、傳輸和儲存方式的根本性變革。

從最廣義的商業性定義來看，光計算指的是使用光子而非電子作為互連、處理、感測和量子計算中的主要資訊載體。該市場涵蓋五個技術層面：光積體電路（PIC），它是採用 CMOS 相容製程在 300毫米晶圓上製造的基礎硬體基板；光處理器，它在光域中執行直接計算，包括用於人工智慧推理的矩陣乘法；光量子計算（包括光量子電腦、量子金鑰傳輸、量子隨機數生成和量子感測）；光連接模組和共封裝光學裝置，它們用光鏈路取代晶片內部和基板之間、光電導引之間的基於光的電感器和電感器系統之間的基於光電傳導

成長並非由單一技術變革驅動，而是由光電應用不斷擴展的連鎖反應所驅動。第一波浪潮，也是目前發生的浪潮，是由人工智慧驅動的資料中心頻寬危機。耗電量高達數百兆瓦的人工智慧訓練叢集需要超過 800G 的光連接模組。這是由於銅線的物理限制，在現代 GPU叢集所需的距離和密度下，銅線無法提供必要的頻寬。將光收發器直接整合到交換器 ASIC 封裝中的共封裝光元件，將在2026年至2028年間從試點部署過渡到量產，這將推動資料中心每美元光子成本的結構性成長，並預計到2030年市場將以 50%的年複合成長率成長。

第二波是光學人工智慧處理。光學矩陣乘法引擎能夠在皮秒級的物理層面上執行運算密集的人工智慧任務（神經網路推理的核心 - 線性代數運算），與同等GPU運算相比，能耗大幅降低。 Lightmatter、Luminous Computing和Optalysys等公司正從研發和展示階段轉向商業部署，目標是在2027年至2031年間，在人工智慧推理計算市場展開一場「贏家通吃」的競爭。隨著模型規模擴展到數兆個參數，光子架構在能源效率方面具有結構性優勢。

第三波浪潮，也是最有可能出現的浪潮，是光學量子運算。到2025年，光子硬體在私人融資額方面將超過超導性系統，成為量子運算硬體領域最大的子類別。 PsiQuantum 完成的10億美元E輪資金籌措、Xanadu在納斯達克上市以及Photonic Inc.獲得Microsoft支持的成長資金籌措，都顯示投資人對與CMOS晶圓代工廠相容的光子架構充滿信心，認為它是實現百萬量子位元級容錯量子運算最可靠的途徑。

本報告對全球光計算市場進行調查分析，包括定量市場規模估算、詳細的技術評估、98 家公司的概況以及依地區和細分市場分類的10年預測。

The global optical computing market stands at one of the most consequential inflection points in the history of computing infrastructure. After decades of incremental evolution, the convergence of three independent but mutually reinforcing structural forces - the exponential bandwidth and energy demands of artificial intelligence, the progressive commercialisation of photonic quantum computing, and the maturation of silicon photonics manufacturing at semiconductor foundry scale - is driving a fundamental transition in how information is processed, transmitted, and stored at every level of the computing stack.

Optical computing, in its broadest commercial definition, encompasses the use of photons rather than electrons as the primary carrier of information for interconnection, processing, sensing, and quantum computation. The market spans five primary technology layers: photonic integrated circuits (PICs), which are the foundational hardware substrate manufactured at 300 mm wafer scale using CMOS-compatible processes; optical processors, which perform computation directly in the optical domain including matrix multiplication for AI inference; quantum optical computing, encompassing photonic quantum computers, quantum key distribution, quantum random number generation, and quantum sensing; optical interconnects and co-packaged optics, which replace copper electrical signalling with photonic links within and between chips, boards, and servers; and optical sensing, which applies PIC technology to LiDAR, biomedical diagnostics, inertial navigation, and environmental monitoring.

Growth is not driven by a single technology transition but by a cascading sequence of photonic adoption waves. The first and already underway wave is the AI-driven data centre bandwidth crisis: AI training clusters consuming hundreds of megawatts require optical interconnects at 800G and beyond simply because copper physics cannot deliver the required bandwidth at the distances and densities of modern GPU clusters. Co-packaged optics - integrating optical transceivers directly onto switch ASIC packages - is transitioning from pilot deployments to volume production between 2026 and 2028, representing a structural expansion of photonic content per data centre dollar that will grow the market at 50% CAGR through 2030.

The second wave is photonic AI processing: optical matrix multiplication engines perform the most computationally intensive AI operations - the linear algebra at the core of neural network inference - at the physical level in picoseconds, with energy consumption orders of magnitude below equivalent GPU operations. Companies including Lightmatter, Luminous Computing, and Optalysys are transitioning from research demonstrations to commercial deployments in the 2027-2031 period, targeting a winner-takes-most competition for the AI inference compute market that photonic architectures are structurally positioned to win on energy efficiency grounds as model sizes grow to trillions of parameters.

The third and highest-optionality wave is photonic quantum computing. In 2025, photonic hardware overtook superconducting systems as the largest quantum computing hardware sub-category by private capital raised, with PsiQuantum's $1 billion Series E, Xanadu's NASDAQ listing, and Photonic Inc.'s Microsoft-backed growth round collectively signalling decisive investor conviction that CMOS foundry-compatible photonic architectures represent the most credible path to million-qubit fault-tolerant quantum computation.

The Global Optical Computing Market 2026-2036 is the most comprehensive and technically rigorous market intelligence report available on the global optical computing industry. Spanning 430 pages, 110 data tables, and 55 market forecast figures, the report provides quantitative market sizing, granular technology assessments, competitive intelligence across 98 company profiles, and ten-year forecasts for every major market segment, application area, material platform, and geographic region - all updated to reflect the decisive structural shifts of 2025 and 2026 including PsiQuantum's Nature-published Omega chipset breakthrough, the commercial ramp of 800G co-packaged optics at hyperscale data centres, and Xanadu's NASDAQ listing as the first publicly traded photonic quantum computing company.

The report begins with a detailed executive summary providing an immediate-use market snapshot, technology status assessment across nine major optical computing segments, and a four-table structured outlook covering short-term (2026-2028), medium-term (2029-2032), and long-term (2033-2036) projections. The introduction and key concepts section provides the foundational technical grounding required to interpret market dynamics correctly - covering the physics of optical computing, a detailed comparison of photonics versus electronics across every commercially relevant parameter, PIC architecture and component technology from waveguides and modulators through to heterogeneous integration and co-packaged optics, and a comprehensive treatment of quantum computing architectures including superconducting, trapped-ion, photonic, neutral atom, and topological qubit systems.

The materials and manufacturing section provides the most detailed publicly available assessment of PIC material platform economics and manufacturing trajectories, covering silicon-on-insulator, silicon nitride, indium phosphide, thin-film lithium niobate, barium titanate, gallium arsenide, and emerging platforms including diamond, aluminium nitride, and electro-optic polymers. Detailed fabrication method tables for each platform, heterogeneous integration technique assessments, wafer size scaling trajectories to 2036, and a benchmarked foundry capability matrix covering thirteen commercial PIC foundries provide the supply-side intelligence required for technology strategy and procurement decisions.

The optical computing technologies section covers four technology domains with depth and rigour not available in any competing publication. PIC architecture evolution is mapped across six generations from 2026 to 2036. Optical processor technologies - digital, analog AI/ML, neuromorphic, and Fourier optical - are assessed with competitive landscape tables covering every commercial-stage company. Quantum optical computing receives exhaustive treatment covering fusion-based quantum computing, GKP continuous-variable approaches, measurement-based computation, and the full quantum PIC component roadmap from single-photon sources through fast electro-optic switches to waveguide-integrated SNSPDs. Co-packaged optics and advanced packaging receives a dedicated section covering CPO architecture variants, the full CPO technology roadmap to 2036, and competitive analysis of all major CPO ecosystem participants.

The markets and applications section provides structured, quantitative treatment of seven end-market segments - data centres and HPC, telecommunications, quantum computing and communications, automotive and LiDAR, aerospace and defence, healthcare and biomedical, and industrial sensing and IoT - with individual market sizing tables for each segment forecasting to 2036. The market analysis and forecasts section provides the report's core quantitative deliverable: thirty forecast tables covering the global market by technology type, application, geography, PIC material platform, transceiver data rate, and quantum technology sub-segment, all presented consistently from 2026 to 2036 with CAGR calculations and sub-segment growth drivers.

Technology trends and future outlook, challenges and opportunities, and detailed sections on energy efficiency standards and material sustainability round out the strategic intelligence content. The report concludes with 93 company profiles organised across the full value chain, a 145-entry reference list with active hyperlinks, a comprehensive glossary of 80+ terms, and a complete list of abbreviations.

Report contents:

Chapter 1 - Executive Summary: Market snapshot table; global market size and growth projections 2026-2036; technology status summary; market map across five technology layers; short-, medium-, and long-term outlook projections; PIC maturity assessment by material platform
Chapter 2 - Introduction and Key Concepts: Optical computing history and basic principles; photonics versus electronics comparison (speed, bandwidth, energy, integration); EIC versus PIC comparison; optical computing advantages and challenges; PIC key concepts covering coupling, lasers, photodetectors, modulators, waveguides, and architecture (monolithic, hybrid, heterogeneous); quantum computing concepts covering all five major qubit architectures with technology descriptions, materials, and market players
Chapter 3 - Materials and Manufacturing: Silicon-on-insulator; silicon nitride; indium phosphide; organic polymer on silicon; thin-film lithium niobate (electro-optic properties, fabrication methods, emerging applications); barium titanate and rare earth metals; emerging PIC materials; metasurfaces; neuromorphic photonics; materials benchmarking scorecard; wafer sizes and scaling; monolithic, hybrid, and heterogeneous integration schemes; wafer bonding, flip-chip bonding, and micro-transfer printing; PIC design cycle and multi-project wafers; fabrication services; testing and packaging; key manufacturers and foundries
Chapter 4 - Optical Computing Technologies: PIC architectures and evolution roadmap; integration schemes; operational frequency windows; digital and analog optical processors; neuromorphic photonics; quantum optical computing systems, components, and roadmap; photon detection technologies; quantum PIC current state; optical interconnects (chip-to-chip, data centre); data centre interconnect standards and specifications; advanced packaging (2D, 2.5D, 3D); co-packaged optics architecture, roadmap, benefits, and challenges
Chapter 5 - Markets and Applications: Data centres and HPC (transceivers, AI accelerator interconnects, photonic TPUs); telecommunications (5G/6G, WDM networking, mmWave photonics); quantum computing and communications (QKD, quantum sensing, QRNG, quantum networking); automotive and LiDAR (coherent FMCW, flash, autonomous vehicles, HD mapping); aerospace and defence (optical gyroscopes, free-space optical communications); healthcare and biomedical (OCT, lab-on-chip, photoacoustic imaging, FLIM); industrial sensing and IoT (gas sensing, distributed fibre sensing, structural health monitoring, DAS)
Chapter 6 - Market Analysis and Forecasts: Global market overview and historical trends; market size and growth projections 2026-2036; key growth drivers and inhibitors; segmentation by technology type, application, and geography; PIC market by material platform (SOI, SiN, InP, TFLN, GaAs, others); PIC transceiver market by data rate and application; PIC for AI/data centres, telecommunications, quantum computing, quantum communications, automotive LiDAR, and industrial sensing; optical processor market by type and application; quantum optical computing market by technology type and application area
Chapter 7 - Technology Trends and Future Outlook: All-optical computing; neuromorphic photonics; quantum photonics TRL assessment 2026-2036; photonic-electronic integration roadmap; 3D integration for optical computing; advanced manufacturing techniques; automated testing and packaging; scalable quantum photonic architectures; quantum error correction advances; AI-assisted PIC design; PIC, optical processor, and quantum optical computing technology roadmaps to 2036
Chapter 8 - Challenges and Opportunities: Technical challenges and potential solutions (25 challenges with detailed mitigation pathways); market challenges covering cost competitiveness, adoption barriers, and standardisation; opportunities in data centre AI acceleration, 5G/6G, quantum technologies, and green computing; energy efficiency standards; material usage and recycling policies
Chapter 9 - Company Profiles: 93 profiles across silicon photonics and PIC platforms; optical interconnects and CPO; photonic AI processors; photonic quantum computing systems; quantum communications and sensing; PIC foundries and component suppliers; supporting ecosystem
Chapter 10 - Appendices: Glossary of 80+ terms; list of abbreviations; research methodology including primary research, secondary research, market sizing, segmentation framework, TRL reference, limitations and caveats
Chapter 11 - References: 145 references covering academic literature, industry reports, company sources, government and institutional sources, conference proceedings, news and trade media, patent literature, and standards documents - all with active hyperlinks

Companies profiled include AIM Photonics, Akhetonics, Alpine Quantum Technologies, Arago, Astrape Networks, Atom Computing, Black Semiconductor, Celestial AI, Cognifiber, Cornerstone, Crystal Quantum Computing, Dawn Semiconductor, Duality Quantum Photonics, DustPhotonics, EFFECT Photonics, eleQtron, Ephos, Exail Quantum Sensors, Finchetto, GlobalFoundries, Heguang Microelectronics Technology, Hongguang Xiangshang, HyperLight, IBM, ID Quantique, Infineon Technologies, Infleqtion, IonQ, Ipronics, Ligentec, Lightelligence, Lightium AG, LightMatter, LightON, Lightsolver, Liobate Technologies, LioniX, Lumai, Luxtelligence SA, Microsoft, Miraex, M Squared Lasers, Myrias Optics, Nanofiber Quantum Technologies, NcodiN, nEye Systems, Neurophos, New Origin and more.....

1 EXECUTIVE SUMMARY

1.1 Market snapshot
1.2 Market map
1.3 Technology Status
- 1.3.1 Current Market State of Optical Computing
- 1.3.2 Photonic Integrated Circuits (PICs) Maturity
1.4 Future Outlook
- 1.4.1 Short-term Projections (2025-2027)
- 1.4.2 Medium-term Outlook (2028-2031)
- 1.4.3 Long-term Vision (2032-2035)

2 INTRODUCTION AND KEY CONCEPTS

2.1 Technology Background
- 2.1.1 What is Optical Computing?
  - 2.1.1.1 Historical Context
  - 2.1.1.2 Basic Principles of Optical Computing
- 2.1.2 Photonics versus Electronics
  - 2.1.2.1 Speed and Bandwidth Comparison
  - 2.1.2.2 Energy Efficiency Considerations
  - 2.1.2.3 Integration Challenges
- 2.1.3 Electronic and Photonic Integrated Circuits Compared
  - 2.1.3.1 Architectural Differences
  - 2.1.3.2 Performance Characteristics
  - 2.1.3.3 Manufacturing Considerations
- 2.1.4 Advantages and Challenges of Optical Computing
  - 2.1.4.1 Speed and Bandwidth Advantages
  - 2.1.4.2 Energy Efficiency Benefits
  - 2.1.4.3 Integration and Miniaturization Challenges
  - 2.1.4.4 Cost Considerations
2.2 Photonic Integrated Circuit (PIC) Key Concepts
- 2.2.1 Optical IO, Coupling and Couplers
  - 2.2.1.1 Fiber-to-Chip Coupling
  - 2.2.1.2 On-Chip Optical Couplers
- 2.2.2 Emission and Photon Sources/Lasers
  - 2.2.2.1 Semiconductor Lasers
  - 2.2.2.2 Integration of Light Sources on PICs
- 2.2.3 Detection and Photodetectors
  - 2.2.3.1 Types of Photodetectors
  - 2.2.3.2 Integration Challenges for Detectors
- 2.2.4 Modulation and Modulators
  - 2.2.4.1 Electro-optic Modulators
  - 2.2.4.2 Thermo-optic Modulators
  - 2.2.4.3 All-optical Modulators
- 2.2.5 Light Propagation and Waveguides
  - 2.2.5.1 Waveguide Structures
  - 2.2.5.2 Loss Mechanisms in Optical Waveguides
- 2.2.6 PIC Architecture
  - 2.2.6.1 Monolithic Integration
  - 2.2.6.2 Hybrid Integration
  - 2.2.6.3 Heterogeneous Integration
2.3 Quantum Computing Concepts
- 2.3.1 Introduction to Quantum Computing
  - 2.3.1.1 Quantum Bits (Qubits)
  - 2.3.1.2 Quantum Gates and Circuits
- 2.3.2 Quantum Computing Architectures Overview
  - 2.3.2.1 Superconducting Qubits
    - 2.3.2.1.1 Technology description
    - 2.3.2.1.2 Materials
    - 2.3.2.1.3 Market players
  - 2.3.2.2 Trapped Ions
    - 2.3.2.2.1 Technology description
    - 2.3.2.2.2 Materials
      - 2.3.2.2.2.1 Integrating optical components
      - 2.3.2.2.2.2 Incorporating high-quality mirrors and optical cavities
      - 2.3.2.2.2.3 Engineering the vacuum packaging and encapsulation
      - 2.3.2.2.2.4 Removal of waste heat
    - 2.3.2.2.3 Market players
  - 2.3.2.3 Photonic Qubits
    - 2.3.2.3.1 Technology description
    - 2.3.2.3.2 Market players
  - 2.3.2.4 Neutral Atoms
    - 2.3.2.4.1.1 Technology description
    - 2.3.2.4.1.2 Market players
  - 2.3.2.5 Topological Qubits
    - 2.3.2.5.1 Technology description
    - 2.3.2.5.2 Market players

3 MATERIALS AND MANUFACTURING

3.1 Optical Computing Materials
- 3.1.1 Silicon and Silicon-on-Insulator (SOI)
  - 3.1.1.1 Properties and Advantages
  - 3.1.1.2 Limitations and Challenges
  - 3.1.1.3 Key Players and Developments
- 3.1.2 Silicon Nitride (SiN)
  - 3.1.2.1 Optical Properties
  - 3.1.2.2 Manufacturing Processes
  - 3.1.2.3 Applications and Market Adoption
- 3.1.3 Indium Phosphide
  - 3.1.3.1 Material Characteristics
  - 3.1.3.2 Integration Challenges
  - 3.1.3.3 Market Players and Products
- 3.1.4 Organic Polymer on Silicon
  - 3.1.4.1 Advantages of Polymer-based PICs
  - 3.1.4.2 Manufacturing Techniques
- 3.1.5 Thin Film Lithium Niobate
  - 3.1.5.1 Electro-optic Properties
  - 3.1.5.2 Fabrication Methods
  - 3.1.5.3 Emerging Applications
- 3.1.6 Barium Titanate and Rare Earth Metals
  - 3.1.6.1 Novel Properties for Optical Computing
  - 3.1.6.2 Integration Challenges
  - 3.1.6.3 Future Prospects
- 3.1.7 Emerging PIC materials
- 3.1.8 Metasurfaces
- 3.1.9 Neuromorphic photonics
- 3.1.10 Materials Comparison and Benchmarking
  - 3.1.10.1 Cost Analysis
- 3.1.11 Wafer Sizes and Processing
  - 3.1.11.1 Current Wafer Size Trends
  - 3.1.11.2 Scaling Challenges
- 3.1.12 Integration Schemes
  - 3.1.12.1 Monolithic Integration
  - 3.1.12.2 Hybrid Integration
  - 3.1.12.3 Heterogeneous Integration
- 3.1.13 Heterogeneous Integration Techniques
  - 3.1.13.1 Wafer Bonding
  - 3.1.13.2 Flip-Chip Bonding
  - 3.1.13.3 Micro-Transfer Printing
- 3.1.14 The PIC Design Cycle: Multi-Project Wafers
  - 3.1.14.1 Design Tools and Software
  - 3.1.14.2 Fabrication Services
  - 3.1.14.3 Testing and Packaging
3.2 Key Manufacturers and Foundries
- 3.2.1 Pure-Play PIC Foundries
- 3.2.2 Integrated Device Manufacturers (IDMs)

4 OPTICAL COMPUTING TECHNOLOGIES

4.1 Photonic Integrated Circuits (PICs)
- 4.1.1 PIC Architectures
  - 4.1.1.1 Planar Lightwave Circuits
  - 4.1.1.2 3D Integrated Photonics
- 4.1.2 Integration Schemes of PICs
- 4.1.3 Operational Frequency Windows of Optical Materials
  - 4.1.3.1 Visible Light PICs
  - 4.1.3.2 Near-Infrared PICs
  - 4.1.3.3 Mid-Infrared PICs
4.2 Optical Processors
- 4.2.1 Digital Optical Computing
  - 4.2.1.1 All-Optical Logic Gates
  - 4.2.1.2 Optical Flip-Flops and Memory
- 4.2.2 Analog Optical Computing
  - 4.2.2.1 Optical Matrix Multiplication
  - 4.2.2.2 Fourier Optics and Signal Processing
- 4.2.3 Neuromorphic Photonics
  - 4.2.3.1 Optical Neural Networks
  - 4.2.3.2 Reservoir Computing
4.3 Quantum Optical Computing
- 4.3.1 Photonic Platform for Quantum Computing
  - 4.3.1.1 Single-Photon Sources
  - 4.3.1.2 Quantum Gates and Circuits
  - 4.3.1.3 Photon Detection Technologies
- 4.3.2 Comparison with Other Quantum Computing Architectures
  - 4.3.2.1 Advantages of Photonic Qubits
  - 4.3.2.2 Scaling Challenges
  - 4.3.2.3 Error Correction in Photonic Quantum Computing
- 4.3.3 Quantum PIC Requirements and Roadmap
  - 4.3.3.1 Current State of Quantum PICs
4.4 Optical Interconnects
- 4.4.1 On-Device Interconnects
  - 4.4.1.1 Chip-to-Chip Optical Interconnects
  - 4.4.1.2 On-Chip Optical Interconnects
- 4.4.2 Data Center Interconnects
  - 4.4.2.1 Rack-to-Rack Interconnects
  - 4.4.2.2 Inter-Data Center Interconnects
4.5 Advanced Packaging and Co-Packaged Optics
- 4.5.1 Evolution of Semiconductor Packaging
  - 4.5.1.1 2D to 2.5D Packaging
    - 4.5.1.1.1 Silicon Interposer 2.5D
      - 4.5.1.1.1.1 Through Si Via (TSV)
      - 4.5.1.1.1.2 (SiO2) based redistribution layers (RDLs)
    - 4.5.1.1.2 2.5D Organic-based packaging
      - 4.5.1.1.2.1 Chip-first and chip-last fan-out packaging
      - 4.5.1.1.2.2 Organic substrates
      - 4.5.1.1.2.3 Organic RDL
    - 4.5.1.1.3 2.5D glass-based packaging
      - 4.5.1.1.3.1 Benefits
      - 4.5.1.1.3.2 Glass Si interposers in advanced packaging
      - 4.5.1.1.3.3 Glass material properties
      - 4.5.1.1.3.4 2/2 micrometer m line/space metal pitch on glass substrates
      - 4.5.1.1.3.5 3D Glass Panel Embedding (GPE) packaging
      - 4.5.1.1.3.6 Thermal management
      - 4.5.1.1.3.7 Polymer dielectric films
      - 4.5.1.1.3.8 Challenges
      - 4.5.1.1.3.9 Comparison with other substrates
    - 4.5.1.1.4 2.5D vs. 3D Packaging
    - 4.5.1.1.5 Benefits
    - 4.5.1.1.6 Challenges
    - 4.5.1.1.7 Trends
    - 4.5.1.1.8 Market players
  - 4.5.1.2 3D Packaging Technologies
    - 4.5.1.2.1 Overview
      - 4.5.1.2.1.1 Conventional 3D packaging
      - 4.5.1.2.1.2 Advanced 3D Packaging with through-silicon vias (TSVs)
      - 4.5.1.2.1.3 Three-dimensional (3D) hybrid bonding
      - 4.5.1.2.1.4 Devices using hybrid bonding
    - 4.5.1.2.2 3D Microbump technology
      - 4.5.1.2.2.1 Technologies
      - 4.5.1.2.2.2 Challenges
      - 4.5.1.2.2.3 Bumpless copper-to-copper (Cu-Cu) hybrid bonding
      - 4.5.1.2.2.4 Trends
- 4.5.2 Co-Packaged Optics (CPO) Technology
  - 4.5.2.1 CPO Architectures
  - 4.5.2.2 Benefits and Challenges of CPO
- 4.5.3 CPO Market Players and Developments

5 MARKETS AND APPLICATIONS

5.1 Data Centers and High-Performance Computing
- 5.1.1 Optical Transceivers for Data Centers
  - 5.1.1.1 Current and Future Data Rates
  - 5.1.1.2 Form Factors and Standards
- 5.1.2 PIC-based Transceivers for AI and Machine Learning
  - 5.1.2.1 AI Accelerator Interconnects
  - 5.1.2.2 High-Bandwidth Memory Interfaces
- 5.1.3 Photonic Engines and Accelerators for AI
  - 5.1.3.1 Optical Matrix Multiplication Engines
  - 5.1.3.2 Photonic Tensor Processing Units
5.2 Telecommunications
- 5.2.1 5G and Beyond
  - 5.2.1.1 Fronthaul and Backhaul Networks
  - 5.2.1.2 Millimeter-Wave Photonics
- 5.2.2 Optical Networking Equipment
  - 5.2.2.1 Optical Switches and Routers
  - 5.2.2.2 Wavelength Division Multiplexing (WDM) Systems
5.3 Quantum Computing and Communications
- 5.3.1 Quantum Key Distribution
  - 5.3.1.1 Discrete Variable vs. Continuous Variable QKD Protocols
- 5.3.2 Quantum Sensing
  - 5.3.2.1 Quantum Magnetometers
  - 5.3.2.2 Quantum Gravimeters
    - 5.3.2.2.1 Applications
    - 5.3.2.2.2 Key players
5.4 Automotive and LiDAR
- 5.4.1 PIC-based LiDAR Systems
  - 5.4.1.1 Coherent LiDAR
  - 5.4.1.2 Flash LiDAR
- 5.4.2 Autonomous Vehicle Applications
  - 5.4.2.1 Object Detection and Tracking
  - 5.4.2.2 HD Mapping and Localization
5.5 Aerospace and Defense
- 5.5.1 Optical Gyroscopes
- 5.5.2 Free-Space Optical Communications
5.6 Healthcare and Biomedical
- 5.6.1 PIC-based Biosensors
  - 5.6.1.1 Lab-on-a-Chip Devices
  - 5.6.1.2 Point-of-Care Diagnostics
- 5.6.2 Medical Imaging
  - 5.6.2.1 Optical Coherence Tomography (OCT)
  - 5.6.2.2 Photoacoustic Imaging
5.7 Industrial Sensing and IoT
- 5.7.1 Gas and Chemical Sensors
  - 5.7.1.1 Environmental Monitoring
  - 5.7.1.2 Process Control in Manufacturing
  - 5.7.1.3 Structural Health Monitoring
  - 5.7.1.4 Fiber Optic Sensors for Infrastructure
  - 5.7.1.5 Distributed Acoustic Sensing

6 MARKET ANALYSIS AND FORECASTS

6.1 Global Optical Computing Market Overview
- 6.1.1 Historical Market Trends
- 6.1.2 Market Size and Growth Projections (2025-2035)
- 6.1.3 Key Growth Drivers and Inhibitors
6.2 Market Segmentation
- 6.2.1 By Technology Type
  - 6.2.1.1 Photonic Integrated Circuits
  - 6.2.1.2 Optical Processors
  - 6.2.1.3 Quantum Optical Computing
- 6.2.2 By Application
  - 6.2.2.1 Data Centers and HPC
  - 6.2.2.2 Telecommunications
  - 6.2.2.3 Automotive and LiDAR
  - 6.2.2.4 Healthcare and Biomedical
- 6.2.3 By Geography
  - 6.2.3.1 North America
  - 6.2.3.2 Europe
  - 6.2.3.3 Asia-Pacific
  - 6.2.3.4 Rest of the World
6.3 PIC Market Forecasts
- 6.3.1 PIC Market by Material Platform
  - 6.3.1.1 Silicon Photonics
  - 6.3.1.2 Indium Phosphide
  - 6.3.1.3 Silicon Nitride
  - 6.3.1.4 Others
- 6.3.2 PIC-based Transceiver Market
  - 6.3.2.1 By Data Rate
  - 6.3.2.2 By Application
- 6.3.3 PIC for AI and Data Centers
  - 6.3.3.1 AI Accelerator Interconnects
  - 6.3.3.2 High-Performance Computing
- 6.3.4 PIC for Telecommunications
  - 6.3.4.1 5G and Beyond
  - 6.3.4.2 Optical Networking Equipment
- 6.3.5 Quantum PIC Market
  - 6.3.5.1 Quantum Computing
  - 6.3.5.2 Quantum Communications
- 6.3.6 PIC-based Sensor and LiDAR Markets
  - 6.3.6.1 Automotive LiDAR
  - 6.3.6.2 Industrial Sensing
6.4 Optical Processor Market Forecasts
- 6.4.1 By Type (Digital, Analog, Neuromorphic)
- 6.4.2 By Application
6.5 Quantum Optical Computing Market Forecasts
- 6.5.1 By Type of Quantum Technology
- 6.5.2 By Application Area

7 TECHNOLOGY TRENDS AND FUTURE OUTLOOK

7.1 Emerging Technologies in Optical Computing
- 7.1.1 All-Optical Computing
- 7.1.2 Neuromorphic Photonics
- 7.1.3 Quantum Photonics
7.2 Integration Trends
- 7.2.1 Photonic-Electronic Integration
- 7.2.2 3D Integration for Optical Computing
7.3 Scalability and Manufacturability Improvements
- 7.3.1 Advanced Manufacturing Techniques
- 7.3.2 Automated Testing and Packaging
7.4 Advances in Quantum Optical Computing
- 7.4.1 Scalable Quantum Photonic Architectures
- 7.4.2 Quantum Error Correction in Optical Systems
7.5 The Role of AI in Optical Computing Design
- 7.5.1 AI-assisted PIC Design
- 7.5.2 Optimization of Optical Neural Networks
7.6 Roadmaps for Various Optical Computing Technologies
- 7.6.1 PIC Technology Roadmap
- 7.6.2 Optical Processor Roadmap
- 7.6.3 Quantum Optical Computing Roadmap

8 CHALLENGES AND OPPORTUNITIES

8.1 Technical Challenges
- 8.1.1 Efficiency and Power Consumption
- 8.1.2 Integration and Packaging
- 8.1.3 Scalability and Yield
8.2 Market Challenges
- 8.2.1 Cost Competitiveness
- 8.2.2 Adoption Barriers
- 8.2.3 Standardization Issues
8.3 Opportunities
- 8.3.1 Data Center and AI/ML Acceleration
- 8.3.2 5G and 6G Communications
- 8.3.3 Quantum Technologies
- 8.3.4 Green Computing Initiatives
8.4 Environmental Regulations and Sustainability
- 8.4.1 Energy Efficiency Standards
- 8.4.2 Material Usage and Recycling Policies

9 COMPANY PROFILES (93 company profiles)

10 APPENDICES

10.1 Glossary of Terms
10.2 List of Abbreviations
10.3 Research Methodology

11 REFERENCES

簡介目錄圖表

List of Tables

Table 1. Market snapshot for Optical Computing.
Table 2. Global Optical Computing Market Size and Growth Projections, 2026-2036 (Billions USD).
Table 3. Technology Status Summary for Key Optical Computing Segments, 2026.
Table 4. Optical Computing Market Outlook by Time Horizon, 2026-2036.
Table 5. Comparison of Key Parameters: Electronic vs. Photonic Computing.
Table 6. Energy Efficiency Considerations: Photonic vs. Electronic Interconnects.
Table 7. Energy Efficiency Considerations.
Table 8. Integration Challenges.
Table 9. Electronic and Photonic Integrated Circuits: Key Comparisons.
Table 10. Electronic and Photonic Integrated Circuits: Performance Comparison.
Table 11. Electronic and Photonic Integrated Circuits Manufacturing Considerations.
Table 12. Cost Considerations: Optical vs. Electronic Computing.
Table 13. Optical Coupling Mechanisms: Comparison.
Table 14. Comparison of Different Laser Types for PICs.
Table 15. Types of Photodetectors.
Table 16. Integration Challenges for Detectors.
Table 17. Comparison of Modulator Types for Photonic Integrated Circuits.
Table 18. Waveguide Structures and Their Characteristics.
Table 19. PIC Integration Schemes: Pros and Cons.
Table 20. Comparison of Quantum Computing Architectures.
Table 21. Superconducting qubit market players.
Table 22. Initialization, manipulation and readout for trapped ion quantum computers.
Table 23. Ion trap market players.
Table 24. Pros and cons of photon qubits.
Table 25. Comparison of photon polarization and squeezed states.
Table 26. Initialization, manipulation and readout of photonic platform quantum computers.
Table 27. Photonic qubit market players.
Table 28. Initialization, manipulation and readout for neutral-atom quantum computers.
Table 29. Pros and cons of cold atoms quantum computers and simulators
Table 30. Neural atom qubit market players.
Table 31. Initialization, manipulation and readout of topological qubits.
Table 32. Topological qubits market players.
Table 33. Properties of Key Materials Used in Optical Computing.
Table 34. Silicon-on-Insulator (SOI) Properties and Advantages.
Table 35. SOI Limitations and Challenges.
Table 36. Comparison of SOI and SiN Platforms.
Table 37. Silicon Nitride (SiN) Manufacturing Processes.
Table 38. Indium Phosphide Material Characteristics.
Table 39. Indium Phosphide Integration Challenges.
Table 40. Advantages of Polymer-based PICs.
Table 41. Organic Polymer on Silicon Manufacturing Techniques.
Table 42. Thin-Film Lithium Niobate: Key Properties and Fabrication.
Table 43. Thin Film Lithium Niobate Fabrication Methods.
Table 44. Thin Film Lithium Niobate Emerging Applications.
Table 45. Barium Titanate and Rare Earth Metal Integration.
Table 46. Barium Titanate and Rare Earth Metals Integration Challenges.
Table 47. Emerging PIC Materials: Properties and Prospects.
Table 48. Materials Cost Analysis.
Table 49. Wafer Sizes by PIC Platform.
Table 50. Current Wafer Size Trends by PIC Platform, 2026-2036.
Table 51. Wafer Scaling Challenges.
Table 52. Wafer PIC Integration Schemes: Technical Comparison.
Table 53. Heterogeneous Integration Techniques Comparison.
Table 54. PIC Design Ecosystem: Tools, Foundries, and Design Flow.
Table 55. Top PIC Foundries and Their Capabilities.
Table 56. Integrated Device Manufacturers (IDMs).
Table 57. Integration Schemes of PICs: Pros and Cons.
Table 58. Operational Frequency Windows: PIC Material and Application Matrix.
Table 59. All-Optical Logic Gate Implementations.
Table 60. Optical Matrix Multiplication Engine Comparison.
Table 61. Neuromorphic Photonic Architectures.
Table 62. Comparison of Quantum Computing Architectures.
Table 63. Quantum PIC Components and Their Functions.
Table 64. Photon Detection Technologies for Quantum Computing.
Table 65. Photon Detection Technologies.
Table 66. Advantages of Photonic Qubits.
Table 67. Error Correction Approaches in Photonic Quantum Computing.
Table 68. Quantum PIC Roadmap, 2026-2036.
Table 69. Data Centre Interconnect Standards and Specifications (2026).
Table 70. 2.5D Packaging Technologies: Comparison.
Table 71. Fan-out packaging process overview.
Table 72. Comparison between mainstream silicon dioxide (SiO2) and leading organic dielectrics for electronic interconnect substrates.
Table 73. Benefits of glass in 2.5D glass-based packaging.
Table 74. Comparison between key properties of glass and polymer molding compounds commonly used in semiconductor packaging applications.
Table 75. Challenges of glass semiconductor packaging.
Table 76. Comparison between silicon, organic laminates and glass as packaging substrates.
Table 77. 2.5D vs. 3D packaging.
Table 78. 2.5D packaging challenges.
Table 79. Market players in 2.5D packaging.
Table 80.Comparison: 2.5D, 3D Microbump, and 3D Hybrid Bonding.
Table 81. Advantages and disadvantages of 3D packaging.
Table 82. Comparison between 2.5D, 3D micro bump, and 3D hybrid bonding.
Table 83. Challenges in 3D Hybrid Bonding.
Table 84. Challenges in scaling bumps.
Table 85. Key methods for enabling copper-to-copper (Cu-Cu) hybrid bonding in advanced semiconductor packaging:
Table 86. Micro bumps vs Cu-Cu bumpless hybrid bonding.
Table 87. Benefits and Challenges of Co-Packaged Optics.
Table 88. Benefits and Challenges of CPO.
Table 89. Key Companies in CPO.
Table 90. Global Market for Optical Computing in Data Centers and HPC, 2026-2036 (Billions USD).
Table 91. Data Centre Transceiver Roadmap, 2026-2036.
Table 92. AI Accelerator Interconnect Bandwidth Trends.
Table 93. Market for PIC in AI Accelerator Interconnects, 2026-2036 (Millions USD).
Table 94. Photonic Tensor Processing Units: Competitive Landscape (2026).
Table 95. Global Market for Optical Computing in Telecommunications, 2026-2036 (Billions USD).
Table 96. Market for PIC in Optical Networking Equipment, 2026-2036 (Millions USD).
Table 97. Global Market for Quantum Optical Computing and Communications, 2026-2036 (Billions USD).
Table 98. Discrete Variable vs. Continuous Variable QKD Protocols.
Table 99. Comparison of Key Parameters: Magnetic Field Sensors.
Table 100. Market Opportunity for Quantum Magnetic Field Sensors, 2026-2036 (Millions USD).
Table 101. Applications of Quantum Gravimeters.
Table 102. Key Players in Quantum Gravimeters.
Table 103. Applications of quantum gravimeters
Table 104. Comparative table between quantum gravity sensing and some other technologies commonly used for underground mapping.
Table 105. Key players in quantum gravimeters.
Table 106. Global Market for Optical Computing in Automotive and LiDAR, 2026-2036 (Billions USD).
Table 107. Market for PIC-Based Automotive LiDAR, 2026-2036 (Millions USD).
Table 108. Global Market for Optical Computing in Aerospace and Defence, 2026-2036 (Billions USD).
Table 109. Free-Space Optical Communications: Key Parameters and Applications.
Table 110. Global Market for Optical Computing in Healthcare and Biomedical, 2026-2036 (Billions USD).
Table 111. Global Market for Optical Computing in Industrial Sensing and IoT, 2026-2036 (Billions USD).
Table 112. Global Optical Computing Market Size and Growth Projections, 2026-2036 (Billions USD).
Table 113. Key Growth Drivers and Inhibitors.
Table 114. Global Market for Photonic Integrated Circuits, 2026-2036 (Billions USD).
Table 115. Global Market for Optical Processors, 2026-2036 (Billions USD).
Table 116. Global Market for Quantum Optical Computing, 2026-2036 (Billions USD).
Table 117. Global Market for Optical Computing in Data Centers and HPC, 2026-2036 (Billions USD).
Table 118. Global market for Optical Computing in Telecommunications 2025-2035 (Billions USD).
Table 119. Global market for Optical Computing in Automotive and LiDAR 2025-2036 (Billions USD).
Table 120. Global market for Optical Computing in Healthcare and Biomedical 2025-2036 (Billions USD).
Table 121. Global market for Optical Computing in North America 2025-2036 (Billions USD).
Table 122. Global market for Optical Computing in Europe 2025-2036 (Billions USD).
Table 123. Global Market for Optical Computing in Asia-Pacific, 2026-2036 (Billions USD).
Table 124. Global market for Optical Computing in Rest of the World 2025-2036 (Billions USD).
Table 125. PIC Market by Material Platform - Silicon Photonics, 2026-2036 (Millions USD).
Table 126. PIC Market by Material Platform - Indium Phosphide, 2026-2036 (Millions USD).
Table 127. PIC Market by Material Platform - Silicon Nitride, 2026-2036 (Millions USD).
Table 128. PIC Market by Material Platform - Others, 2026-2036 (Millions USD).
Table 129. PIC-based Transceiver Market 2025-2036 (Millions USD), By Data Rate.
Table 130. PIC-Based Transceiver Market, 2026-2036 (Millions USD), By Application.
Table 131. Market for PIC in AI Accelerator Interconnects, 2026-2036 (Millions USD).
Table 132. Market for PIC in High-Performance Computing, 2026-2036 (Millions USD).
Table 133. Market for PIC in 5G/6G, 2025-2036 (Millions USD).
Table 134. Market for PIC in Optical Networking Equipment, 2025-2036 (Millions USD).
Table 135. Market for PIC in Quantum Computing, 2026-2036 (Millions USD).
Table 136. Market for PIC in Quantum Communications, 2026-2036 (Millions USD).
Table 137. Market for PIC in Automotive LiDAR, 2025-2036 (Millions USD).
Table 138. Market for PIC in Industrial Sensing, 2026-2036 (Millions USD).
Table 139. Optical Processor Market Forecasts By Type, 2025-2036 (Billions USD).
Table 140. Optical Processor Market Forecasts by Application, 2026-2036 (Billions USD).
Table 141. Quantum Optical Computing Market Forecasts by Type of Quantum Technology, 2026-2036.
Table 142. Quantum Optical Computing Market Forecasts, By Application Area 2025-2036.
Table 143. All-Optical Computing: Current Status and Development Roadmap.
Table 144. Neuromorphic Photonic Technology Trends and Forecast.
Table 145. Quantum Photonics Technology Readiness Levels, 2026-2036.
Table 146. Photonic-Electronic Integration Technology Roadmap, 2026-2036.
Table 147. Advanced Manufacturing Techniques in PIC Production, 2026-2036.
Table 148. Automated Testing and Packaging: State of Progress.
Table 149. Scalable Quantum Photonic Architecture Comparison.
Table 150. Quantum Error Correction Advances in Photonic Systems, 2026-2036.
Table 151. AI Applications in PIC Design and Manufacturing.
Table 152. PIC Technology Roadmap, 2026-2036.
Table 153. Optical Processor Technology Roadmap, 2026-2036.
Table 154. Quantum Optical Computing Roadmap, 2026-2036.
Table 155. Technical Challenges in Optical Computing and Potential Solutions.
Table 156. Technical Challenges in Optical Computing and Potential Solutions.
Table 157. Integration and Packaging Challenges.
Table 158. Scalability and Yield Challenges in Optical Computing.
Table 159. Cost Comparison: Optical vs. Electronic Computing Systems.
Table 160. Adoption Barriers by Application Area.
Table 161. Standardisation Status and Roadmap for Optical Computing.
Table 162. Optical Computing Opportunities in Data Centers and AI, 2026-2036.
Table 163. Quantum Technology Opportunities for Optical Computing, 2026-2036.
Table 164. Energy Efficiency Standards Relevant to Optical Computing.
Table 165. Energy Efficiency Standards.
Table 166. Material Usage and Recycling Policies.
Table 167. Glossary of Terms.
Table 168. List of Abbreviations.

List of Figures

Figure 1. Timeline of Major Milestones in Optical Computing.
Figure 2. Basic Architecture of a Photonic Integrated Circuit (PIC).
Figure 3. Superconducting quantum computer.
Figure 4. Superconducting quantum computer schematic.
Figure 5. Components and materials used in a superconducting qubit.
Figure 6. Ion-trap quantum computer.
Figure 7. Various ways to trap ions
Figure 8. Universal Quantum's shuttling ion architecture in their Penning traps.
Figure 9. Neutral atoms (green dots) arranged in various configurations
Figure 10. PIC Material Platform Benchmarking Scorecard (1 = Poor, 5 = Excellent).
Figure 11. PIC Architecture Evolution, 2025-2035.
Figure 12. 2D chip packaging.
Figure 13. Typical structure of 2.5D IC package utilizing interposer.
Figure 14. Fan-out chip-first process flow and Fan-out chip-last process flow.
Figure 15. Manufacturing process for glass interposers.
Figure 16. 3D Glass Panel Embedding (GPE) package.
Figure 17. Co-Packaged Optics (CPO) Technology Roadmap.
Figure 18. Data Center Transceiver Roadmap, 2025-2035.
Figure 19. Quantum Gravimeter.
Figure 20. Quantum Optical Computing: Technology Readiness Levels.
Figure 21. ColdQuanta Quantum Core (left), Physics Station (middle) and the atoms control chip (right).
Figure 22. IonQ's ion trap
Figure 23. PT-2 photonic quantum computer.
Figure 24. PsiQuantum's modularized quantum computing system networks.