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量子机器学习(QML)的全球市场(2026年～2040年)
市场调查报告书
商品编码
1734000

量子机器学习(QML)的全球市场(2026年～2040年)

The Global Quantum Machine Learning Market 2026-2040
出版日期: | 出版商: Future Markets, Inc. | 英文 143 Pages, 50 Tables, 21 Figures | 订单完成后即时交付

价格

量子机器学习 (QML) 利用量子力学的独特特性——迭加、纠缠和量子干涉——有望以比传统电脑更快的速度解决机器学习问题。量子机器学习代表了计算智慧的范式转变，使量子演算法能够同时处理大量资料集，并透过量子迭加并行执行多项计算。与存在于 0 或 1 确定状态的经典位元不同，量子位元（qubit）可以存在于迭加态，从而使量子电脑能够同时探索多种解决方案路径。这种量子优势在最佳化问题、模式识别和复杂资料分析任务中尤其明显，而这些任务是机器学习应用的核心。

该领域包含几种关键方法，包括量子增强机器学习（使用量子处理器加速传统演算法）和量子原生机器学习（利用量子力学特性的全新演算法）。量子神经网路、量子支援向量机和量子强化学习是有望从根本上改变人工智慧系统学习和决策方式的新技术。

目前的实作主要围绕量子-经典混合系统，其中量子处理器处理某些计算任务，而经典电脑则负责资料预处理、后处理和系统控制。这种方法最大限度地发挥了两种范式的优势，同时缓解了当前量子硬体的局限性，例如杂讯、退相干和有限的量子位元数。

量子机器学习的市场潜力涵盖众多高价值应用，在这些应用中，量子机器学习可以发挥显着优势。金融机构正在探索用于投资组合优化、风险分析和诈欺检测的量子演算法，这些演算法能够同时处理多种市场场景，从而製定更优的投资策略。医疗保健和製药公司正在探索药物研发、蛋白质折迭预测和个人化医疗领域的量子应用，因为量子电脑或许能够以前所未有的精确度模拟分子相互作用。

製造业正在评估量子最佳化在供应链管理、品质控制和预测性维护中的应用，而网路安全应用则包括量子安全加密和进阶威胁侦测系统。该技术的潜力也扩展到气候建模、交通优化、科学研究以及其他传统计算受限的应用领域。

本报告探讨了目前的杂讯中型量子 (NISQ) 时代，其特点是量子系统拥有 50 至 1,000 个量子位元。虽然这些量子系统尚无法证明其普遍的量子优越性，但它们是迈向能够可靠执行复杂 QML 演算法的容错量子电脑的重要基石。

主要课题包括量子退相干（由于环境干扰，量子态会迅速退化）、超过传统计算的量子误差率以及量子程式专家的短缺。此外，硬体成本对许多公司来说仍然过高，因此需要基于云端的存取模型和量子即服务 (QaaS)。

竞争格局包括开发量子硬体和量子软体平台的领先科技公司、专注于量子运算的公司，以及将量子技术整合到现有产品中的传统科技公司。政府投资、学术研究计画​​和创投基金正在加速量子机器学习 (QML) 的开发过程和商业应用。

本报告提供全球量子机器学习(QML)市场相关调查分析，市场规模与预测，演算法和软体的形势，投资与资金筹措的生态系统，主要企业49公司的简介等资讯。

目录

第1章 摘要整理

  • 量子机器学习市场推动因素
  • QML 演算法与软体
  • 从机器学习到量子机器学习
  • QML 的各个阶段
  • 优点
  • 课题
  • QML的蓝图

第2章 简介

  • 什么是量子机器学习？
  • 经典计算范式与量子计算范式
  • 量子力学原理
  • 机器学习基础
  • 交叉路口：为何要将量子运算和机器学习结合？
  • 市场发展
  • 领域现状
  • 应用程式和用例
  • 课题与局限性
  • 技术与效能路线图

第3章 QML的演算法和软体

  • 机器学习
  • 机器学习的类型
  • 量子深度学习与量子神经网络
  • 量子反向传播
  • QML 中的 Transformer
  • QDL 中的感知器
  • 机器学习资料集
  • 量子编码
  • 混合量子经典机器学习以及通往真正 QML 的道路
  • 最佳化技术
  • 云端 QML 和 QML 即服务
  • QML 中的安全与隐私
  • 人工智慧、机器学习、深度学习与量子运算
  • 日益增长的QML 在训练和推理阶段的漏洞
  • QML 云端和 QML 即服务的安全性
  • 专利态势
  • QML 架构的安全性
  • 企业级

第4章 QML硬体设备和基础设施

  • 概述
  • 路线图
  • 成本
  • 量子退火
  • NISQ 计算机和 QML
  • 超越 NISQ 的 QML
  • 使用 QML 製造和优化量子硬体
  • 机器学习和 QRNG

第5章 QML的市场与用途

  • QML的机会
  • 金融·银行
    • 概要
    • 用途
    • 企业
  • 医疗·生命科学
    • 概要
    • 用途
    • 感测器
    • 个人化医疗
    • 药物研发
    • 製药·QML
    • 企业
  • 製造
    • 概要
    • 用途
  • 其他应用
  • QML 在各产业的优势 Megumi 分析
  • 市场规模及成长预测 (2026-2040)
  • 区域市场
    • 北美
    • 欧洲
    • 亚太地区
    • 其他地区
    • 地区的投资与政策架构
  • QML市场区隔
    • 各技术类型
    • 各应用领域
  • 市场促进因素与阻碍因素
  • QML技术准备度的评估
  • 市场成长情势

第6章 投资与资金筹措

  • 创业投资与民间投资趋势
  • 政府的资金援助和国家的配合措施
  • 企业的研究开发投资

第7章 企业简介(企业47公司的简介)

第8章 词彙表

第9章 调查手法

第10章 参考文献

Quantum Machine Learning (QML) harnesses the unique properties of quantum mechanics-superposition, entanglement, and quantum interference-to potentially solve machine learning problems exponentially faster than classical computers. Quantum Machine Learning represents a paradigm shift in computational intelligence, where quantum algorithms can process vast datasets simultaneously through quantum superposition, enabling multiple calculations to occur in parallel. Unlike classical bits that exist in definitive states of 0 or 1, quantum bits (qubits) can exist in superposition states, allowing quantum computers to explore multiple solution paths simultaneously. This quantum advantage becomes particularly pronounced in optimization problems, pattern recognition, and complex data analysis tasks that form the core of machine learning applications.

The field encompasses several key approaches including quantum-enhanced machine learning, where classical algorithms are accelerated using quantum processors, and quantum-native machine learning, where entirely new algorithms leverage quantum mechanical properties. Quantum neural networks, quantum support vector machines, and quantum reinforcement learning represent emerging methodologies that could fundamentally transform how artificial intelligence systems learn and make decisions.

Current implementations focus on hybrid quantum-classical systems, where quantum processors handle specific computational tasks while classical computers manage data preprocessing, post-processing, and system control. This approach maximizes the strengths of both paradigms while mitigating current quantum hardware limitations such as noise, decoherence, and limited qubit counts.

The market potential spans numerous high-value applications where quantum machine learning could provide significant advantages. Financial institutions are exploring quantum algorithms for portfolio optimization, risk analysis, and fraud detection, where the ability to process multiple market scenarios simultaneously could yield superior investment strategies. Healthcare and pharmaceutical companies are investigating quantum-enhanced drug discovery, protein folding prediction, and personalized medicine applications, where quantum computers could simulate molecular interactions with unprecedented accuracy.

Manufacturing sectors are evaluating quantum optimization for supply chain management, quality control, and predictive maintenance, while cybersecurity applications include quantum-resistant cryptography and advanced threat detection systems. The technology's potential extends to climate modeling, traffic optimization, and scientific research applications where classical computational limitations currently constrain progress.

The report examines the current Noisy Intermediate-Scale Quantum (NISQ) era, characterized by quantum systems with 50-1000 qubits that exhibit significant noise and limited error correction. While these systems cannot yet demonstrate universal quantum advantage, they serve as crucial stepping stones toward fault-tolerant quantum computers capable of running complex QML algorithms reliably.

Key challenges include quantum decoherence, where quantum states deteriorate rapidly due to environmental interference, quantum error rates that currently exceed classical computation, and the scarcity of quantum programming expertise. Hardware costs remain prohibitive for most organizations, necessitating cloud-based access models and quantum-as-a-service offerings.

The competitive landscape includes technology giants developing quantum hardware and software platforms, specialized quantum computing companies, and traditional technology firms integrating quantum capabilities into existing products. Government investments, academic research programs, and venture capital funding are accelerating development timelines and commercial applications.

Report contents include:

  • Detailed market evolution analysis from 2020 through 2040
  • Comprehensive pros and cons assessment of quantum machine learning
  • Technology and performance roadmap with key development milestones
  • Market segmentation by technology type and application sectors
  • Growth projections with multiple scenario analysis
  • Technology readiness assessment across different quantum platforms
  • Algorithm and Software Landscape
    • Machine learning fundamentals and quantum integration approaches
    • Comprehensive analysis of machine learning types and quantum applications
    • Quantum deep learning and quantum neural network architectures
    • Training methodologies for quantum neural networks
    • Applications and use cases for quantum neural networks across industries
    • Neural network types suitable for quantum implementation
    • Quantum generative adversarial networks development and applications
    • Quantum backpropagation techniques and optimization methods
    • Transformers implementation in quantum machine learning systems
    • Perceptrons in quantum deep learning architectures
    • Dataset characteristics and quantum data encoding requirements
    • Quantum encoding schemes and their performance characteristics
    • Hybrid quantum/classical ML development pathways
    • Advanced optimization techniques for quantum machine learning
    • Cloud-based QML services and quantum-as-a-service platforms
    • Security and privacy considerations in quantum machine learning
    • Patent landscape analysis for QML algorithms and implementations
    • Comprehensive profiles of leading QML software companies
  • Hardware Infrastructure Analysis
    • Quantum computing hardware overview and market assessment
    • Hardware development roadmap through 2040
    • Comprehensive cost analysis for quantum computing systems
    • Quantum annealing systems and their ML applications
    • Comparison between quantum annealing and gate-based systems
    • NISQ computers specifications for machine learning applications
    • Error rates and coherence times across different platforms
    • Hardware optimization using quantum machine learning techniques
    • Quantum random number generators for ML applications
    • Leading hardware companies and their technology approaches
  • Application Sector Analysis
    • Comprehensive QML opportunities across multiple industries
    • Financial services and banking applications including risk analysis and optimization
    • Healthcare and life sciences applications for drug discovery and diagnostics
    • Sensor integration for quantum ML-based diagnostic systems
    • Personalized medicine implementation using quantum algorithms
    • Pharmaceutical applications and drug discovery acceleration
    • Manufacturing sector applications for optimization and quality control
    • Additional applications across various industries and use cases
    • Cross-industry benefit analysis and performance comparisons
  • Market Forecasts and Projections
    • Global QML market size projections by year (2026-2040)
    • Regional market growth rates and compound annual growth rate analysis
    • Market segmentation by technology type with revenue projections
    • Application sector segmentation with detailed revenue forecasts
    • Market drivers versus restraints impact analysis
    • Technology readiness assessment matrix across platforms
    • Hardware versus software revenue split projections
    • Market penetration rates by industry sector
    • Technology adoption milestones and timeline analysis
    • Market growth scenarios including conservative, base, and optimistic projections
    • Technology maturity curve analysis and commercial viability assessment
  • Investment and Funding Ecosystem
    • Venture capital investment trends in QML companies
    • Government funding programs and national quantum initiatives
    • Corporate R&D spending patterns and investment strategies
    • Investment trends segmented by technology focus areas
    • Public-private partnership models and collaboration frameworks
  • Company Profiles and Competitive Analysis
    • Comprehensive profiles of 49 leading companies in the QML ecosystem. Companies profiled include AbaQus, Adaptive Finance, Aliro Quantum, Amazon/AWS, Atom Computing, Baidu Inc., BlueQubit Inc., Cambridge Quantum Computing (CQC), D-Wave, GenMat, Google Quantum AI, IBM, IonQ, Kuano, MentenAI, MicroAlgo, Microsoft, Mind Foundry, Mphasis, Nordic Quantum Computing Group, ORCA Computing, Origin Quantum Computing Technology, OTI Lumionics, Oxford Quantum Circuits, Pasqal, PennyLane/Xanadu, planqc GmbH, Polaris Quantum Biotech (POLARISqb), ProteinQure, and more....

TABLE OF CONTENTS

1. EXECUTIVE SUMMARY

  • 1.1. Quantum Machine Learning Market Drivers
  • 1.2. Algorithms and Software for QML
  • 1.3. Machine Learning to Quantum Machine Learning
  • 1.4. QML Phases
    • 1.4.1. The First Phase of QML
    • 1.4.2. The Second Phase of QML
  • 1.5. Advantages
    • 1.5.1. Improved Optimization and Generalization
    • 1.5.2. Quantum Advantage
    • 1.5.3. Training Advantages and Opportunities
    • 1.5.4. Quantum Advantage and ML
    • 1.5.5. Improved Accuracy
  • 1.6. Challenges
    • 1.6.1. Costs
    • 1.6.2. Nascent Technology
    • 1.6.3. Training
    • 1.6.4. Quantum Memory Issues
  • 1.7. QML Roadmap

2. INTRODUCTION

  • 2.1. What is Quantum Machine Learning?
  • 2.2. Classical vs. Quantum Computing Paradigms
  • 2.3. Quantum Mechanical Principles
  • 2.4. Machine Learning Fundamentals
  • 2.5. The Intersection: Why Combine Quantum and ML?
  • 2.6. Market evolution
  • 2.7. Current State of the Field
  • 2.8. Applications and Use Cases
  • 2.9. Challenges and Limitations
  • 2.10. Technology and Performance Roadmap

3. QML ALGORITHMS AND SOFTWARE

  • 3.1. Machine Learning
  • 3.2. Types of Machine Learning
  • 3.3. Quantum Deep Learning and Quantum Neural Networks
    • 3.3.1. Quantum Deep Learning
    • 3.3.2. Training Quantum Neural Networks
    • 3.3.3. Applications for Quantum Neural Networks
    • 3.3.4. Types of Neural Networks
    • 3.3.5. Quantum Generative Adversarial Networks
  • 3.4. Quantum Backpropagation
  • 3.5. Transformers in QML
  • 3.6. Perceptrons in QDL
  • 3.7. ML Datasets
  • 3.8. Quantum Encoding
  • 3.9. Hybrid Quantum/Classical ML and the Path to True QML
    • 3.9.1. Quantum Principal Component Analysis
      • 3.9.1.1. Handling Larger Data Sets
      • 3.9.1.2. Dimensionality Reduction
      • 3.9.1.3. Uses of Grover's Algorithm
  • 3.10. Optimization Techniques
  • 3.11. QML-over-the-Cloud and QML-as-a-Service
  • 3.12. Security and Privacy in QML
  • 3.13. AI, Machine Learning, Deep Learning and Quantum Computing
  • 3.14. Growing QML Vulnerabilities During the Training and Inference Phases
  • 3.15. Security on QML Clouds and QML-as-a-Service
  • 3.16. Patent Landscape
    • 3.16.1. Quantum Machine Learning Patents by Type (2020-2025)
    • 3.16.2. QML Algorithms
  • 3.17. Security on QML Architecture
  • 3.18. Companies

4. QML HARDWARE AND INFRASTRUCTURE

  • 4.1. Overview
  • 4.2. Roadmap
  • 4.3. Costs
  • 4.4. Quantum Annealing
    • 4.4.1. Quantum Annealing vs. Gate-based Systems
    • 4.4.2. Companies
  • 4.5. NISQ Computers and QML
    • 4.5.1. NISQ System Specifications for QML
    • 4.5.2. Companies
  • 4.6. QML beyond NISQ
  • 4.7. Fabricating and Optimizing Quantum Hardware Using QML
  • 4.8. Machine Learning and QRNGs

5. QML MARKETS AND APPLICATIONS

  • 5.1. QML Opportunities
  • 5.2. Finance and Banking
    • 5.2.1. Overview
    • 5.2.2. Applications
    • 5.2.3. Companies
  • 5.3. Healthcare and Life Sciences
    • 5.3.1. Overview
    • 5.3.2. Applications
    • 5.3.3. Sensors
    • 5.3.4. Personalized Medicine
    • 5.3.5. Drug Discovery
    • 5.3.6. Pharma and QML
    • 5.3.7. Companies
  • 5.4. Manufacturing
    • 5.4.1. Overview
    • 5.4.2. Applications
  • 5.5. Other Applications
  • 5.6. Cross-Industry QML Benefit Analysis
  • 5.7. Market Size and Growth Projections (2026-2040)
  • 5.8. Regional Market
    • 5.8.1. North America
    • 5.8.2. Europe
    • 5.8.3. Asia-Pacific
    • 5.8.4. Rest of World
    • 5.8.5. Regional Investment and Policy Framework
  • 5.9. QML Market Segmentation
    • 5.9.1. By Technology Type
    • 5.9.2. By Application Sector
  • 5.10. Market Drivers vs. Restraints
  • 5.11. QML Technology Readiness Assessment
  • 5.12. Market Growth Scenarios

6. INVESTMENT AND FUNDING

  • 6.1. Venture Capital and Private Investment Trends
  • 6.2. Government Funding and National Initiatives
  • 6.3. Corporate R&D Investment

7. COMPANY PROFILES (47 company profiles)

8. GLOSSARY OF TERMS

9. RESEARCH METHODOLOGY

10. REFERENCES

List of Tables

  • Table 1. The Six Segments of the Quantum Machine Language Market
  • Table 2. Quantum Machine Learning Market Drivers
  • Table 3. Opportunities in Algorithms and Software for QML
  • Table 4. Advantages of QML
  • Table 5. QML Challenges
  • Table 6. Comparison of the Prospects and Challenges of QML
  • Table 7. QML Pros and Cons
  • Table 8. Classical ML vs. Quantum ML Performance Comparison
  • Table 9. Types of Machine Learning
  • Table 10. QML Algorithm Classification Matrix
  • Table 11. Quantum Neural Network Architectures Comparison
  • Table 12. Training Time Comparison: Classical vs. Quantum Networks
  • Table 13. Applications for Quantum Neural Networks
  • Table 14. Types of Neural Networks
  • Table 15. Quantum Generative Adversarial Networks
  • Table 16. QML Software Platform Feature Comparison
  • Table 17. ML Transformer Applications
  • Table 18. Cloud-based QML Service Providers Analysis
  • Table 19. Characteristics of ML Data by Source
  • Table 20. QML Encoding Schemes
  • Table 21. QML Development Frameworks Comparison
  • Table 22. QML Security Vulnerability Assessment
  • Table 23. Quantum Machine Learning Patents by Type (2020-2025)
  • Table 24. Patent Landscape in QML Algorithms (2020-2025)
  • Table 25. QML Software Companies
  • Table 26. Quantum Computing Hardware Cost Analysis
  • Table 27. Cloud Access Pricing Models for Quantum Hardware
  • Table 28. Quantum Hardware Performance Metrics Trends
  • Table 29. Quantum Hardware Platform Comparison Matrix
  • Table 30. Quantum Annealing vs. Gate-based Systems for ML
  • Table 31. Companies in Quantum Annealing
  • Table 32. NISQ System Specifications for QML
  • Table 33. Companies in NISQ Computers and QML
  • Table 34. Error Rates and Coherence Times by Platform
  • Table 35. Applications for QML in Banking and Financial Services
  • Table 36. Companies in QML for Banking and Financial Services
  • Table 37. Healthcare and Life Science QML Applications
  • Table 38. Drug Discovery QML vs. Classical ML Performance
  • Table 39. Companies in QML for Healthcare and Life Sciences
  • Table 40. Manufacturing QML Use Cases and Benefits
  • Table 41. Other Potential Applications of QML
  • Table 42. Cross-Industry QML Benefit Analysis
  • Table 44. Revenues from Quantum Machine Learning and Quantum Deep Learning ($ Millions) 2026-2040
  • Table 45. Revenue Projections by Geographic Region
  • Table 46. QML Market Segmentation by Technology Type (2026-2040)-Millions USD
  • Table 47. QML Market Segmentation by Application Sector (2026-2040)-Millions USD
  • Table 48. Market Drivers vs. Restraints Impact Analysis
  • Table 49. QML Technology Readiness Assessment Matrix
  • Table 50. VC Investment in QML Companies (2020-2025)
  • Table 51. Government Funding Programs by Country
  • Table 52. Extensive Glossary of Quantum Machine Learning Terms

List of Figures

  • Figure 1. Machine Learning and Quantum Machine Learning
  • Figure 2. QML Roadmap
  • Figure 3. QML Market Evolution Timeline (2020-2040)
  • Figure 4. Technology and Performance Roadmap
  • Figure 5. QML Hardware Roadmap
  • Figure 6. Financial Services QML Adoption Timeline
  • Figure 7. Manufacturing Sector QML Implementation
  • Figure 8. Global QML Market Size by Year (2026-2040) - Millions USD
  • Figure 9. QML Market Segmentation by Technology Type (2026-2040)-Millions USD
  • Figure 10. QML Market Segmentation by Application Sector (2026-2040)-Millions USD
  • Figure 12. Market Penetration Rates by Industry
  • Figure 13. Technology Adoption Milestones Timeline
  • Figure 14. Market Growth Scenarios (Conservative, Base, Optimistic)
  • Figure 15. IonQ's ion trap
  • Figure 16. IonQ product portfolio