量子时代的机器学习与深度学习（2024）：市场预测与技术评估

机器学习 (ML) 是人工智慧市场最成熟的领域之一，其历史可以追溯到 20 世纪 50 年代。机器学习教导机器执行特定任务并透过识别模式提供准确的结果。量子电脑的出现引发了人们对如何将量子计算的力量应用于机器学习的猜测。人们越来越认识到，量子机器学习 (QML) 可以在更快的执行时间、更高的学习效率和更高的学习能力方面改进经典机器学习。

在本报告中，我们探讨了量子时代的机器学习和深度学习，确定了 QML 的机会和应用，并重点介绍了那些已经开始出现和未来可能出现的机会和应用。它还讨论了 QML 技术如何发展，并包括活跃在该领域的 25 家主要公司和研究机构的概况，以及 QML 收入的 10 年预测。我们也分析了阻碍 QML 成长的因素，包括量子机器学习的成本和不成熟性、对 QML 最佳化演算法的需求，以及对如何最好地实施 QML 的更深入理解。

目录

执行摘要

第1章量子机器学习潜能概述

• 本报告的目的
• QML：可能的好处
• QML：可能的缺点
• 本报告的计划
• 资讯来源
• 预测研究方法

第 2 章 QML 演算法与软体的可能性

• 机器学习及其出现
• 机器学习的类型
• 量子深度学习与量子神经网络
• 量子反向传播的兴起
• QML变压器
• QDL 中的感知器
• 关于 ML 和资料集的一些註释
• 量子演算法：发展与机遇
• 处理大型资料集：量子主成分分析
• Grover 演算法的用途
• 优化技术的改进
• QML 云端和 QML 即服务
• QML 中的安全和隐私
• QML软体公司
  • Dassault/Abaqus
  • GenMat
  • Google
  • Microsoft
  • OTI Lumionics
  • PennyLane/Xanadu
  • ProteinQure
  • 1Qbit and Good Chemistry
  • QC Ware
  • QpiAI
  • Quantistry

第 3 章 QML 硬体注意事项

• 量子退火
• NISQ 计算机和 QML
• 超越 NISQ 的 QML
• 使用 QML 进行量子硬体製造和最佳化
• 关于机器学习和 QRNG 的註释

第4章QML的应用

• QML 机会简介
• QML在金融和银行业的应用
• 医疗保健和生命科学
• QML在製造业的应用
• QML的其他应用

第5章 QML 10 年预测

Machine learning (ML) is one of the most mature segments of the AI market - it dates to the 1950s. ML teaches machines to perform specific tasks and provide accurate results by identifying patterns. The advent of quantum computers has led to speculations on how the power of quantum computing can be applied to ML. A consensus is building that Quantum Machine Learning (QML) can improve classical ML in terms of faster run times, increased learning efficiencies and boosted learning capacity.

QML exhibits several emerging trends:

• Using quantum computers to solve traditional ML problems.
• Developing improved ML algorithms better suited to QML.
• Investigating new ways of delivering QML, especially over a cloud.
• Using classical ML to optimize quantum hardware operations, control systems, and user interfaces.

In this report, IQT Research identifies QML opportunities and applications already beginning to appear and those that we believe will emerge in the future. We also discuss how QML technology will evolve and include ten-year forecasts of QML revenues, along with profiles of 25 profiles of leading firms and research institutes active in the field. The report also analyzes the factors retarding the growth of QML such as the cost and immaturity of quantum machine learning, the need for QML-optimized algorithms and a deeper understanding of how QML is best deployed.

Table of Contents

Executive Summary

• E.1. Factors Driving the Quantum Machine Learning Market
• E.2. Opportunities in Algorithms and Software for QML
  • E.2.1. Translating ML into QML: The First Phase of QML
  • E.2.2. New Algorithms and Products: The Second Phase of QML
• E.3. Thoughts on Deep Learning
• E.4. Advantages of QML
  • E.4.1. Improved Optimization and Generalization
  • E.4.2. QML and Quantum Advantage
• E.5. The Disadvantages of QML
  • E.5.1. High Cost of QCs
  • E.5.2. Early Stage of Technology
  • E.5.3. The Workforce Problem
• E.6. QML Roadmap and Forecasts

Chapter One: A Summary of Quantum Machine Learning Opportunities

• 1.1. Objective of this Report
• 1.2. QML: Possible Advantages
  • 1.2.1. Training Advantages and Opportunities
  • 1.2.2. Quantum Advantage and ML
  • 1.2.3. Improved Accuracy
• 1.3. QML: Possible Disadvantages
  • 1.3.1. Training Challenges
  • 1.3.2. Uncertainty Regarding Quantum Advantage
  • 1.3.3. Quantum Memory Issues
  • 1.3.4. Comparisons of the Prospects and Challenges of QML at the Present Time
• 1.4. Plan of this Report
• 1.5. Information Sources
• 1.6. Forecasting Methodology

Chapter Two: Opportunities in QML Algorithms and Software

• 2.1. Machine Learning and its Emergence
• 2.2. Types of Machine Learning
• 2.3. Quantum Deep Learning and Quantum Neural Networks
  • 2.3.1. Quantum Deep Learning (a.k.a. Deep Quantum Learning)
  • 2.3.2. Training Quantum Neural Networks
  • 2.3.3. Possible Applications for Quantum Neural Networks
  • 2.3.4. Types of Neural Networks
  • 2.3.5. Quantum Generative Adversarial Networks
• 2.4. The Rise of Quantum Backpropagation
• 2.5. Transformers in QML
• 2.6. Perceptrons in QDL
• 2.7. Some Notes on ML and Datasets
• 2.8. Quantum Algorithms: Development and Opportunities
  • 2.8.1. Quantum Encoding
  • 2.8.2. Example of other QML Algorithms
  • 2.8.3. Hybrid Quantum/Classical ML and the Path to True QML
• 2.9. Handling Larger Data Sets: Quantum Principal Component Analysis
  • 2.9.1. Dimensionality Reduction: Quantum Principal Component Analysis
• 2.10. Uses of Grover's Algorithm
• 2.11. Improved Optimization Techniques
• 2.12. QML-over-the-Cloud and QML-as-a-Service
• 2.13. Security and Privacy in QML
  • 2.13.1. Growing QML Vulnerabilities During the Training and Inference Phases
  • 2.13.2. Security on QML Clouds and QML-as-a-Service
  • 2.13.3. Security on QML Architecture
• 2.14. QML Software Companies
  • 2.14.1. Dassault/Abaqus (United States)
  • 2.14.2. GenMat (United States)
  • 2.14.3. Google (United States)
  • 2.14.4. Microsoft (United States)
  • 2.14.5. OTI Lumionics
  • 2.14.6. PennyLane/Xanadu (Canada)
  • 2.14.7. ProteinQure (Canada)
  • 2.14.8. 1Qbit and Good Chemistry (Canada)
  • 2.14.9. QC Ware (United States)
  • 2.14.10. QpiAI (India)
  • 2.14.11. Quantistry (Germany)

Chapter Three: QML Hardware Considerations

• 3.1. Quantum Annealing
  • 3.1.1. A Note on Boltzman Machines
  • 3.1.2. D-Wave (Canada)
• 3.2. NISQ Computers and QML
  • 3.2.1. Amazon/AWS (United States)
  • 3.2.2. Atom Computing
  • 3.2.3. Google AI (United States)
  • 3.2.4. IBM (United States)
  • 3.2.5. IonQ (United States)
  • 3.2.6. Nordic Quantum Computing Group (Norway)
  • 3.2.7. ORCA Computing (UK)
  • 3.2.8. Oxford Quantum Circuits (United Kingdom)
  • 3.2.9. Pasqal (France)
  • 3.2.10. planqc (Germany)
  • 3.2.11. QuEra (United States)
  • 3.2.12. Quantinuum (United States)
  • 3.2.13. Rigetti (United States)
  • 3.2.14. Terra Quantum (Switzerland)
• 3.3. QML beyond NISQ
• 3.4. Fabricating and Optimizing Quantum Hardware Using QML
  • 3.4.1. Mind Foundry (United Kingdom)
  • 3.4.2. QuantrolOx (UK/Finland)
• 3.5. A Note on Machine Learning and QRNGs

Chapter Four: Applications for QML

• 4.1. Introduction to QML Opportunities
• 4.2. Financial and Banking Applications for QML
  • 4.2.1. Adaptive Finance (Canada)
  • 4.2.2. Qkrishi (India)
• 4.3. Healthcare and Life Sciences
  • 4.3.1. Impact of Sensors as a Source of QML-based Diagnostic Data
  • 4.3.2. QML and Personalized Medicine
  • 4.3.3. Pharma and QML
  • 4.3.4. Kuano (Lithuania)
  • 4.3.5. QunaSys (Japan)
  • 4.3.6. MentenAI (Canada)
• 4.4. Manufacturing Sector Applications for QML
• 4.5. Other Applications for QML

Chapter Five: Ten-Year Forecasts of QML

• 5.1. Background to Forecasts
  • 5.1.1. Reasons to Doubt QML
• 5.2. Forecast of QML as Technology
• 5.3. Forecast of QML by Application
• About the Analyst

List of Exhibits

• Exhibit E-1: Ten-year Revenues from Quantum Machine Learning and Quantum Deep Learning ($ Millions)
• Exhibit 1-1: Variations on a QML Theme: The Six Segments of the Quantum Machine Language Market
• Exhibit 1-2: Pros and Cons of QML
• Exhibit 2-1: The Relationship Between AI, Machine Learning, Deep Learning and Quantum Computing
• Exhibit 2-2: Types of ML Learning
• Exhibit 2-3: Selected Neural Network Type/Algorithms
• Exhibit 2-4: ML Transformer Applications
• Exhibit 2-5: Characteristics of ML Data by Source
• Exhibit 2-6: Selected QML Encoding Schemes
• Exhibit 2-7: Other QML Algorithms of Importance
• Exhibit 4-1: Selected Applications for QML in Banking and Financial Services
• Exhibit 4-2: Other Potential Applications of QML
• Exhibit 5-1: Ten-year Revenues from Quantum Machine Learning and Quantum Deep Learning ($ Millions)
• Exhibit 5-2: Ten-year Revenues - QML/ QDL by Application ($ Millions)