양자 시대의 머신러닝과 딥러닝(2024년) : 시장 예측 및 기술 평가

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)