Summary
REPORT WILL BE PUBLISHED IN SEPTEMBER, 2024 — PRE-ORDER NOW!
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.
Report: IQT-QML2024-0924
Published: September, 2024
ページTOPに戻る
Table of Contents
TABLE OF CONTENTS:
Chapter One: A Summary of Quantum Machine Learning Opportunities
Chapter Two: Emerging QML Technology
2.1 Can quantum computers speed up machine learning?
2.1.1 Types of quantum computers being used for ML
2.1.2 Error correction, fault tolerance and ML
2.2 QML state of the art
2.3 Evolution of QML algorithms and software platforms
2.3.1 Using quantum computers to enhance ML algorithms
2.3.2 Developing improved ML algorithms better suited to QML
2.4 QML over the cloud
2.5 A QML roadmap: Ten-year forecasts
2.5.1 QML revenue forecasts by application
2.5.2 QML revenue forecasts by type of platform
Chapter Three: Applications
3.1 Using quantum computers to solve traditional ML problems
3.2 Improving ML training: LLMs and neural networks
3.3 Acceleration of large data sets and classification for image and speech recognition
3.4 Improved Natural Language Processing (NLP)
3.5 Solving near-to-medium term problems in quantum chemistry: New drugs and new materials
3.6 Financial applications: Portfolio optimization, risk management, fraud detection, and trading
3.7 Energy: Grid optimization, demand prediction, energy storage and integration of renewables
3.8 Manufacturing: Scheduling, shift scheduling, resource allocation, and defect detection,
3.9 Retail: Demand forecasting, inventory management, delivery route optimization, loss prevention, and store layout optimization
3.10 Aerospace: Trajectory optimization, flight data analysis, and communication optimization
3.11 Environment: Weather forecasting, disaster prediction, environmental modeling, and natural disaster response management
3.12 Healthcare services, including medical image analysis, diagnosis assistance, treatment plan optimization, early detection screening, and clinical trial design
3.13 Customer service and social media, including personal recommendations, sentiment analysis, support ticket routing, omnichannel experience optimization, and chatbots
3.14. Autonomous systems: self-driving cars, drones, and robots
3.15 Using classical ML to optimize quantum hardware operations, control systems, and interfaces.
Chapter Four: Key Vendors
The report will contain profiles of 25-30 QML vendors. The vendors from which this coverage report will be selected are shown below:
1Qbit
AbaQus
Adaptive Finance
Algo Dynamix
Amazon
Atom Computing
Anyon Systems
Atlantic Quantum
Azure Quantum
Data Reply
D-wave
Equal 1. Labs
Euler Scientific
GoogleAI
IBM
Intel
IonQ
Kuano
MentenAI
Microsoft
Miraex
Nordic Quantum Computing
Orca Computing
OTI Lumionics
Oxford Quantum Circuits
PlanQC
ProteinCure
Q-Chem
QCWare
QpAI
Qkrishi
Quantum Generative Materials
Quantum Machines
Quantagonia
Quantistry
QuEra
QuantrolOx
Quantinuum
Quantum Machines
Qoherent
QuanSys
Rigetti
Riverlane
Terra Quantum
Xanadu
Zapata Computing
