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Eliška Greplová
Quantum computing is a fast-growing technology and semiconductor chips are one of the most promising platforms for quantum devices.The current bottleneck for scaling is the ability to control semiconductor computing chips quickly and efficiently.
Read more
Quantum computing is a fast-growing technology and semiconductor chips are one of the most promising platforms for quantum devices.The current bottleneck for scaling is the ability to control semiconductor computing chips quickly and efficiently.
Read more
Quantum computing is a fast-growing technology and semiconductor chips are one of the most promising platforms for quantum devices.The current bottleneck for scaling is the ability to control semiconductor computing chips quickly and efficiently.
This course, aimed at students with experience equivalent to a master’s degree in physics, computer science or electrical engineering introduces hands-on machine learning examples for the application of machine learning in the field of semiconductor quantum devices. Examples include coarse tuning into the correct quantum dot regime, specific charge state tuning, fine tuning and unsupervised quantum dot data analysis.
After the completion of the course students will be able to
- assess the suitability of machine learning for specific qubit tuning or control task and
- implement a machine learning prototype that is ready to be embedded into their experimental or theoretical quantum research and engineering workflow.
Register for this course and see more details by visiting:
OpenCourser.com/course/ppwd64/machine
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What's inside
Learning objectives
- To understand the utility of machine learning in tuning of semiconductor quantum devices
- To formulate various stages of tuning as a machine learning problem
- To develop and implement in python a machine learning prototype for variety of semiconductor qubit tuning tasks
- To assess the suitability of machine learning in specific semiconductor quantum computing experimental workflows
- To understand the utility of machine learning in tuning of semiconductor quantum devices
- To formulate various stages of tuning as a machine learning problem
- To develop and implement in python a machine learning prototype for variety of semiconductor qubit tuning tasks
- To assess the suitability of machine learning in specific semiconductor quantum computing experimental workflows
Syllabus
Week 0: Introduction to the course and self-study of the prerequisites
Week 1: Supervised learning for quantum dot configuration tuning
Review of neural networks
Read more
Syllabus
Week 0: Introduction to the course and self-study of the prerequisites
Week 1: Supervised learning for quantum dot configuration tuning
Review of neural networks
Read more
Formulate configuration tuning as a neural network learning task
Applicability for quantum experiments
Coding demonstration: Supervised supervised neural network configuration classification
Week 2: Charge tuning with neural networks
Introduction to charge tuning
Tuning to specific charge states as supervised neural network with feedback loop
Experimental charge tuning
Coding demonstration: Charge charge state preparation using neural network with feedback loop
Midterm exam (multiple choice)
Week 3: Unsupervised learning for analysis of quantum dot data
Introduction to unsupervised learning
Clustering methods for analysis of charge stability diagrams
Outlook and applicability to experimental systems
Coding demonstration: kernel-PCA clustering of charge stability data
Week 4: Fine-tuning with neural networks
Introduction to fine-tuning
Fine Fine-tuning as a Hamiltonian learning problem
Experimental fine-tuning
Coding demonstration: Hamiltonian learning for qubit characterization
Week 5: Conclusion and Recap
Overview of the techniques and applications
Outlook for artificial intelligence as a tool for control and calibration of quantum devices
Final exam - multiple choice and optional project (video brief) with a forum for questions
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Covers control and calibration of quantum devices, an emerging field in quantum computing
Provides hands-on machine learning examples for implementing machine learning into quantum devices
Introduces a variety of machine learning techniques, including supervised learning, unsupervised learning, and reinforcement learning
Emphasizes the practical applications of machine learning in quantum computing, enabling learners to apply their knowledge to real-world problems
Taught by Eliška Greplová, an experienced researcher in quantum computing, ensuring learners have access to up-to-date knowledge and expertise
Save this course
Save Machine Learning for Semiconductor Quantum Devices to your list so you can find it easily later:
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Machine Learning for Semiconductor Quantum Devices with these
activities:
Review of quantum mechanics
Show steps
Refreshing your knowledge of quantum mechanics will help you understand the theoretical foundations of quantum computing and prepare you for the more advanced topics covered in the course.
Browse courses on
Quantum Mechanics
Show steps
-
Review your lecture notes or textbook from a previous quantum mechanics course.
-
Solve practice problems to test your understanding.
-
Watch online videos or attend a refresher workshop on quantum mechanics.
Organize and review course materials
Show steps
Organizing and reviewing course materials will help you stay on top of the content and prepare for assessments.
Show steps
-
Create a system for organizing your notes, assignments, and other course materials.
-
Review your materials regularly to reinforce your understanding and identify areas where you may need additional support.
-
Use your organized materials to prepare for quizzes, exams, and projects.
Review of supervised learning
Show steps
Reviewing supervised learning techniques will help prepare you for the neural network-based methods in this course.
Browse courses on
Supervised Learning
Show steps
-
Review basic concepts of supervised learning, such as classification, regression, and model evaluation.
-
Implement a supervised learning algorithm, such as linear regression or logistic regression, using a programming language like Python.
-
Practice using supervised learning techniques to solve real-world problems, such as image classification or text classification.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Study group: Quantum computing concepts
Show steps
Discussing quantum computing concepts with peers will help reinforce your understanding and prepare you for the more advanced topics covered in the course.
Browse courses on
Quantum Computing
Show steps
-
Form a study group with other students in the course.
-
Meet regularly to discuss course material, solve problems, and share insights.
-
Prepare presentations on specific quantum computing topics and present them to the group.
Coding challenge: Quantum gate implementation
Show steps
Implementing quantum gates in code will help you understand the underlying principles and prepare you for the more advanced machine learning techniques used in the course.
Browse courses on
Quantum Computing
Show steps
-
Choose a quantum computing library, such as Qiskit or Cirq.
-
Implement basic quantum gates, such as the Hadamard gate, CNOT gate, and Toffoli gate.
-
Combine quantum gates to create quantum circuits.
-
Simulate quantum circuits to observe their behavior.
Tutorial: Machine learning for semiconductor device optimization
Show steps
This tutorial will provide a practical introduction to applying machine learning techniques to optimize semiconductor devices, which is directly relevant to the course content.
Browse courses on
Machine Learning
Show steps
-
Find a tutorial or online course on machine learning for semiconductor device optimization.
-
Follow the tutorial and complete the exercises.
-
Apply the techniques learned in the tutorial to a real-world semiconductor device optimization problem.
Contribute to an open-source quantum computing project
Show steps
Contributing to an open-source quantum computing project will allow you to gain practical experience and deepen your understanding of the field.
Browse courses on
Quantum Computing
Show steps
-
Find an open-source quantum computing project on platforms like GitHub.
-
Identify a specific task or issue that you can contribute to.
-
Submit a pull request with your contribution and follow the project's contribution guidelines.
Project: Implement a machine learning model for quantum dot tuning
Show steps
Implementing a machine learning model for quantum dot tuning will allow you to apply the techniques learned in the course to a practical problem and deepen your understanding.
Browse courses on
Machine Learning
Show steps
-
Choose a specific type of quantum dot tuning problem to focus on.
-
Research different machine learning algorithms and select one that is appropriate for the problem.
-
Implement the machine learning algorithm and train it on a dataset of quantum dot tuning data.
-
Evaluate the performance of the machine learning model and make adjustments as needed.
-
Write a report summarizing your work and present your findings.
Review of quantum mechanics
Show steps
Refreshing your knowledge of quantum mechanics will help you understand the theoretical foundations of quantum computing and prepare you for the more advanced topics covered in the course.
Browse courses on
Quantum Mechanics
Show steps
Show steps
Show steps
- Review your lecture notes or textbook from a previous quantum mechanics course.
- Solve practice problems to test your understanding.
- Watch online videos or attend a refresher workshop on quantum mechanics.
Organize and review course materials
Show steps
Organizing and reviewing course materials will help you stay on top of the content and prepare for assessments.
Show steps
Show steps
Show steps
- Create a system for organizing your notes, assignments, and other course materials.
- Review your materials regularly to reinforce your understanding and identify areas where you may need additional support.
- Use your organized materials to prepare for quizzes, exams, and projects.
Review of supervised learning
Show steps
Reviewing supervised learning techniques will help prepare you for the neural network-based methods in this course.
Browse courses on
Supervised Learning
Show steps
Show steps
Show steps
- Review basic concepts of supervised learning, such as classification, regression, and model evaluation.
- Implement a supervised learning algorithm, such as linear regression or logistic regression, using a programming language like Python.
- Practice using supervised learning techniques to solve real-world problems, such as image classification or text classification.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Study group: Quantum computing concepts
Show steps
Discussing quantum computing concepts with peers will help reinforce your understanding and prepare you for the more advanced topics covered in the course.
Browse courses on
Quantum Computing
Show steps
Show steps
Show steps
- Form a study group with other students in the course.
- Meet regularly to discuss course material, solve problems, and share insights.
- Prepare presentations on specific quantum computing topics and present them to the group.
Coding challenge: Quantum gate implementation
Show steps
Implementing quantum gates in code will help you understand the underlying principles and prepare you for the more advanced machine learning techniques used in the course.
Browse courses on
Quantum Computing
Show steps
Show steps
Show steps
- Choose a quantum computing library, such as Qiskit or Cirq.
- Implement basic quantum gates, such as the Hadamard gate, CNOT gate, and Toffoli gate.
- Combine quantum gates to create quantum circuits.
- Simulate quantum circuits to observe their behavior.
Tutorial: Machine learning for semiconductor device optimization
Show steps
This tutorial will provide a practical introduction to applying machine learning techniques to optimize semiconductor devices, which is directly relevant to the course content.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Find a tutorial or online course on machine learning for semiconductor device optimization.
- Follow the tutorial and complete the exercises.
- Apply the techniques learned in the tutorial to a real-world semiconductor device optimization problem.
Contribute to an open-source quantum computing project
Show steps
Contributing to an open-source quantum computing project will allow you to gain practical experience and deepen your understanding of the field.
Browse courses on
Quantum Computing
Show steps
Show steps
Show steps
- Find an open-source quantum computing project on platforms like GitHub.
- Identify a specific task or issue that you can contribute to.
- Submit a pull request with your contribution and follow the project's contribution guidelines.
Project: Implement a machine learning model for quantum dot tuning
Show steps
Implementing a machine learning model for quantum dot tuning will allow you to apply the techniques learned in the course to a practical problem and deepen your understanding.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Choose a specific type of quantum dot tuning problem to focus on.
- Research different machine learning algorithms and select one that is appropriate for the problem.
- Implement the machine learning algorithm and train it on a dataset of quantum dot tuning data.
- Evaluate the performance of the machine learning model and make adjustments as needed.
- Write a report summarizing your work and present your findings.
Career center
Learners who complete Machine Learning for Semiconductor Quantum Devices will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models. They work with Data Scientists to identify the most appropriate models for a given problem and then implement and evaluate these models. This course will help you build a foundation in machine learning. You will learn how to formulate problems as machine learning tasks and how to implement and evaluate machine learning models. This knowledge will be valuable to you as a Machine Learning Engineer, as it will allow you to design, develop, and deploy machine learning models for a variety of applications.
Data Scientist
Data Scientist
Data Scientists use machine learning and other statistical models to observe the world. They understand how these models work and are able to identify the most appropriate methods to use for a given problem. This course will help you build a foundation in machine learning. You will learn how to formulate problems as machine learning tasks and how to implement and evaluate machine learning models. This knowledge will be valuable to you as a Data Scientist, as it will allow you to use machine learning to solve problems in a variety of domains.
Research Scientist
Research Scientist
Research Scientists conduct research in a variety of fields, including computer science, physics, and engineering. They use their knowledge of machine learning and other statistical models to develop new methods for solving problems. This course will help you build a foundation in machine learning. You will learn how to formulate problems as machine learning tasks and how to implement and evaluate machine learning models. This knowledge will be valuable to you as a Research Scientist, as it will allow you to use machine learning to develop new methods for solving problems in your field.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They may work on a variety of projects, from small personal projects to large enterprise systems. This course will help you build a foundation in machine learning. You will learn how to formulate problems as machine learning tasks and how to implement and evaluate machine learning models. This knowledge will be valuable to you as a Software Engineer, as it will allow you to use machine learning to develop new features and improve the performance of existing software applications.
Data Analyst
Data Analyst
Data Analysts use machine learning and other statistical methods to analyze data. They work with Data Scientists to identify trends and patterns in data and to develop models that can predict future outcomes. This course will help you build a foundation in machine learning. You will learn how to formulate problems as machine learning tasks and how to implement and evaluate machine learning models. This knowledge will be valuable to you as a Data Analyst, as it will allow you to use machine learning to analyze data and develop models that can predict future outcomes.
Project Manager
Project Manager
Project Managers are responsible for planning, organizing, and executing projects. They work with teams of people to achieve project goals. This course will help you build a foundation in machine learning. You will learn how to formulate problems as machine learning tasks and how to implement and evaluate machine learning models. This knowledge will be valuable to you as a Project Manager, as it will allow you to use machine learning to improve the efficiency of your projects.
Marketing Analyst
Marketing Analyst
Marketing Analysts use machine learning and other statistical methods to analyze marketing data. They work with marketing teams to develop and implement marketing campaigns and to measure the effectiveness of these campaigns. This course will help you build a foundation in machine learning. You will learn how to formulate problems as machine learning tasks and how to implement and evaluate machine learning models. This knowledge will be valuable to you as a Marketing Analyst, as it will allow you to use machine learning to analyze marketing data and develop and implement effective marketing campaigns.
Product Manager
Product Manager
Product Managers are responsible for the development and management of products. They work with engineers, designers, and marketers to bring new products to market and to improve existing products. This course will help you build a foundation in machine learning. You will learn how to formulate problems as machine learning tasks and how to implement and evaluate machine learning models. This knowledge will be valuable to you as a Product Manager, as it will allow you to use machine learning to develop new products and improve existing products.
Consultant
Consultant
Consultants provide advice and expertise to organizations. They work with organizations to identify problems and to develop and implement solutions. This course will help you build a foundation in machine learning. You will learn how to formulate problems as machine learning tasks and how to implement and evaluate machine learning models. This knowledge will be valuable to you as a Consultant, as it will allow you to use machine learning to develop and implement solutions to problems faced by organizations.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use machine learning and other statistical methods to analyze financial data. They work with portfolio managers to develop trading strategies and to manage risk. This course will help you build a foundation in machine learning. You will learn how to formulate problems as machine learning tasks and how to implement and evaluate machine learning models. This knowledge will be valuable to you as a Quantitative Analyst, as it will allow you to use machine learning to analyze financial data and develop trading strategies.
Teacher
Teacher
Teachers educate students in a variety of subjects. They work with students to help them learn and grow. This course will help you build a foundation in machine learning. You will learn how to formulate problems as machine learning tasks and how to implement and evaluate machine learning models. This knowledge will be valuable to you as a Teacher, as it will allow you to use machine learning to develop and deliver effective lessons.
Researcher
Researcher
Researchers conduct research in a variety of fields. They work to develop new knowledge and to solve problems. This course will help you build a foundation in machine learning. You will learn how to formulate problems as machine learning tasks and how to implement and evaluate machine learning models. This knowledge will be valuable to you as a Researcher, as it will allow you to use machine learning to develop new knowledge and solve problems in your field.
Financial Analyst
Financial Analyst
Financial Analysts use machine learning and other statistical methods to analyze financial data. They work with companies to evaluate their financial performance and to make investment recommendations. This course will help you build a foundation in machine learning. You will learn how to formulate problems as machine learning tasks and how to implement and evaluate machine learning models. This knowledge will be valuable to you as a Financial Analyst, as it will allow you to use machine learning to analyze financial data and make investment recommendations.
Business Analyst
Business Analyst
Business Analysts use machine learning and other statistical methods to analyze data and to develop models that can improve the performance of businesses. They work with businesses to identify opportunities for improvement and to develop strategies for achieving these improvements. This course will help you build a foundation in machine learning. You will learn how to formulate problems as machine learning tasks and how to implement and evaluate machine learning models. This knowledge will be valuable to you as a Business Analyst, as it will allow you to use machine learning to analyze data and develop models that can improve the performance of businesses.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use machine learning and other statistical methods to analyze data and to develop models that can improve the efficiency of operations. They work with businesses to improve their supply chains, scheduling, and other operations. This course will help you build a foundation in machine learning. You will learn how to formulate problems as machine learning tasks and how to implement and evaluate machine learning models. This knowledge will be valuable to you as an Operations Research Analyst, as it will allow you to use machine learning to analyze data and develop models that can improve the efficiency of operations.
For more career information including salaries, visit:
OpenCourser.com/course/ppwd64/machine
Reading list
We've selected seven books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Machine Learning for Semiconductor Quantum Devices.
A classic textbook on quantum computation and quantum information, covering a wide range of topics.
Provides a gentle introduction to quantum computing for computer scientists, covering the basics of quantum mechanics and quantum algorithms.
Save
Offers a comprehensive overview of quantum computing for beginners, covering the history, principles, and applications.
A concise introduction to quantum computing, covering the essential concepts and algorithms.
An introduction to quantum computer science, covering topics such as quantum algorithms and quantum information theory.
Provides a comprehensive introduction to quantum information theory, covering topics such as quantum entanglement and quantum communication.
A comprehensive textbook on quantum computing, covering topics such as quantum algorithms and quantum information theory.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/ppwd64/machine
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Effort
6 to 7 hours per week for 6 weeks
Level
Advanced
Via
edX
Institution
Delft University of Technology
Instructor
Eliška Greplová
Session
February 6, 2024
- July 31, 2024
Language
English
Transcript
English
Good to know
Covers control and calibration of quantum devices, an emerging field in quantum computing
Provides hands-on machine learning examples for implementing machine learning into quantum devices
Introduces a variety of machine learning techniques, including supervised learning, unsupervised learning, and reinforcement learning
Emphasizes the practical applications of machine learning in quantum computing, enabling learners to apply their knowledge to real-world problems
Taught by Eliška Greplová, an experienced researcher in quantum computing, ensuring learners have access to up-to-date knowledge and expertise
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