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Supriyo Datta
and
Risi Jaiswal
A unique course that connects three diverse fields using the unifying concept of a state-space with 2^N dimensions defined by N binary bits. We start from the seminal concepts of statistical mechanics like entropy, free energy and the law of equilibrium that have been developed with the purpose of describing interacting systems occurring in nature. We then move to the concept of Boltzmann machines (BM) which are interacting systems cleverly engineered to solve important problems in machine learning. Finally, we move to engineered quantum systems stressing the phenomenon of quantum interference which can lead to awesome computing power.
Read more
A unique course that connects three diverse fields using the unifying concept of a state-space with 2^N dimensions defined by N binary bits. We start from the seminal concepts of statistical mechanics like entropy, free energy and the law of equilibrium that have been developed with the purpose of describing interacting systems occurring in nature. We then move to the concept of Boltzmann machines (BM) which are interacting systems cleverly engineered to solve important problems in machine learning. Finally, we move to engineered quantum systems stressing the phenomenon of quantum interference which can lead to awesome computing power.
Read more
A unique course that connects three diverse fields using the unifying concept of a state-space with 2^N dimensions defined by N binary bits. We start from the seminal concepts of statistical mechanics like entropy, free energy and the law of equilibrium that have been developed with the purpose of describing interacting systems occurring in nature. We then move to the concept of Boltzmann machines (BM) which are interacting systems cleverly engineered to solve important problems in machine learning. Finally, we move to engineered quantum systems stressing the phenomenon of quantum interference which can lead to awesome computing power.
What you'll learn
- Boltzmann Law
- Boltzmann Machines
- Transition Matrix
- Quantum Boltzmann Law
- Quantum Gates
Register for this course and see more details by visiting:
OpenCourser.com/course/11khjy/boltzmann
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What's inside
Learning objectives
- Boltzmann law
- Boltzmann machines
- Transition matrix
- Quantum boltzmann law
- Quantum gates
- Boltzmann law
- Boltzmann machines
- Transition matrix
- Quantum boltzmann law
- Quantum gates
Syllabus
Week 1: Boltzmann Law
1.1 State Space
1.2 Boltzmann Law
1.3 Shannon Entropy
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Syllabus
Week 1: Boltzmann Law
1.1 State Space
1.2 Boltzmann Law
1.3 Shannon Entropy
Read more
1.4 Free Energy
1.5 Self-consistent Field
1.6 Summary for Exam 1
Week 2: Boltzmann Machines
2.1. Sampling
2.2. Orchestrating Interactions
2.3. Optimization
2.4. Inference
2.5. Learning
Week 3: Transition Matrix
3.1. Markov Chain Monte Carlo
3.2. Gibbs Sampling
3.3. Sequential versus Simultaneous
3.4. Bayesian Networks
3.5. Feynman Paths
3.6 Summary for Exam 2
Week 4: Quantum Boltzmann Law
4.1. Quantum Spins
4.2. One q-bit Systems
4.3. Spin-spin Interactions
4.4. Two q-bit Systems
4.5. Quantum Annealing
Week 5: Quantum Transition Matrix
5.1. Adiabatic to Gated Computing
5.2. Hadamard Gates
5.3. Grover Search
5.4. Shor's Algorithm
5.5. Feynman Paths
5.6 Summary for Exam 3
Epilogue
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Introduces learners to advanced topics in machine learning and quantum computing
Core audience includes scientists, engineers, and data scientists
Builds a strong foundation in the fundamentals of statistical mechanics
Helps learners develop expertise in Boltzmann machines and Boltzmann law
Taught by recognized experts in statistical mechanics and quantum computing
Requires learners to come in with some background in physics and mathematics
Save this course
Save Boltzmann Law: Physics to Computing 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 Boltzmann Law: Physics to Computing with these
activities:
Review key concepts of statistical mechanics
Show steps
Review the foundational concepts of statistical mechanics to enhance your understanding of Boltzmann Law.
Show steps
-
Revisit textbooks or online resources on statistical mechanics.
-
Focus on understanding the concepts of entropy, free energy, and equilibrium.
Read 'Quantum Computation and Quantum Information' by Nielsen and Chuang
Show steps
Supplement your understanding of quantum concepts by reading an authoritative book that covers fundamental principles and advanced topics.
View
QUANTUM COMPUTATION+QUANTUM INFORMA
on Amazon
Show steps
-
Acquire a copy of the book 'Quantum Computation and Quantum Information'.
-
Read and study the book, focusing on chapters relevant to the course.
Explore Boltzmann Machines (BM) tutorials
Show steps
Delve deeper into Boltzmann Machines by following guided tutorials to strengthen your understanding of their applications in machine learning.
Browse courses on
Machine Learning
Show steps
-
Search for online tutorials or workshops on Boltzmann Machines.
-
Follow the tutorials step-by-step to implement and experiment with BM algorithms.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Solve practice problems on Transition Matrix
Show steps
Reinforce your grasp of Transition Matrix by solving practice problems, improving your problem-solving skills and deepening your understanding.
Show steps
-
Find online resources or textbooks with practice problems on Transition Matrix.
-
Attempt to solve the problems independently, referring to course materials for guidance.
Join a study group for Quantum Transition Matrix
Show steps
Engage with peers in a study group to discuss Quantum Transition Matrix, exchange perspectives, and enhance your comprehension of the topic.
Show steps
-
Form or join a study group with other students enrolled in the course.
-
Meet regularly to discuss concepts, solve problems, and share insights.
Write a blog post on Feynman Paths
Show steps
Expand your understanding of Feynman Paths by writing a blog post that explains the concept and its significance in quantum computing.
Show steps
-
Research and gather information on Feynman Paths and their applications.
-
Write a blog post that explains the concept clearly and engagingly.
-
Publish your blog post and share it with others.
Develop a Quantum Boltzmann Law simulator
Show steps
Solidify your understanding of Quantum Boltzmann Law by creating a simulator that models quantum systems and demonstrates the law's principles.
Show steps
-
Research and gather information on Quantum Boltzmann Law and quantum simulations.
-
Design and implement a simulation program using a programming language of your choice.
-
Test and refine your simulator to ensure accurate results.
Review key concepts of statistical mechanics
Show steps
Review the foundational concepts of statistical mechanics to enhance your understanding of Boltzmann Law.
Show steps
Show steps
Show steps
- Revisit textbooks or online resources on statistical mechanics.
- Focus on understanding the concepts of entropy, free energy, and equilibrium.
Read 'Quantum Computation and Quantum Information' by Nielsen and Chuang
Show steps
Supplement your understanding of quantum concepts by reading an authoritative book that covers fundamental principles and advanced topics.
View
QUANTUM COMPUTATION+QUANTUM INFORMA
on Amazon
Show steps
Show steps
Show steps
- Acquire a copy of the book 'Quantum Computation and Quantum Information'.
- Read and study the book, focusing on chapters relevant to the course.
Explore Boltzmann Machines (BM) tutorials
Show steps
Delve deeper into Boltzmann Machines by following guided tutorials to strengthen your understanding of their applications in machine learning.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Search for online tutorials or workshops on Boltzmann Machines.
- Follow the tutorials step-by-step to implement and experiment with BM algorithms.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Solve practice problems on Transition Matrix
Show steps
Reinforce your grasp of Transition Matrix by solving practice problems, improving your problem-solving skills and deepening your understanding.
Show steps
Show steps
Show steps
- Find online resources or textbooks with practice problems on Transition Matrix.
- Attempt to solve the problems independently, referring to course materials for guidance.
Join a study group for Quantum Transition Matrix
Show steps
Engage with peers in a study group to discuss Quantum Transition Matrix, exchange perspectives, and enhance your comprehension of the topic.
Show steps
Show steps
Show steps
- Form or join a study group with other students enrolled in the course.
- Meet regularly to discuss concepts, solve problems, and share insights.
Write a blog post on Feynman Paths
Show steps
Expand your understanding of Feynman Paths by writing a blog post that explains the concept and its significance in quantum computing.
Show steps
Show steps
Show steps
- Research and gather information on Feynman Paths and their applications.
- Write a blog post that explains the concept clearly and engagingly.
- Publish your blog post and share it with others.
Develop a Quantum Boltzmann Law simulator
Show steps
Solidify your understanding of Quantum Boltzmann Law by creating a simulator that models quantum systems and demonstrates the law's principles.
Show steps
Show steps
Show steps
- Research and gather information on Quantum Boltzmann Law and quantum simulations.
- Design and implement a simulation program using a programming language of your choice.
- Test and refine your simulator to ensure accurate results.
Career center
Learners who complete Boltzmann Law: Physics to Computing will develop knowledge and skills
that may be useful to these careers:
Quantum Computing Engineer
Quantum Computing Engineer
Quantum Computing Engineers design and build quantum computers, which are powerful machines that can solve complex problems that are impossible for classical computers. This course provides an introduction to the fundamental principles of quantum computing, including quantum mechanics, quantum algorithms, and quantum hardware.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers develop and implement machine learning algorithms to solve real-world problems. This course provides a foundation in the fundamental principles of machine learning, including probability, statistics, and optimization. It also covers specific machine learning algorithms, such as linear regression, logistic regression, and neural networks.
Data Scientist
Data Scientist
Data Scientists analyze data to extract insights and make predictions. This course provides a foundation in the fundamental principles of data science, including statistics, machine learning, and data visualization. It also covers specific data science techniques, such as data cleaning, data transformation, and data mining.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course provides a foundation in the fundamental principles of software engineering, including software design, software development, and software testing. It also covers specific software engineering topics, such as object-oriented programming, data structures, and algorithms.
Computer Scientist
Computer Scientist
Computer Scientists conduct research in the field of computer science. This course provides a foundation in the fundamental principles of computer science, including computer architecture, operating systems, and computer networks. It also covers specific computer science topics, such as artificial intelligence, machine learning, and data science.
Physicist
Physicist
Physicists study the fundamental laws of nature. This course provides an introduction to the fundamental principles of physics, including mechanics, electricity and magnetism, and thermodynamics. It also covers specific physics topics, such as quantum mechanics, nuclear physics, and particle physics.
Mathematician
Mathematician
Mathematicians study the properties of numbers, shapes, and other abstract objects. This course provides an introduction to the fundamental principles of mathematics, including algebra, calculus, and statistics. It also covers specific mathematics topics, such as number theory, geometry, and topology.
Statistician
Statistician
Statisticians collect, analyze, and interpret data. This course provides an introduction to the fundamental principles of statistics, including probability, inference, and regression. It also covers specific statistics topics, such as sampling, hypothesis testing, and data analysis.
Actuary
Actuary
Actuaries use mathematical and statistical methods to assess risk and uncertainty. This course provides an introduction to the fundamental principles of actuarial science, including probability, statistics, and finance.
Financial Analyst
Financial Analyst
Financial Analysts analyze financial data to make investment recommendations. This course provides an introduction to the fundamental principles of financial analysis, including financial statement analysis, valuation, and portfolio management.
Investment Banker
Investment Banker
Investment Bankers help companies raise capital and advise on mergers and acquisitions. This course provides an introduction to the fundamental principles of investment banking, including corporate finance, capital markets, and financial modeling.
Management Consultant
Management Consultant
Management Consultants help organizations improve their performance. This course provides an introduction to the fundamental principles of management consulting, including strategy, operations, and organizational behavior.
Marketing Manager
Marketing Manager
Marketing Managers develop and execute marketing campaigns. This course provides an introduction to the fundamental principles of marketing, including market research, product development, and pricing.
Sales Manager
Sales Manager
Sales Managers lead and motivate sales teams. This course provides an introduction to the fundamental principles of sales management, including sales planning, sales forecasting, and customer relationship management.
Human Resources Manager
Human Resources Manager
Human Resources Managers oversee the human resources function of an organization. This course provides an introduction to the fundamental principles of human resources management, including employee relations, compensation and benefits, and training and development.
For more career information including salaries, visit:
OpenCourser.com/course/11khjy/boltzmann
Reading list
We've selected eight 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
Boltzmann Law: Physics to Computing.
Provides a comprehensive introduction to statistical mechanics, covering the basics of the subject as well as more advanced topics. It valuable resource for students and researchers in physics, chemistry, and other fields.
Provides a comprehensive introduction to machine learning from a probabilistic perspective. It covers the basics of probability theory, machine learning algorithms, and applications. It valuable resource for students and researchers in machine learning, artificial intelligence, and related fields.
Save
Provides a gentle introduction to quantum computing. It covers the basics of quantum mechanics, quantum computing algorithms, and applications. It valuable resource for students and researchers in quantum computing, computer science, and related fields.
Provides a comprehensive introduction to statistical physics of particles. It covers the basics of statistical mechanics, as well as more advanced topics such as phase transitions and critical phenomena. It valuable resource for students and researchers in physics, chemistry, and other fields.
Provides a comprehensive introduction to quantum information theory. It covers the basics of quantum mechanics, quantum information theory, and applications. It valuable resource for students and researchers in quantum information theory, computer science, and related fields.
Provides a comprehensive introduction to computational complexity. It covers the basics of computational complexity theory, as well as more advanced topics such as NP-completeness and approximation algorithms. It valuable resource for students and researchers in computer science, mathematics, and related fields.
Provides a comprehensive introduction to quantum error correction. It covers the basics of quantum error correction, as well as more advanced topics such as topological quantum error correction.
Provides a tutorial on quantum computing. It covers the basics of quantum mechanics, quantum computing, and quantum algorithms.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/11khjy/boltzmann
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Effort
5 to 6 hours per week for 5 weeks
Level
Introductory
Via
edX
Institution
Purdue University
Instructors
Supriyo Datta
Risi Jaiswal
Session
March 25, 2024
- May 4, 2024
Language
English
Transcript
English
Good to know
Introduces learners to advanced topics in machine learning and quantum computing
Core audience includes scientists, engineers, and data scientists
Builds a strong foundation in the fundamentals of statistical mechanics
Helps learners develop expertise in Boltzmann machines and Boltzmann law
Taught by recognized experts in statistical mechanics and quantum computing
Requires learners to come in with some background in physics and mathematics
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