• Length:
    5 Weeks
  • Effort:
    5–6 hours per week
  • Price:

    FREE
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  • Institution
  • Subject:
  • Level:
    Advanced
  • Language:
    English
  • Video Transcript:
    English
  • Course Type:
    Instructor-led on a course schedule

Prerequisites

This course is designed for students who have an undergraduate degree in engineering or the physical sciences, specifically differential equations, and linear algebra.

About this course

Skip About this course

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

Skip What you'll learn
  • Boltzmann Law
  • Boltzmann Machines
  • Transition Matrix
  • Quantum Boltzmann Law
  • Quantum Gates

Week 1: Boltzmann Law

1.1 State Space

1.2 Boltzmann Law

1.3 Shannon Entropy

1.4 Free Energy

1.5 Self-consistent Field

1.6 Summary for Exam 1

Week 2: Boltzmann Machines

2.1. Binary Stochastic Neuron

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 Operations

4.3. Spin-spin Interactions

4.4. Two q-bit Operations

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

Meet your instructors

Supriyo Datta
Thomas Duncan Distinguished Professor of Electrical and Computer Engineering, NAE member
Purdue University
Shuvro Chowdhury
PhD Student
Purdue University

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Who can take this course?

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