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. 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