COURSE OBJECTIVES:
The problem of understanding the structural dynamics of macromolecular systems provides an
ideal framework to develop and validate new paradigms for advanced multi-scale computing.
In this short graduate-level course, I will discuss how the integration of advanced statistical
mechanics techniques with machine learning algorithms and (very recently) quantum computing
hardware is opening new research avenues in this field, paving the way to accurately simulating
complex transitions with an unprecedented level of accuracy and resolution.
STRUCTURE OF THE COURSE
The course consists in a set of 8 two-hour lectures intended to cover concepts in advanced
statistical mechanics and theory of stochastic processes, embedded in the framework of
molecular simulation of rare macromolecular transitions
PREREQUISITES:
A background in equilibrium statistical mechanics and basic (undergraduate level) quantum
mechanics will be assumed.
SYLLABUS:

• LECT 1: Macromolecules as complex systems: classical and quantum degrees of freedom,
  frustration, metastability, and multi-scale rare event problems.
• LECT 2: Stochastic dynamics of macromolecules: Langevin equation and Fokker-Plank
  Equation
• LECT 3: Renormalizing the Stochastic Dynamics: Markov State modelling
• LECT 4: Stochastic path integrals and their applications to macromolecular systems. Statistical
  mechanics of transition paths
• LECT 5: Machine learning based enhanced sampling methods.
• LECT 6: Machine learning schemes for data reduction and intrinsic manifold exploration
• LECT 7: Introduction to quantum annealing and QUBO
• LECT 8: Quantum encoding and quantum computing of paradigmatically hard statistical
  mechanical problems