In this unit you will learn the main theoretical properties and practical applications of Quantum Computing (QC) and Tensor Networks.
Basic knowledge of Essentials and of textbook Quantum Mechanics.
Linear Algebra, Tensor Products
Introduction into Matrix Product States and DMRG
Algorithms and Techniques for Matrix Product States
Tips and tricks for matrix product states simulations
Simple Applications
Entropy, conformal charge and all that
Algorithms for tensor networks in larger than 1 dimension
Advanced Applications of tensor networks in condensed matter physics
Real and imaginary time evolution
Advanced Applications of tensor networks in lattice gauge theory
Advanced Applications of tensor networks in conformal field theory
Alternative Techniques (tensor trains etc.)
Vector Spaces, Tensor Products and Qubits
Introduction to Quantum Circuits
Simple Quantum Algorithms I (also hands on tutorials)
Simple Quantum Algorithms II (also hands on tutorials)
Noise in Quantum Computers - part 1
Noise in Quantum Computers - part 2
Introduction to advanced Quantum Algorithms
Variational Quantum Algorithms
Quantum Machine Learning Algorithms
Introduction to Quantum Classifier Algorithms
Barren Plateaus, Problems in Trainability and methods to mitigate them
Error mitigation in quantum computations
Dimensional Expressivity Analysis
Selected Applications I
Selected Applications II
Quantum hardware and advanced applications