Explores Markov chains and their applications in algorithms, focusing on user impatience and faithful sample generation.
Explores Monte Carlo moves in simulation, including trial moves and biased moves, comparing Monte Carlo with Molecular Dynamics.
Covers Markov chains, Monte Carlo sampling, isotropy, and the curse of dimensionality.
Covers the interpretation of the Lindblad equation and its unitary part in quantum gases.
Covers the Metropolis-Hastings algorithm and gradient-based approaches for biasing searches towards higher likelihood values.
Explores NISQ devices and IBM Q, covering noisy quantum circuits, qubit technologies, and quantum algorithm development.
