Quantum Monte Carlo: Basic Concepts

Related lectures (32)

Page 3 of 4

Covers the application of Markov chains and algorithms for function optimization and graph colorings.

Explores reversibility in Markov chains and its impact on the stationary distribution, highlighting the complexity of non-reversible chains.

Covers the Metropolis-Hastings algorithm and gradient-based approaches for biasing searches towards higher likelihood values.

Explores ergodicity and stationary distribution in Markov chains, emphasizing convergence properties and unique distributions.

Explores Markov chains and algorithm applications, including exact simulation and Propp-Wilson algorithms.

Explores the early days of computer simulation, focusing on the Monte Carlo method and its evolution in scientific research.

Explores NISQ devices and IBM Q, covering noisy quantum circuits, qubit technologies, and quantum algorithm development.

Explores rejection sampling for generating sample values from a target distribution, along with Bayesian inference using MCMC.

Covers the theory of Markov chains and Monte Carlo methods.

Covers applied probability, Markov chains, and stochastic processes, including transition matrices, eigenvalues, and communication classes.