Quantum Markov Chain Monte Carlo Algorithm - IPAM at UCLA

Explore a cutting-edge lecture on the "Quantum" Markov Chain Monte Carlo algorithm presented by Anthony (Chi-Fang) Chen from the California Institute of Technology. Delve into the challenges of preparing ground states and thermal states in quantum simulation algorithms, and discover a novel approach using Markov Chain Monte Carlo (MCMC) to sample quantum Gibbs states. Learn about the first construction of a continuous-time quantum Markov chain with unique properties, including exact quantum detailed balance, efficient Lindbladian simulation, and purification as a "quantum-walk" Hamiltonian. Examine the practical implications for lattice Hamiltonians and gain insights into the ideal quantum counterpart of classical MCMC. Recorded at IPAM's Quantum Algorithms for Scientific Computation Workshop, this 49-minute talk offers a comprehensive perspective on open system thermodynamics and the future of quantum algorithms.