Description
Machine Learning and Quantum Computing
I will explain the Wang-Landau (WL) Monte Carlo method, comparing it with the traditional Metropolis algorithm. The WL method is particularly useful for calculating the density of states, from which we can derive important thermodynamic quantities like energy, entropy, specific heat, and free energy.
As a demonstration, I’ll examine the Ising model with both nearest-neighbor (J₁) and...
Fullerene, primarily known as C₆₀, presents intriguing spectral properties when ^12C atoms are substituted with ^13C isotopes. This study focuses on the specific configurations of ^13C substitution in C₆₀, particularly cases where one ^12C atom is replaced by ^13C at either a pentagonal or hexagonal position. While isolated substitutions offer limited configurations, increasing the number of...
I will introduce the recently developed quantum eigensolver algorithm based on optimized binary configurations measured by quantum annealing of D-Wave Advantage. The approach provides all energy specrum of L by L matrix with the computational cost of a linear increase in L, unlike exact diagonalization with L^3 iterations on classic computer. Using the method, I examined the energy dispersion...
We study the deuteron model, consisting of a proton and a neutron, by employing a Quantum Annealer to compute the binding energy. Quantum Annealing, as performed by D-wave's quantum computers, is an advanced quantum computing technique designed to solve complex optimization problems. It leverages quantum fluctuations to efficiently identify the global minimum of a specific function. In our...
