Thermalization of Quantum Memories - A Tensor Networks Approach

Thermalization of Quantum Memories - A Tensor Networks Approach

Explore the thermalization of quantum memories through a tensor networks approach in this comprehensive lecture. Delve into the commonly held belief that 2D quantum memories cannot be self-correcting when exposed to a thermal bath. Examine recent results confirming this belief for 2D Kitaev's quantum double models. Learn about the proof based on representing the Gibbs state of quantum double models as a PEPS (Projected Entangled Pair State). Understand how this representation allows for rigorous estimation of the spectral gap of the corresponding parent Hamiltonian. Gain insights into the implications of these findings for quantum memory stability and relaxation times as a function of system size.