Quantum Dot Phase Transition Simulation with Hybrid Quantum Annealing via Metropolis-Adjusted Stochastic Gradient Langevin Dynamics

Abstract

We report a hybrid quantum-classical simulation approach for simulating the optical phase transition observed experimentally in the ultrahigh-density type-II InAs quantum dot array. A hybrid simulation scheme, which contains stochastic gradient Langevin dynamics (a well-known Bayesian machine learning algorithm for big data) along with adiabatic quantum annealing, is developed to reproduce the experimentally observed phase transition. By implementing the simulation scheme into a quantum circuit, we successfully verified the phase transition observed in the experiment. Our work demonstrates for the first time the feasibility of hybridizing quantum computation with classical Langevin dynamics for the analysis of carrier dynamics and quantum phase transition of the quantum dot.