In this master thesis, a 2D device simulator run on a hybrid classical-quantum computer was developed. The simulator was developed to treat statistical quantum effects such as quantum tunneling and quantum confinement in nanoscale transistors. The simulation scheme is based on a self-consistent solution of the coupled non-linear 2D SchrödingerPoisson equations. The Open Boundary Condition (OBC) of the Schrödinger equation, which allows for electrons to pass through the device between the leads (source and drain), are modeled with the QuantumTransmitting Boundary Method (QTBM). The differential equations are discretized with the finite-element method, using rectangular mesh elements. The self-consistent loop is a very time-consuming process, mainly due to the solution of the discretized OBC Schrödinger equation. To accelerate the solution time of the Schrödinger equation, a quantum assisted domain decomposition method is implemented. The domain decomposition method of choice is the Block Cyclic Reduction (BCR) method. The BCR method is at least 15 times faster (CPU time) than solving the whole linear system of equations with the Python solver numpy.linalg.solve, based on the LAPACK routine _gesv. In the project, we also propose an alternative approach of the BCR method called the "extra layer BCR" that shows an improved accuracy for certain types of solutions. In a quantum assisted version, the matrix inverse solver as a step in the BCR method was computed on the D-Wave quantum annealer chip ADVANTAGE_SYSTEM4.1 [4]. Two alternative methods to solve the matrix inverses on a quantum annealer were compared. One is called the "unit vector" approach, based on work by Rogers and Singleton [5], and the other is called the "whole matrix" approach which was developed in the thesis. Because of the limited amount of qubits available on the quantum annealer, the "unit vector" approach was more suitable for adaption in the BCR method. Comparing the quantum annealer to the Python inverse solver numpy.linalg.inv, also based on LAPACK, it was found that an accurate solution can be achieved, but the simulation time (CPU time) is at best 500 times slower than numpy.linalg.inv.