Model-Driven Optimization for Quantum Program Synthesis with MOMoT

In the realm of classical software engineering, model-driven optimization has been widely used for different problems such as (re)modularization of software systems. In this paper, we investigate how techniques from model-driven optimization can be applied in the context of quantum software engineering. In quantum computing, creating executable quantum programs is a highly non-trivial task which requires significant expert knowledge in quantum information theory and linear algebra. Although different approaches for automated quantum program synthesis exist—e.g., based on reinforcement learning and genetic programming—these approaches represent tailor-made solutions requiring dedicated encodings for quantum programs. This paper applies the existing model-driven optimization approach MOMoT to the problem of quantum program synthesis. We present the resulting platform for experimenting with quantum program synthesis and present a concrete demonstration for a well-known quantum algorithm.