ProvideQ: Using Meta-Solving Strategies to enable Quantum Optimization

Quantum algorithms have shown theoretical advantages over their classical counterparts. 

However, we are currently in the early stages of quantum computing, which hinders the practical realization of these advantages. 

The main obstacle to achieving a practical quantum advantage is the availability of stable and scalable quantum processing units. 

Other challenges, such as poor general accessibility and a lack of well-established software engineering, also limit their feasibility. 

We aim to unlock the full potential of quantum algorithms by introducing a new concept called Hybrid Meta-Solving, which combines the advantages of classical and quantum optimization to create hybrid solution strategies and to develop new, powerful solvers for well-known algorithmic problems. 

Meta-solving describes the decomposition of an algorithmic problem into multiple sub-programs, each of which can be solved by multiple solvers. Using expert knowledge, empirical data, and established heuristics, we compare potential classical and quantum solvers for a subroutine with the goal of finding the best solver. 

In this talk, we describe the fundamental concepts of meta-solving and explain how these concepts can be used to create interactive, semi-automated workflows that allow a user to find efficient solutions to a given algorithmic problem, exploiting the potential of quantum computing. 

We present a prototype of the ProvideQ toolbox, which makes Meta-Solving accessible to a broad audience and allows users to exploit the practical benefits of quantum computing in realistic workflows as soon as scalable quantum computers become available.