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Towards an Automated Framework for Realizing Quantum Computing Solutions

Nils Quetschlich, Lukas Burgholzer, Robert Wille

TL;DR

The paper addresses the steep entry barrier for applying quantum computing to practical problems by proposing an automated framework that handles problem specification, algorithm selection, encoding, execution, and decoding, returning classical results. It details how algorithm choices (e.g., Grover, QPE, VQAs) guide encoding strategies and demonstrates end-to-end automation through two proof-of-concept implementations for SAT and graph-based optimization within the MQT. The contributions include a structured workflow design, concrete encoding/decoding approaches, and PoCs that illustrate user-friendly, push-button quantum problem solving. This work lays groundwork for scalable design automation in quantum software, aiming to lower the threshold for real-world quantum computing adoption.

Abstract

Quantum computing is fast evolving as a technology due to recent advances in hardware, software, as well as the development of promising applications. To use this technology for solving specific problems, a suitable quantum algorithm has to be determined, the problem has to be encoded in a form suitable for the chosen algorithm, it has to be executed, and the result has to be decoded. To date, each of these tedious and error-prone steps is conducted in a mostly manual fashion. This creates a high entry barrier for using quantum computing -- especially for users with little to no expertise in that domain. In this work, we envision a framework that aims to lower this entry barrier by allowing users to employ quantum computing solutions in an automatic fashion. To this end, interfaces as similar as possible to classical solvers are provided, while the quantum steps of the workflow are shielded from the user as much as possible by a fully automated backend. To demonstrate the feasibility and usability of such a framework, we provide proof-of-concept implementations for two different classes of problems which are publicly available on GitHub (https://github.com/cda-tum/MQTProblemSolver) as part of the Munich Quantum Toolkit (MQT). By this, this work provides the foundation for a low-threshold approach realizing quantum computing solutions with no or only moderate expertise in this technology.

Towards an Automated Framework for Realizing Quantum Computing Solutions

TL;DR

The paper addresses the steep entry barrier for applying quantum computing to practical problems by proposing an automated framework that handles problem specification, algorithm selection, encoding, execution, and decoding, returning classical results. It details how algorithm choices (e.g., Grover, QPE, VQAs) guide encoding strategies and demonstrates end-to-end automation through two proof-of-concept implementations for SAT and graph-based optimization within the MQT. The contributions include a structured workflow design, concrete encoding/decoding approaches, and PoCs that illustrate user-friendly, push-button quantum problem solving. This work lays groundwork for scalable design automation in quantum software, aiming to lower the threshold for real-world quantum computing adoption.

Abstract

Quantum computing is fast evolving as a technology due to recent advances in hardware, software, as well as the development of promising applications. To use this technology for solving specific problems, a suitable quantum algorithm has to be determined, the problem has to be encoded in a form suitable for the chosen algorithm, it has to be executed, and the result has to be decoded. To date, each of these tedious and error-prone steps is conducted in a mostly manual fashion. This creates a high entry barrier for using quantum computing -- especially for users with little to no expertise in that domain. In this work, we envision a framework that aims to lower this entry barrier by allowing users to employ quantum computing solutions in an automatic fashion. To this end, interfaces as similar as possible to classical solvers are provided, while the quantum steps of the workflow are shielded from the user as much as possible by a fully automated backend. To demonstrate the feasibility and usability of such a framework, we provide proof-of-concept implementations for two different classes of problems which are publicly available on GitHub (https://github.com/cda-tum/MQTProblemSolver) as part of the Munich Quantum Toolkit (MQT). By this, this work provides the foundation for a low-threshold approach realizing quantum computing solutions with no or only moderate expertise in this technology.
Paper Structure (15 sections, 4 equations, 4 figures)

This paper contains 15 sections, 4 equations, 4 figures.

Table of Contents

  1. Introduction
  2. Quantum Computing
  3. Realizing Quantum Computing Solutions
  4. Quantum Algorithm Selection
  5. Encoding
  6. Executing
  7. Decoding
  8. Envisioned Framework
  9. Proof-of-Concept Implementations
  10. Satisfiability Problems
  11. Graph-based Optimization Problem: Travelling Salesman Problem
  12. Case Studies: User's Perspective
  13. SAT Problem: Kakuro Riddle
  14. Graph-based Optimization Problem: TSP
  15. Conclusions

Figures (4)

  • Figure 1: Exemplary quantum circuit starting in state $\ket{00}$.
  • Figure 2: Workflow from an initial problem instance to a valid solution using quantum computing.
  • Figure 3: Envisioned framework.
  • Figure 4: Case studies: Using the proposed framework to solve two problem instances of different problem classes.

Theorems & Definitions (6)

  • Example 1
  • Example 2
  • Example 3
  • Example 4
  • Example 5
  • Example 6