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Information and Software Technology

Facilitating usage of quantum–classical optimization software with QHyper library

Tomasz Lamża, Justyna Zawalska, Kacper Jurek, Mariusz Sterzel, Katarzyna Rycerz

Published Sep 2, 2025• 1 citation
Publisher

Abstract

Context: Quantum computing offers promising approaches to solving combinatorial optimization problems. While there are many software frameworks for these approaches, they are developed by different providers, requiring users to re-implement their problems separately in each environment to facilitate comparisons. Additionally, due to the current limitations of quantum hardware, classical solvers are often used as reference methods, further increasing implementation overhead. This lack of integration highlights the need for a unified API that seamlessly connects all approaches, enabling efficient problem formulation and comparison of different methods. Objective: This work aims to provide a research tool with a unified interface for specifying combinatorial optimization problems, selecting different solvers, managing problem hyperparameters, and standardizing the output for effortless analysis and comparison. Methods: We have developed an open-source research tool with a modular architecture that fulfills the aforementioned objective. QHyper’s design provides an independent representation of the problem along with a converter that supports various formulations compatible with available solvers. Among them are the Quantum Approximate Optimization Algorithm implemented in PennyLane, the D-Wave Advantage solver, and the Gurobi optimizer. For optimization, QHyper offers various methods, from local techniques based on gradient descent to custom global Monte Carlo methods for hyperparameter optimization. Results: QHyper’s overhead is within the statistical margin of error compared to the stand-alone use of solvers. The included illustrative example shows how to create a custom problem and easily switch between the chosen solvers. The paper also contains references to real-life scientific use cases for the presented tool. Conclusion: QHyper’s design ensures easy extensibility to new problems, solvers, and optimizers, and has proven its usefulness for several scientific use cases mentioned in the paper. With simple configuration options and easy management, such as through Jupyter Notebooks, this library can be useful for practitioners, engineers, and academics working on combinatorial optimization research.

DOI
10.1016/j.infsof.2025.107861

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Context: Quantum computing offers promising approaches to solving combinatorial optimization problems. While there are many software frameworks for these approaches, they are developed by different providers, requiring users to re-implement their problems separately in each environment to facilitate comparisons. Additionally, due to the current limitations of quantum hardware, classical solvers are often used as reference methods, further increasing implementation overhead. This lack of integration highlights the need for a unified API that seamlessly connects all approaches, enabling efficient problem formulation and comparison of different methods. Objective: This work aims to provide a research tool with a unified interface for specifying combinatorial optimization problems, selecting different solvers, managing problem hyperparameters, and standardizing the output for effortless analysis and comparison. Methods: We have developed an open-source research tool with a modular architecture that fulfills the aforementioned objective. QHyper’s design provides an independent representation of the problem along with a converter that supports various formulations compatible with available solvers. Among them are the Quantum Approximate Optimization Algorithm implemented in PennyLane, the D-Wave Advantage solver, and the Gurobi optimizer. For optimization, QHyper offers various methods, from local techniques based on gradient descent to custom global Monte Carlo methods for hyperparameter optimization. Results: QHyper’s overhead is within the statistical margin of error compared to the stand-alone use of solvers. The included illustrative example shows how to create a custom problem and easily switch between the chosen solvers. The paper also contains references to real-life scientific use cases for the presented tool. Conclusion: QHyper’s design ensures easy extensibility to new problems, solvers, and optimizers, and has proven its usefulness for several scientific use cases mentioned in the paper. With simple configuration options and easy management, such as through Jupyter Notebooks, this library can be useful for practitioners, engineers, and academics working on combinatorial optimization research.
Quantum computing has the potential to surpass the capabilities of current classical computers when solving complex problems. Combinatorial optimization has emerged as one of the key target areas for quantum computers as problems found in this field play a critical role in many different industrial application sectors (e.g., enhancing manufacturing operations or improving decision processes). Currently, there are different types of high-performance optimization software (e.g., ILOG CPLEX and Gurobi) that support engineers and scientists in solving optimization problems using classical computers. In order to utilize quantum resources, users require domain-specific knowledge of quantum algorithms, SDKs and libraries, which can be a limiting factor for any practitioner who wants to integrate this technology into their workflows. Our goal is to add software infrastructure to a classical optimization package so that application developers can interface with quantum platforms readily when setting up their workflows. This paper presents a tool for the seamless utilization of quantum resources through a classical interface. Our approach consists of a Python library extension that provides a backend to facilitate access to multiple quantum providers. Our pipeline enables optimization software developers to experiment with quantum resources selectively and assess performance improvements of hybrid quantum-classical optimization solutions.
Quantum computing has the potential to surpass the capabilities of current classical computers when solving complex problems. Combinatorial optimization has emerged as a pivotal target area for quantum computers, as problems in this field are renowned for their complexity and resource-intensive nature. Moreover, these challenges play a critical role in various industrial sectors, including logistics, manufacturing, and finance. This thesis explores the integration of quantum computation into classical software tools as a means to potentially address combinatorial optimization problems more efficiently and effectively. This work introduces QPLEX, a Python software library that enables practitioners and researchers to implement the general mathematical formulation of a given combinatorial optimization problem once and execute it seamlessly on multiple quantum devices using various quantum algorithms. This software solution automatically adapts a general optimization model to the specific instructions utilized by the target quantum device’s SDK. It offers a versatile execution workflow capable of running gate-based hybrid quantum-classical algorithms for combinatorial optimization in a platform-agnostic manner. This approach reduces the programming overhead required for modeling and experimenting with combinatorial optimization solutions. Within this manuscript, we address and introduce the various aspects associated with the development of QPLEX in a clear and comprehensive manner. These aspects encompass the quantum algorithms and quantum hardware available in the library, along with QPLEX’s system design and implementation. Additionally, we provide a guide on how to use the library and conduct a thorough evaluation of the software solution within a specific use case as part of this thesis.
Combinatorial optimization (CO) problems are computationally challenging as evidenced in various industry and research domains. With recent advances in quantum computing hardware and algorithms, such problems represent an excellent case study for these technologies. Nevertheless, current software tools for CO lack platform-agnostic abstractions to enable researchers and practitioners to utilize quantum resources effectively. This thesis aims to validate and extend the QPLEX Python library, a platform-agnostic CO package built on DOcplex which integrates execution across multiple quantum providers using various algorithms. We focus on two key software quality attributes: completeness, examining the quantum providers QPLEX supports to look for features that could be added to our library, enhancing its capabilities for handling CO problems; and extensibility, making the library more adaptable for future expansions. We first compile a high-level workflow for solving CO problems to ensure that our elicited software requirements align with the actual process practitioners follow when solving these problems. Subsequently, we evaluate QPLEX through a comprehensive analysis of its completeness by comparing features against alternative solutions including platform-specific SDKs, and its extensibility by examining how easily new features can be integrated without disrupting existing functionality. Based on the identified functional and non-functional requirements, we design and implement several extensions to QPLEX, including support for Qiskit Runtime Sessions, integration with D-Wave's quantum solvers and implementation of the QAOAnsatz algorithm. Furthermore, we enhance the extensibility of the library through comprehensive documentation, automated testing, and CI/CD pipelines to ensure smooth integration of future open-source contributions. Validation results demonstrate that these enhancements successfully extend QPLEX's capabilities for solving CO problems using quantum resources, providing a more comprehensive suite of features for quantum-based CO while establishing robust foundations for future development. This work contributes to the evolving field of quantum software engineering by advancing an abstraction layer that shields practitioners from low-level quantum details, allowing them to focus on problem formulation. As quantum hardware and algorithms continue to advance, such platform-agnostic libraries will play a crucial role in broadening quantum computing adoption, enabling domain experts to leverage quantum resources without requiring deep quantum computing knowledge.
Hybrid solvers for combinatorial optimization problems combine the advantages of classical and quantum computing to overcome difficult computational challenges. Although their theoretical performance seems promising, their practical applicability is challenging due to the lack of a technological stack that can seamlessly integrate quantum solutions with existing classical optimization frameworks. We tackle this challenge by introducing the ProvideQ toolbox, a software tool that enables users to easily adapt and configure hybrid solvers via Meta-Solver strategies. A Meta-Solver strategy implements decomposition techniques, which splits problems into classical and quantum subroutines. The ProvideQ toolbox enables the interactive creation of such decompositions via a Meta-Solver configuration tool. It combines well-established classical optimization techniques with quantum circuits that are seamlessly executable on multiple backends. This paper introduces the technical details of the ProvideQ toolbox, explains its architecture, and demonstrates possible applications for several real-world use cases. Our proof of concept shows that Meta-Solver strategies already enable the application of quantum subroutines today, however, more sophisticated hardware is required to make their performance competitive.
Solving discrete optimization problems is one of the most promising use cases on quantum computers with tremendous potential both in research and industry. We present two libraries quark and quapps which provide an easily usable, modular workflow to encode discrete optimization problems and interface them to both classical and quantum hardware. We provide a collection of already implemented optimization problems which can be easily adapted by the user and run on different solvers. From this we derive an additional instance data set that can directly be used to benchmark and test quantum hardware. In conjunction both libraries and the instance data set enable both expert and non-expert users to easily implement optimization problems, run them on classical and quantum machines and simplify benchmarking on quantum machines.
Optimizing objective functions stands to benefit significantly from leveraging quantum computers, promising enhanced solution quality across various application domains in the future. However, harnessing the potential of quantum solvers necessitates formulating problems according to the Quadratic Unconstrained Binary Optimization (QUBO) model, demanding significant expertise in quantum computation and QUBO formulations. This expertise barrier limits access to quantum solutions. Fortunately, automating the conversion of conventional optimization problems into QUBO formulations presents a solution for promoting accessibility to quantum solvers. This article addresses the unmet need for a comprehensive automatic framework to assist users in utilizing quantum solvers for optimization tasks while preserving interfaces that closely resemble conventional optimization practices. The framework prompts users to specify variables, optimization criteria, as well as validity constraints and, afterwards, allows them to choose the desired solver. Subsequently, it automatically transforms the problem description into a format compatible with the chosen solver and provides the resulting solution. Additionally, the framework offers instruments for analyzing solution validity and quality. Comparative analysis against existing libraries and tools in the literature highlights the comprehensive nature of the proposed framework. Two use cases (the knapsack problem and linear regression) are considered to show the completeness and efficiency of the framework in real-world applications. Finally, the proposed framework represents a significant advancement towards automating quantum computing solutions and widening access to quantum optimization for a broader range of users. The framework is publicly available on GitHub (https://github.com/cda-tum/mqt-qao) as part of the Munich Quantum Toolkit (MQT).