Our work addresses the Digital Humanities (DH) 2026 conference theme of Engagement by exploring how emerging computational paradigms can foster new forms of interaction between technology, culture, and research. Specifically, it examines how quantum computing, often described as the next frontier of computation, can become an accessible and meaningful tool for DH (Hullurappa 2024).

Its relevance for DH lies in the advantages it offers, including exponential speedups for certain problems, improved accuracy, and enhanced energy efficiency (Nielsen et al. 2010, Preskill 2018, Havlíček et al. 2019). However, these benefits are accompanied by increased conceptual and technical complexity. This is due to the substantial expertise in physics, mathematics, and programming required to engage with quantum computing, which creates a significant barrier for scholars in the humanities (Barzen 2023).

To address this challenge, platforms such as QHAna (Barzen 2022) support the experimental evaluation of classical and quantum algorithms through plugins. While this allows flexibility, extending the platform still requires programming expertise and familiarity with quantum frameworks, limiting accessibility for non-technical users.

One approach for reducing programming effort involves low-code platforms, which enable users to build software visually or textually using pre-built components rather than manually writing code (Prinz et al. 2021). These platforms shift the focus toward conceptual modeling and problem formulation. However, applying this paradigm to quantum computing is non-trivial, as classical abstractions do not directly map to quantum concepts such as superposition or entanglement. This challenge is further compounded by the limitations of existing platforms. Existing quantum low-code platforms are often constrained to a single provider (Amato et al. 2023) or offer limited extensibility (Minerbi 2022), thereby restricting their applicability in humanities contexts.

To overcome these limitations, we develop Qonstruct (Stiliadou et al. 2026b), an open-source quantum low-code platform, as shown in Figure 1. The platform provides a prebuilt set of domain-relevant analytical methods, such as clustering and classification, which are well-established in humanities research (Jockers 2013). At the same time, it enables users to build custom models when required functionality is not available, thereby ensuring flexibility. In addition, Qonstruct provides domain-specific abstractions for humanities users, enabling quantum operations to be expressed using familiar conceptual building blocks and vocabulary (Stiliadou et al., 2026a).

Figure 1: Screenshot of Qonstruct, showing the visual interface for building and configuring quantum algorithms with less coding effort. The use case is depicted in the tool and shown in more detail in Figure 2.

The platform further emphasizes reproducibility and transparency. Once a model is defined, it is automatically transformed into an executable workflow that captures each step of the computation, from data preprocessing and circuit construction to execution and analysis. This workflow not only enhances transparency but also ensures the robustness and reproducibility of experiments, enabling researchers to share and replicate results.

As a proof of concept, we present a use case in computational musicology (see Figure 2), in which clustering algorithms are applied to a subset of the OpenScore Lieder Collection (Gotham 2022) of songs by 19th-century composers, grouping musical pieces based on selected features. These include melodic characteristics (e.g., pitch class distribution, ambitus) and rhythmic properties (e.g., meter, tempo indications). Using XML-based music encoding formats such as MusicXML as input, Qonstruct extracts and preprocesses these features into vector representations. The resulting feature vectors are then processed using a quantum clustering algorithm. While clustering is a well-established task in classical computing, its application in a quantum context is motivated by the potential of quantum computing to handle high-dimensional data. The resulting clusters provide insights into relationships between musical works, thereby supporting new perspectives in cultural analysis. To further support users, we are also exploring the generation of low-code models from natural language specifications.

Figure 2: Example use case in digital musicology, solved using Qonstruct: the user specifies the path to the MusicXML or MEI data, which is then processed: features are automatically extracted, encoded, and clustered using quantum algorithms. Blue and squared rectangles indicate quantum processing, while orange and rounded rectangles indicate classical processing.

Acknowledgments. This work was partially funded by the project QUORUM4H.