Quantum Computing in Data Science and STEM Education: Mapping Academic Trends and Analyzing Practical Tools

Abstract

Quantum computing is emerging as a key enabler of digital transformation in data science and STEM education. This study investigates how quantum computing can be meaningfully integrated into higher education by combining a dual approach: a structured assessment of the specialized literature and a practical evaluation of educational tools. First, a science mapping study based on 281 peer-reviewed publications indexed in Scopus (2015–2024) identifies growth trends, thematic clusters, and international collaboration networks at the intersection of quantum computing, data science, and education. Second, a comparative analysis of widely used educational platforms—such as Qiskit, Quantum Inspire, QuTiP, and Amazon Braket—is conducted using pedagogical criteria including accessibility, usability, and curriculum integration. The results highlight a growing convergence between quantum technologies, artificial intelligence, and data-driven learning. A strategic framework and roadmap are proposed to support the gradual and scalable adoption of quantum literacy in university-level STEM programs.

1. Introduction

In recent years, quantum computing has evolved from a theoretical domain within physics into an emerging technology with transformative potential across various strategic sectors. Its capacity to reshape information processing is beginning to materialize in concrete applications in fields such as artificial intelligence, optimization, and data science [1]. This technological evolution has been documented in specialized literature reviews that highlight the exponential growth of scientific output related to quantum computing and its cross-sector applicability [2].

In the context of quantum information technologies, the concept of NISQ (Noisy Intermediate-Scale Quantum, or sistemas cuánticos intermedios ruidosos in Spanish), proposed by Preskill, marks a turning point between technical feasibility and experimental applications [3]. Though still in its consolidation phase, this scenario has sparked renewed interest in incorporating quantum computing into educational environments, especially in higher education programs linked to STEM disciplines.

However, the high conceptual complexity of quantum computing, combined with the scarcity of accessible materials and trained instructors, continues to hinder its systematic integration into curricula. The academic literature identifies this educational gap as one of the main challenges in preparing future professionals for the quantum-digital ecosystem [4].

Simultaneously, data science has become a key competency in multiple sectors, driving a profound transformation in educational approaches focused on analysis, modeling, and decision-making. Within this context, quantum machine learning has emerged as a high-potential field that seeks to leverage quantum resources to optimize intensive computational tasks [5]. This development has amplified the demand for professionals skilled in applying emerging technologies—such as quantum computing—to real-world data analysis contexts. In response, universities and research centers around the world have begun to design specialized training programs that combine skills in data science, quantum programming, and complex systems modeling [6].

Various institutional initiatives—both academic and industrial—such as IBM Quantum Education, Microsoft Quantum Programs, and the Quantum Education Coalitions reflect this growing movement toward the integration of quantum competencies into STEM and data science education.

This paradigm shift highlights the urgency of bridging the gap between theoretical advances in quantum computing and its practical teaching in undergraduate and postgraduate programs. The objective is not merely to train specialized researchers but to equip a new generation of professionals who, from the early stages of their university education, gain familiarity with quantum concepts applied to real-world problems in data analysis, simulation, and optimization [7].

This convergence creates the need to train hybrid profiles capable of integrating knowledge in quantum physics, programming, and data analytics. Both academic and industrial sectors recognize the urgency of developing talent with transversal competencies to address these emerging technological challenges [8]. This study aims to contribute to this goal by offering evidence and tools to support curriculum design and educational innovation.

Although the academic literature has begun to explore these connections, few studies combine a quantitative analysis of scientific knowledge with a comparative assessment of pedagogical tools for teaching quantum computing. Even fewer adopt an integrated perspective that articulates scientific trend analysis with the practical evaluation of educational resources, a deliberate approach taken by this study [9].

This paper proposes an integrated framework with three main objectives: (i) to analyze the evolution and thematic structure of scientific production on quantum computing in the fields of data science and education using bibliometric techniques; (ii) to identify documented experiences with the use of quantum platforms for educational purposes; and (iii) to comparatively evaluate various simulation tools and access to quantum hardware from a pedagogical perspective.

The article is organized into five sections. Following this introduction, Section 2 presents the theoretical and conceptual framework, followed by a methodological and analytical characterization of the main scientific trends at the intersection of quantum computing, data science, and higher education (Section 3). Section 4 presents the results of the analysis, including a comparative study of educational platforms. Section 5 offers a critical discussion of the findings and outlines future research directions. Finally, Section 6 summarizes the conclusions and offers perspectives for future investigations and institutional transformation.

Although the relevance of quantum technologies in STEM curricula is widely acknowledged, the pedagogical infrastructure necessary to support this transition remains underexplored. This study offers a dual contribution: a systematic bibliometric mapping of research trends and a comparative analysis of quantum education tools. Together, these elements support the development of an inclusive governance framework for integrating quantum technologies in higher education systems.

2. Background and Theoretical Framework

The intersection between quantum computing, data science, and higher education does not merely represent a convergence of emerging disciplines but rather an opportunity space that redefines the foundations of knowledge in the 21st century. This section aims to establish the conceptual coordinates that allow this articulation to be understood as a transformative phenomenon from both technological and pedagogical perspectives.

Rather than introducing an incremental improvement, quantum computing offers a completely different way of conceiving and processing information. Unlike classical systems, it operates on principles such as superposition—which allows qubits to exist in multiple states simultaneously—and entanglement, a deeply counterintuitive property whereby the state of one particle instantaneously affects the other, regardless of distance. These principles challenge the foundations of classical computation and prompt a rethinking of what it means to “solve a problem” using computational methods [10].

In parallel, data science has evolved into a key discipline at the intersection of statistics, programming, and predictive modeling, enabling new ways of interpreting reality through data. Although its curricular integration has gained traction in numerous academic programs, there are still challenges related to the definition of core content and the pedagogical orientation of instruction [11].

The emergence of quantum machine learning further intensifies this complexity by simultaneously demanding advanced skills in data science—such as predictive modeling and machine learning—and in the fundamentals of quantum mechanics. This remains a nascent field, but one with structural implications for curriculum design in the coming years [12]. Its transformative potential is also evident in recent developments on integrated photonics platforms, where the convergence of quantum computing and machine learning is being explored from both experimental and educational standpoints [13].

From an institutional perspective, higher education faces the challenge of incorporating these technologies without losing sight of its critical and formative mission. Although there are innovative educational initiatives, many remain isolated, fragmented, or overly technical, making it difficult to scale them structurally [14].

The scientific literature supports this observation. Several studies point to a lack of effective articulation between technological advances, methodological developments, and their actual educational application. In the case of data science, diverse instructional approaches persist, hindering coherent curricular implementation at the international level [15]. In turn, quantum computing education is often confined to highly specialized courses, lacking transversal integration that would facilitate broader institutional adoption [16].

Against this backdrop, there is a pressing need to construct an integrative framework capable of identifying substantive relationships, emerging areas, and critical gaps. This study responds to that need through a systematic analysis of the literature, aimed at providing an articulated perspective that can serve as a foundation for curriculum transformation aligned with the educational demands of the quantum-digital era.

3. Methodology and Trends Analysis

This study proposes a dual approach that combines bibliometric trend analysis with a pedagogical review of quantum computing tools. The goal is not only to map the academic landscape but also to reflect on how scientific production connects to real educational practices. Such a combined perspective is especially relevant in emerging domains where research maturity and curriculum integration do not always evolve in parallel [17].

Rather than isolating research metrics from pedagogical needs, this methodology brings them together. It responds to the growing demand for integrated frameworks that can track both the structural dynamics of scholarly output and its capacity to inform teaching and training in complex technological domains [18].

3.1. Research Objectives

The research is organized around three guiding objectives:

• To trace the evolution and thematic orientation of academic publications at the intersection of quantum computing, data science, and education.
• To identify structural and institutional dynamics in the literature, including thematic clusters and networks of international collaboration.
• To examine the degree of alignment between scientific trends and the development of pedagogical tools, highlighting opportunities and gaps in their translation into classroom practice.

These objectives are aligned with recent efforts to bridge academic knowledge production and educational innovation, particularly in areas where technological literacy is becoming a strategic priority [19].

3.2. Data Collection and Scope

To fulfill these goals, we constructed a bibliographic dataset using the Scopus database, recognized for its interdisciplinary reach, metadata robustness, and compatibility with visualization tools. Scopus is especially suitable for tracking research activity in hybrid domains like quantum education, which draw from both technological and pedagogical scholarship [20].

The data extraction was carried out in April 2025 using the query:

TITLE-ABS-KEY (“quantum computing” AND “education”) AND PUBYEAR > 2014 AND PUBYEAR < 2025

This query yielded publications from 2015 to 2024, a period marked by a steady increase in educational interest in quantum technologies and the emergence of various instructional initiatives [21].

Nevertheless, relying exclusively on the indexed literature presents an inherent limitation. Many valuable materials—such as institutional reports, white papers, or local educational pilots—often fall outside conventional databases. Recent studies have emphasized the importance of complementing indexed sources with the gray literature to capture a fuller picture of educational transformation, especially in underrepresented regions [22].

Although publication activity in this field remained limited between 2015 and 2024, these early years were included in the analysis to provide a full view of the field’s emergence and to contextualize its exponential growth in subsequent years. Rather than distorting the results, this temporal scope allows us to highlight the acceleration of interest, the initial institutional actors, and the absence of pedagogical focus during the first phase. In line with bibliometric practice in emerging domains, capturing this formative stage is essential to understand the pace and direction of development [23].

3.3. Data Curation and Analytical Framework

Once retrieved, the dataset underwent a multistage refinement process to ensure conceptual consistency and analytical relevance:

• Deduplication was performed based on DOIs and metadata.
• Manual screening excluded items lacking meaningful connections to at least two of the three domains: quantum computing, data science, and education.
• Inclusion criteria involved peer-reviewed articles in English, with full-text access and clear links to higher education or training programs.
• Exclusion criteria ruled out abstract-only contributions, purely technical papers without pedagogical intent, and non-scientific communications.

The final corpus consisted of 281 documents, which were analyzed using VOSviewer (https://www.vosviewer.com/), a tool widely used for mapping the scientific literature. It allows the construction of visual networks based on keyword co-occurrence, authorship links, and institutional affiliations—essential to understanding both the structure and evolution of a field [24].

The methodology is articulated in five sequential phases, illustrated in Figure 1:

• Definition of research objectives.
• Data retrieval from Scopus.
• Filtering and validation of relevant documents.
• Extraction of networks and thematic clusters.
• Interpretation of structural and temporal patterns.

Figure 1. Methodological framework for bibliometric and pedagogical analysis in quantum education research. Source: authors’ own elaboration.

Figure 1. Methodological framework for bibliometric and pedagogical analysis in quantum education research. Source: authors’ own elaboration.

This structured model ensures both traceability and replicability while enabling comparisons with future studies. Moreover, it provides a coherent analytical base for interpreting how academic production aligns (or fails to align) with the needs of quantum computing education.

Methodologically, this study follows a data mining approach by applying text mining techniques (e.g., co-occurrence analysis), unsupervised clustering algorithms, and metadata pattern extraction to identify latent structures within the academic corpus. These methods support the discovery of meaningful trends and thematic relationships in line with standard practices in data-driven knowledge discovery.

4. Practical Educational Tools and Pedagogical Alignment

The bibliometric patterns described in the previous section reveal an expanding body of research concerned with the intersection of quantum technologies and education. However, to assess the real-world educational impact of these developments, it is essential to move beyond the academic literature and examine the specific tools currently available to instructors and students in university-level programs.

This section presents a comparative evaluation of key educational platforms that incorporate quantum computing into STEM learning environments. The analysis considers not only technical capabilities but also instructional value, curricular integration, and accessibility across diverse educational contexts.

Platforms were selected using the following criteria: (i) provision of a simulation or execution environment for quantum algorithms; (ii) documentation in the peer-reviewed literature or institutional repositories; (iii) inclusion of teaching-oriented resources; and (iv) accessibility for educators or students through open or academic licenses.

The selected tools include institutional initiatives—such as Qiskit (IBM), Amazon Braket, and Azure Quantum (Microsoft)—as well as academic platforms with demonstrated relevance in educational settings. Rather than focusing solely on features, this analysis explores how these tools are being implemented, what kinds of learning outcomes they support, and how they address current challenges in curriculum design and teacher training.

While the evaluation is grounded in publicly available documentation and secondary sources, it aims to synthesize insights from real classroom experiences as reported in case studies and implementation reports. This perspective is essential to understanding whether recent scientific advances are truly reaching learners—or remaining confined to research laboratories.

4.1. Bibliometric Trends and Patterns in Quantum Education Research

The bibliometric analysis conducted for this study reveals a rapidly expanding research domain that is still undergoing structural consolidation. The volume of scientific output indexed in Scopus between 2015 and 2024 shows a marked acceleration in recent years, particularly from 2020 onward. This surge aligns with global policy strategies aimed at embedding quantum technologies within educational and industrial innovation agendas, especially in STEM fields [25].

As illustrated in Figure 2, publication rates remained modest until 2019, after which a notable growth phase began. This shift coincides with international efforts to foster quantum literacy and to scale national investment in quantum infrastructure and curricula [26].

The distribution of document types supports the hypothesis that this is still an emergent area. As shown in Figure 3, nearly half of the output comprises conference proceedings (47.3%), while journal articles represent just over 30%. Reviews, book chapters, and other formats are marginally represented. This suggests a research ecosystem in early development stages, where empirical validations and mature educational assessments are still rare [27].

From a disciplinary perspective, the corpus is overwhelmingly dominated by technical domains. As depicted in Figure 4, computer science and engineering together account for over half of the publications. In contrast, contributions from education, mathematics, or social sciences are significantly less frequent. This imbalance highlights a persisting disconnect between technological innovation and its didactic translation in formal educational settings [28].

In terms of thematic scope, the field spans diverse domains—from quantum physics and computing to educational technology. However, explicit discussions on pedagogical frameworks, learning outcomes, or instructional strategies remain scarce. This evidences an ongoing gap between the rapid evolution of quantum tools and their structured inclusion in curricula [29].

The keyword co-occurrence map presented in Figure 5 confirms this transitional character. Central clusters such as “quantum computing”, “quantum algorithms”, and “STEM education” are complemented by peripheral nodes like “teacher training”, “curriculum design”, and “coding skills”. These patterns indicate an emerging pedagogical dimension, though still marginal in comparison with technical emphases [30].

The geographical distribution of contributions, shown in Figure 6, reveals significant asymmetries. The United States, China, Germany, the Netherlands, and the United Kingdom lead the publication landscape. These nations are not only at the forefront of quantum technological development but also invest systematically in integrating quantum competencies into higher education [31].

Scientific authorship is predominantly collaborative, though with low recurrence of lead contributors. As visualized in Figure 7, the co-authorship network remains fragmented, lacking robust inter-institutional clusters. The presence of many isolated nodes indicates that contributions are often isolated initiatives rather than components of sustained educational research programs [32].

This structural fragmentation underscores the importance of establishing long-term pedagogical networks. Without solid institutional alliances and shared research agendas, advancements in quantum science risk remaining confined to technical domains—failing to reach students, educators, or the broader educational ecosystem.

4.2. Implementation and Pedagogical Suitability of Quantum Education Platforms

In parallel with the growth of scientific output in quantum computing, the integration of practical tools into university education has become a priority for institutions and developers alike. However, not all platforms are equally suited to pedagogical contexts. The educational effectiveness of quantum platforms depends not only on their technical capabilities but also on their didactic design, user accessibility, and adaptability to curricular structures.

This section presents a comparative overview of the most widely adopted platforms for quantum instruction, based on documented use cases, accessibility, and pedagogical integration. The selected tools—IBM Qiskit, Amazon Braket, Azure Quantum, and Qinterpreter—represent both corporate and academic initiatives, reflecting diverse strategic approaches to quantum education. All platforms were selected according to the following criteria: (i) availability of educational materials, (ii) active use in academic environments, and (iii) access to simulators or real quantum devices.

Table 1. Pedagogical and technical comparison of selected quantum computing education platforms. Source: authors’ own elaboration based on platform documentation and academic case studies (2020–2025).

Platform/Provider	Access Type	Programming Interface/ Language	Simulation Type	Pedagogical Materials	Educational Focus	User Friendliness
Qiskit (IBM)	Free/Open Access	Python 3.10 (Qiskit 1.0.2)	State-vector, QASM simulator	Structured tutorials, full curriculum	High (University adoption)	High (Jupyter notebooks, tutorials)
Amazon Braket (Amazon Web Services)	Freemium (Edu support)	Python 3.10 (AWS SDK 1.76.0)	IonQ, Rigetti, OQC	Limited (developer-focused)	Medium	Medium (requires cloud setup)
Azure Quantum (Microsoft)	Free for Education	Q# (QDK 0.27), Python 3.9	IonQ, Quantinuum, etc.	Moderate (documentation and basic samples)	Medium	Medium (documentation available)
Qinterpreter (Multi-institutional consortium)	Open Source	Python 2.8 (Qinterpreter 2.3.1)	Cross-platform interpreter	Tutorials, step-by-step guides	Medium to High	Medium (requires prior programming skills)

offers a structured comparison of these tools based on seven dimensions relevant to teaching: provider identity, access type, programming language, pedagogical materials, simulation scope, and level of educational integration.

These tools exhibit varying degrees of alignment with educational needs. For instance, Qiskit stands out for its open-access curriculum, comprehensive documentation, and widespread integration into university courses worldwide. It provides a consistent user experience through Jupyter notebooks and is backed by IBM’s global initiatives to promote quantum literacy [33].

In contrast, Amazon Braket offers powerful cloud-based backends but a less pedagogically oriented interface. Its documentation is more developer-centric, requiring prior knowledge of infrastructure setup and SDK integration. Although efforts to provide educational support are growing, its adoption in formal programs remains moderate [34].

Azure Quantum represents a hybrid approach, combining Q# and Python in a modular ecosystem supported by Microsoft and various quantum providers. Its educational content is relatively limited but accessible, and its compatibility with other platforms positions it as a flexible option for advanced learners [35].

Qinterpreter, developed by a consortium of academic and research institutions, was designed with accessibility in mind. Its open-source nature, Python-based interface, and step-by-step tutorials make it an effective bridge between theoretical instruction and algorithm implementation. Several case studies report successful integration of Qinterpreter into graduate courses and online modules focused on quantum literacy [36].

Collectively, these tools illustrate the ongoing effort to translate quantum complexity into pedagogical value. Nevertheless, recent analyses reveal a gap between technological innovation and its didactic translation. Many platforms were not originally conceived for education, which complicates their adoption in programs with limited digital resources or high student diversity [37]. A recent study highlights the importance of combining interactive tools and guided exploration to foster conceptual understanding of quantum systems at early academic stages [38].

Finally, the diversification of educational platforms must be accompanied by sustained investment in teacher training and curricular innovation. Without coordinated efforts in these areas, even the most advanced tools risk becoming underused or inaccessible to the majority of university students [39].

5. Strategic Discussion: Integrating Quantum Technologies into Education Systems

The analysis conducted in this study reveals a rapidly expanding technological field that still lacks a consolidated pedagogical structure and clearly defined institutional integration strategies. Although tools such as Qiskit and Qinterpreter have gained traction in academic environments, their adoption remains uneven across programs and regions, particularly in contexts with limited technical capacity or inadequate teacher training. This raises a crucial question: how can we bridge the gap between technical sophistication and effective educational inclusion?

5.1. Institutional and Policy Implications

Beyond technical developments, the sustainable integration of quantum technologies into higher education requires an institutional architecture that supports this transition. Initiatives such as Quantum Education for Everyone (QEd), promoted by the European Quantum Flagship, and the QISE-NET network funded by the U.S. National Science Foundation, demonstrate that alliances between governments, industry, and universities can create fertile ground for educational innovation [40]. However, such experiences remain limited, difficult to scale, and often depend on specific institutional leadership.

Public policy thus plays a key role. The development of curricular standards that include transversal quantum content, along with targeted teacher training programs, can make a substantial difference. Platform availability alone is not enough to ensure structural integration or inclusive adoption.

5.2. Structural Limitations and Academic Fragmentation

The fragmented co-authorship patterns observed in the bibliometric analysis reflect the lack of stable networks and shared agendas in quantum education. This disaggregation restricts the scalability of good practices and limits knowledge transfer between institutions. Moreover, many initiatives are short-lived or lack systematic follow-up, reducing their educational impact.

Structurally, there is also a limited presence of pedagogical disciplines in the scientific production of the field. Most contributions originate from technical domains such as computer science and engineering, generating a technocentric bias and hindering the development of integrative and culturally responsive educational frameworks.

5.3. Scalability and Educational Equity Factors

A major challenge is ensuring that quantum literacy does not become a skill exclusive to elite universities or countries with high technological investment. To achieve genuine equity, it is essential to develop low-cost tools that are multilingual, accessible, and adaptable to diverse educational and cultural levels. The experience of platforms like Qinterpreter, developed by open academic consortia, offers a promising model [41].

Furthermore, teacher training must be considered a strategic priority. Without educators trained in quantum fundamentals and active learning methodologies, even the most advanced platforms risk being underused. Joint certification initiatives, hybrid courses, and modular programs are needed to enable teachers to integrate these competencies without requiring full technical specialization.

Recent experiences with interactive assignments designed to support conceptual understanding in quantum mechanics show that even modest interventions can enhance student engagement when paired with tailored digital content [42].

5.4. Toward a Global Strategic Framework

In summary, the strategic integration of quantum technologies in higher education requires more than technical readiness: it demands political commitment, curriculum design expertise, and sustained investment in educator preparation. As illustrated in Figure 8, the disparities between quantum education platforms reflect divergent approaches regarding accessibility, pedagogical orientation, and alignment with learning outcomes. This visual comparison clearly synthesizes each tool’s relative strengths and limitations based on key criteria such as hardware access, open-source availability, learning resources, and user-friendliness. reflect divergent approaches that, if not aligned with institutional priorities, may exacerbate existing educational inequalities.

We propose moving toward a quantum education governance framework that fosters:

• International networks for co-developing curricula.
• Open platforms with scalable pedagogical content.
• Teacher training programs endorsed by inter-university consortia.
• Quality indicators for assessing the educational effectiveness of quantum tools.

Only through a systemic and integrative approach can quantum literacy become a foundational competence in twenty-first-century STEM programs.

Far from being a niche concern, the integration of quantum education frameworks constitutes a critical challenge for the democratization of scientific literacy in the coming decades.

6. Conclusions and Future Prospects for Quantum Education

The rise of quantum computing is not only reshaping technological frontiers but also challenging educational systems to keep pace with an increasingly complex scientific paradigm. This study has highlighted two key dimensions of that transformation: the structural evolution of academic research in quantum education and the practical deployment of tools aimed at fostering quantum literacy at the university level.

From a bibliometric standpoint, the field shows clear signs of growth but remains fragmented, with limited focus on pedagogical outcomes or curricular integration. While scientific production is increasing, much of it remains concentrated in technical domains, with minimal participation from education-focused researchers or institutions. This disciplinary gap raises critical concerns about the future scalability of quantum learning in diverse academic contexts.

The analysis of practical educational tools further confirms this fragmentation. Although several platforms—such as IBM Qiskit and Qinterpreter—offer robust pedagogical frameworks and accessible interfaces, others remain anchored in developer-centric logics, which hinders adoption in undergraduate and non-specialist programs. Moreover, the lack of alignment between platform capabilities and curricular needs underscores the urgent demand for educational design principles tailored to quantum instruction.

From a policy and institutional standpoint, the findings suggest several urgent lines of action: (i) the creation of interdisciplinary research networks that bridge quantum science and education; (ii) the systematic training of educators in quantum concepts and tools; (iii) the integration of quantum topics into core STEM curricula, particularly in physics, mathematics, and computer science; and (iv) the development of quality assurance mechanisms to assess the didactic effectiveness of quantum learning platforms.

The following roadmap (Figure 9) outlines the key stages and institutional actions required for the effective integration of quantum education into higher education systems.

This strategic vision emphasizes the need for a systemic and collaborative effort involving academia, government, and industry. Rather than relying on isolated initiatives, the long-term success of quantum education will depend on sustained investments in teacher preparation, curricular reform, and cross-sector partnerships. Only through such an integrated approach can quantum literacy become a widespread and equitable component of twenty-first-century education.