Training Computer Scientists for the Challenges of Hybrid Quantum-Classical Computing

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   Hybrid Classical-Quantum Solutions to Accelerate the Adoption of Quantum Computing

   Advait Deshpande, Luciano Baresi, A. Bandara · 2026 · Pas encore résumé

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   Despite the promise and potential of quantum computing, most of the current day developments of quantum computing have several limitations for relevant practical use. As a result, hybrid classical-quantum computing has emerged as a viable solution. This paper discusses the software aspects of hybrid classical-quantum computing solutions to understand and examine how hybrid computing solutions can be expected to work in practice. This paper examines existing software solutions (cloud applications, middleware frameworks, and API gateways) that can enable classical and quantum computing to work together. For practitioners the paper provides a curated list of algorithms, full stack libraries, si…

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   Résumé scientifique

   Hybrid quantum-classical computing systems consist of a classical control system that sends quantum circuits and receives measurement results from a quantum co-processor. Such systems allow us to model algorithms that require the classical control system to generate quantum circuits on the fly, potentially based on prior measurement results. This is challenging, as the classical control system must generate quantum circuits that manipulate live quantum states. In this setting, the classical control system is generating further quantum circuits while the quantum co-processor is internally maintaining the state of the live qubits; this is not ideal, since this not only is costly but also intro…

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   Quantum computing represents a fundamental shift in computation, promising to solve problems currently intractable for classical computers. However, misconceptions persist; quantum computers are not universally faster, nor do they replace classical systems. Current Noisy Intermediate-Scale Quantum (NISQ) devices face significant challenges, including decoherence, high error rates, and limited scalability. Additionally, leveraging quantum systems requires rethinking traditional computing principles, adopting different programming models, and addressing the complexities of integrating quantum and classical workflows, making their practical use in computer science both demanding and non-trivial…

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   Résumé scientifique

   Quantum computing is expected to make significant advances in areas such as optimization, physical system simulation, cryptography, and artificial intelligence. Although most existing educational programs in quantum computing maintain a strong emphasis on quantum physics, advanced mathematics, and the theoretical foundations of quantum computing, they leave a significant training gap for professionals from computer engineering, software development, and computer science, whose usual approach to system design, algorithms, and software architecture differs from the predominant physical perspective. The Ibero-American region has shown growing institutional and academic interest in integrating q…

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   Molecular Dynamics Workflow Decomposition for Hybrid Classic/Quantum Systems

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   Résumé scientifique

   Since we are entering the Post-Moore Law era and consequently the limit of Von Neumann's architecture, the scientific community is looking for alternatives to satisfy the growing computing power demands of scientific applications. Quantum computing promises to achieve a computational advantage over the classic Von Neumann architecture. However, the limited capabilities of current noisy intermediate-scale quantum (NISQ) devices require quantum computers to interoperate with classic systems, forming the so-called hybrid quantum systems. Research on hybrid quantum systems led to the design of Variational Quantum Algorithms, currently the most promising way to move towards quantum advantage. How…

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   A Survey on Integrating Quantum Computers into High Performance Computing Systems

   Philip Döbler, M. Jattana · 2025 · arXiv.org · preprint, non relu par les pairs · cité 11 fois · Pas encore résumé

   Résumé scientifique

   Quantum computers use quantum mechanical phenomena to perform conventionally intractable calculations for specific problems. Despite being universal machines, quantum computers are not expected to replace classical computers, but rather, to complement them and form hybrid systems. This makes integrating quantum computers into high performance computing (HPC) systems an increasingly relevant topic. We present a structured literature review on the integration aspect. We methodologically search literature databases and manually evaluate 107 publications. These publications are divided into seven categories that describe the state of the art in each category. After a brief quantitative analysis …

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   Résumé scientifique

   Quantum machine learning (QML) is increasingly recognized as one of the most promising near-term applications of quantum computing, viewed as a next-frontier candidate beyond purely classical approaches. Hybrid quantum-classical models operationalize this potential by embedding a parameterized quantum circuit within a model where all other components remain classical-a design already applied to chemistry simulation, financial modeling, and image classification. However, their deployment in privacy-sensitive, multi-party settings is constrained by the need to avoid centralizing raw data and by the requirement that modern quantum circuits remain parameter-efficient to stay trainable at scale. …

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   Résumé scientifique

   Classical and quantum computers have achieved collaborative computation and completed applications across numerous fields. Nevertheless, the absence of a unified hybrid computation model has led to issues such as non-standardized engineering architecture design, ambiguous task partitioning criteria and so on. To address these issues, this research proposes a hybrid classical-quantum computation model (HTM). By constructing unified state sets and transition function representations, HTM clarifies hybrid control logic and implements a control-sharing mechanism. Simultaneously, a comprehensive complexity calculation method based on entanglement and superposition was proposed, supporting computa…

Les travaux antérieurs sur lesquels il s’appuie

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   Applications such as simulating large quantum systems or solving large-scale linear algebra problems are immensely challenging for classical computers due their extremely high computational cost. Quantum computers promise to unlock these applications, although fault-tolerant quantum computers will likely not be available for several years. Currently available quantum devices have serious constraints, including limited qubit numbers and noise processes that limit circuit depth. Variational Quantum Algorithms (VQAs), which employ a classical optimizer to train a parametrized quantum circuit, have emerged as a leading strategy to address these constraints. VQAs have now been proposed for essent…

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   Résumé scientifique

   The prospects of quantum computing have driven efforts to realize fully functional quantum processing units (QPUs). Recent success in developing proof-of-principle QPUs has prompted the question of how to integrate these emerging processors into modern high-performance computing (HPC) systems. We examine how QPUs can be integrated into current and future HPC system architectures by accounting for functional and physical design requirements. We identify two integration pathways that are differentiated by infrastructure constraints on the QPU and the use cases expected for the HPC system. This includes a tight integration that assumes infrastructure bottlenecks can be overcome as well as a loo…

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   At the intersection of machine learning and quantum computing, quantum machine learning has the potential of accelerating data analysis, especially for quantum data, with applications for quantum materials, biochemistry and high-energy physics. Nevertheless, challenges remain regarding the trainability of quantum machine learning models. Here we review current methods and applications for quantum machine learning. We highlight differences between quantum and classical machine learning, with a focus on quantum neural networks and quantum deep learning. Finally, we discuss opportunities for quantum advantage with quantum machine learning. Quantum machine learning has become an essential tool t…

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   Quantum information processing is steadily progressing from a purely academic discipline towards applications throughout science and industry. Transitioning from lab-based, proof-of-concept experiments to robust, integrated realizations of quantum information processing hardware is an important step in this process. However, the nature of traditional laboratory setups does not offer itself readily to scaling up system sizes or allow for applications outside of laboratory-grade environments. This transition requires overcoming challenges in engineering and integration without sacrificing the state-of-the-art performance of laboratory implementations. Here, we present a 19-inch rack quantum co…

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