Quantum computing exhibits the unique capability to natively and efficiently encode various natural phenomena, promising theoretical speedups of several orders of magnitude. However, not all computational tasks can be efficiently executed on quantum machines, giving rise to hybrid systems, where some portions of an application run on classical machines, while others utilize quantum resources. However, existing methods often to identify candidates to quantum execution (quantum candidates) involve a trial-and-error approach, relying on the intuition and expertise of computer scientists, resulting in varying identification durations ranging from minutes to days for a single application.
Quantum computing exhibits the unique capability to natively and efficiently encode various natural phenomena, promising theoretical speedups of several orders of magnitude. However, not all computational tasks can be efficiently executed on quantum machines, giving rise to hybrid systems, where some portions of an application run on classical machines, while others utilize quantum resources. However, existing methods often to identify candidates to quantum execution (quantum candidates) involve a trial-and-error approach, relying on the intuition and expertise of computer scientists, resulting in varying identification durations ranging from minutes to days for a single application.
As we enter the post-Moore era, we experience the rise of various non-von-Neumann-architectures to address the increasing computational demand for modern applications, with quantum computing being among the most prominent and promising technologies. However, this development creates a gap in current computer science curricula since most quantum computing lectures are strongly physics-oriented and have little intersection with the remaining curriculum of computer science. This fact makes designing an appealing course very difficult, in particular for non-physicists. Furthermore, in the academic community, there is consensus that quantum computers are going to be used only for specific computational tasks (e.g., in computational science), where hybrid systems - combined classical and quantum computers - facilitate the execution of an application on both quantum and classical computing resources. A hybrid system thus executes only certain suitable parts of an application on the quantum machine, while other parts are executed on the classical components of the system. To fully exploit the capabilities of hybrid systems and to meet future requirements in this emerging field, we need to prepare a new generation of computer scientists with skills in both distributed computing and quantum computing. To bridge this existing gap in standard computer science curricula, we designed a new lecture and exercise series on Hybrid Quantum-Classical Systems, where students learn how to decompose applications and implement computational tasks on a hybrid quantum-classical computational continuum. While learning the inherent concepts underlying quantum systems, students are obligated to apply techniques and methods they are already familiar with, making the entrance to the field of quantum computing comprehensive yet appealing and accessible to students of computer science.
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, simulators, and cloud service providers that are likely to be central to the development of any hybrid computing solutions. We conclude by identifying an agenda for future research on classical-quantum hybrid software engineering, and some near-term priorities for development of functional, robust hybrid software systems. The paper argues that due to the deep dependency of quantum execution on classical pre- and post-processing, hybrid computing may represent a near-permanent outcome of classical-quantum computing rather than a transitional workaround.
Hybrid computing models, which integrate both classical and quantum systems, represent a promising frontier in enhancing computational power and efficiency. Classical computing systems, while highly efficient for a broad range of tasks, encounter significant limitations when addressing complex, large-scale problems such as optimization, cryptography, and molecular simulations. Quantum computing, on the other hand, offers exponential speedup for specific algorithms, particularly in areas like factorization, search, and simulation of quantum systems. However, quantum systems are not yet mature enough to replace classical systems entirely, due to issues like error rates, qubit coherence, and the need for cryogenic environments. As a result, hybrid models are emerging as the most viable solution, combining the strengths of both paradigms to tackle computationally intensive tasks more efficiently. This paper provides a comprehensive analysis of the development and application of hybrid computing models. It explores how these models leverage the parallel processing power of quantum systems while utilizing the robustness and data handling capabilities of classical systems. We discuss various architectures proposed for hybrid computing, focusing on their scalability, efficiency, and real-world applications across industries such as healthcare, finance, cryptography, and artificial intelligence. Additionally, we examine the challenges involved in integrating classical and quantum systems, including synchronization, resource management, and algorithm development. The paper concludes with future directions for hybrid computing, emphasizing the potential for these models to revolutionize fields that rely on high-performance computing by offering unprecedented computational capabilities. In summary, hybrid computing models stand at the intersection of classical and quantum technologies, providing a synergistic approach to solving some of the most challenging computational problems faced today.
This paper investigates the potential of hybrid quantum-classical algorithms to address complex optimization problems. We propose a novel framework where classical computations are predominantly executed, utilizing quantum algorithms for specialized tasks that offer significant speedups. The design focuses on creating a synergistic system where the strengths of both paradigms are maximized. The core claim is that this approach offers a pathway to efficiently solve problems currently deemed intractable, leveraging the unique capabilities of quantum computation while maintaining the scalability of classical methods.
The emergence of quantum computing proposes a revolutionary paradigm that can radically transform numerous scientific and industrial application domains. The ability of quantum computers to scale computations exponentially imply better performance and efficiency for certain algorithmic tasks than current computers provide. However, to gain benefit from such improvement, quantum computers must be integrated with existing software systems, a process that is not straightforward. In this paper, we investigate challenges that emerge from building larger hybrid classical-quantum computers, and discuss some approaches that could be employed to overcome these challenges.
Scientific workflows constitute a pivotal resource for the scientific computing community, as they provide a standard interface to define, execute, and analyze the results of scientific computations. The advent of the Post-Moore era exposes the scientific computing research community with the challenge of scaling HPC facilities, to address the demands of modern scientific applications. While quantum computing, on the one hand, promises noteworthy theoretical speedups for different scientific applications, executing a whole scientific workflow on quantum hardware is infeasible since not all computations can achieve a speedup on quantum hardware. As a consequence, computational scientists defined the concept of a hybrid quantum-classical workflow, which is capable of exploiting the capabilities of quantum and classical hardware. Since the adaptation of classical scientific workflows to the new hybrid quantum-classical scenario can be challenging for scientists, we propose RIGOLETTO, a workflow definition language allowing scientists to define where to execute each task on a hybrid quantum-classical system. As a proof-of-concept, we report on two scientific workflows and identify open challenges in the definition of hybrid quantum-classical workflows.