Current computational theory deals almost exclusively with single models: classical, neural, analogue, quantum, etc. In practice, researchers use ad hoc combinations, realizing only recently that they can be fundamentally more powerful than the individual parts. A Theo Murphy meeting brought together theorists and practitioners of various types of computing, to engage in combining the individual strengths to produce powerful new heterotic devices. 'Heterotic computing' is defined as a combination of two or more computational systems such that they provide an advantage over either substrate used separately. This post-meeting collection of articles provides a wide-ranging survey of the state of the art in diverse computational paradigms, together with reflections on their future combination into powerful and practical applications.
Current computational theory deals almost exclusively with single models: classical, neural, analogue, quantum, etc. In practice, researchers use ad hoc combinations, realizing only recently that they can be fundamentally more powerful than the individual parts. A Theo Murphy meeting brought together theorists and practitioners of various types of computing, to engage in combining the individual strengths to produce powerful new heterotic devices. 'Heterotic computing' is defined as a combination of two or more computational systems such that they provide an advantage over either substrate used separately. This post-meeting collection of articles provides a wide-ranging survey of the state of the art in diverse computational paradigms, together with reflections on their future combination into powerful and practical applications.
This research focuses on the evolving field of hybrid computing, which combines traditional computing architectures with advanced computational paradigms like quantum computing, neuromorphic computing, or cloud-edge computing. Hybrid computing involves integrating multiple computing models or systems to leverage their complementary strengths. For example, combining classical computing with quantum or neuromorphic approaches to solve complex problems more efficiently. The goal is to enhance computational power, increase efficiency, and tackle problems that are difficult for traditional computing models alone. Emerging technologies, including quantum computing, machine learning, and AI, are driving the need for hybrid systems. These technologies enable the handling of more complex computations that classical systems struggle with, such as optimization, simulation, and cryptography. The integration of cloud and edge computing is also central to hybrid computing, enabling decentralized computation and processing closer to data sources. Combining different computing paradigms requires seamless communication and integration between systems, which is a significant technical challenge. Main challenging areas are scalability, software and hardware compatibility, energy efficiency and security. There is a need for standard protocols and frameworks to enable smoother integration of various computing systems. New algorithms that can take advantage of hybrid architectures need to be developed. Depend on applications significant advancements in hardware, including quantum processors, neuromorphic chips, and integrated hybrid processors required. The rise of hybrid computing will also bring ethical concerns, such as the impact on jobs, privacy, and inequality in access to advanced technologies. The study concludes by emphasizing the transformative potential of hybrid computing while acknowledging the technical, economic, and societal hurdles that need to be overcome. It highlights that while significant progress is being made, realizing the full potential of hybrid computing will require continued research, collaboration, and innovation across multiple domains. In summary, hybrid computing is poised to revolutionize many fields by merging the strengths of various computational models, but its widespread adoption will require overcoming substantial technical, economic, and regulatory challenges.
Through harnessing quantum mechanical phenomena, quantum computing substrates yields the promise of transcending new frontiers in confronting the world’s most intractable computational challenges requiring exponential computational power that exceeds that of today’s most powerful super-computers. The choreography of such quantum mechanical phenomena has catalysed accelerated advances in the emergence of a quantum computational substrate paradigm that yields the potential to solve some of humanities most complex problems through quantum speedup: in environment, agriculture, health, energy, climate, materials science, precision medicine, autonomous vehicles in smart cities, renewable energy and problems humanity has not yet even imagined. However, the manifestation of a practical quantum advantage across several of these areas is unlikely through the exclusive application of a quantum computational substrate. Quantum computational substrates will not replace classical computational substrates, instead, both technologies will synergistically complement each other where quantum computational substrate accelerators functions as a specialised co-processor to a classical computational substrate in computing workloads best suited for the quantum computer, in a heterogenous computational substrate ecosystem comprising other accelerators. This paper reviewed novel new frontiers at the fusion of quantum-classical computational substrates, and findings reveal that practical applications and research remain limited. The opportunity exists, in considering a heterogenous computational substrate ecosystem comprising other accelerators, for practical quantum advantage across a broader set of complex problem domains to be realised sooner.
Through harnessing quantum mechanical phenomena, quantum computing substrates yields the promise of transcending new frontiers in confronting the world’s most intractable computational challenges requiring exponential computational power that exceeds that of today’s most powerful super-computers. The choreography of such quantum mechanical phenomena has catalysed accelerated advances in the emergence of a quantum computational substrate paradigm that yields the potential to solve some of humanities most complex problems through quantum speedup: in environment, agriculture, health, energy, climate, materials science, precision medicine, autonomous vehicles in smart cities, renewable energy and problems humanity has not yet even imagined. However, the manifestation of a practical quantum advantage across several of these areas is unlikely through the exclusive application of a quantum computational substrate. Quantum computational substrates will not replace classical computational substrates, instead, both technologies will synergistically complement each other where quantum computational substrate accelerators functions as a specialised co-processor to a classical computational substrate in computing workloads best suited for the quantum computer, in a heterogenous computational substrate ecosystem comprising other accelerators. This paper reviewed novel new frontiers at the fusion of quantum-classical computational substrates, and findings reveal that practical applications and research remain limited. The opportunity exists, in considering a heterogenous computational substrate ecosystem comprising other accelerators, for practical quantum advantage across a broader set of complex problem domains to be realised sooner
Artificial intelligence has been transformed by deep neural networks, yet the search for new learning architectures continues. Quantum machine learning offers one such direction, and hybrid quantum neural networks, which combine classical neural-network components with quantum information processing units, have emerged as a practical framework for near-term quantum technologies. However, the rapid development of the field across diverse architectures, benchmarks and hardware assumptions makes it difficult to assess the utility of various proposals, identify where genuine advantages may arise, and determine how practitioners can use these models. While recent benchmarks caution that such gains have not yet been demonstrated at scale, theoretical work has identified tasks on which quantum models hold provable advantages, and hybrid approaches have delivered promising results on practical problems using deliberately compact quantum components and substantially fewer trainable parameters. Here, we review hybrid quantum neural networks for the machine-learning and quantum-machine-learning communities. We summarize their main theoretical and methodological foundations, survey some of the most promising architectures developed so far, and examine their implementation challenges and reported performance. By consolidating these perspectives, this review provides a structured view of the state of the field and helps identify promising paths for future research and application-driven development.
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.
The coming era of quantum computing promises to revolutionize computation, but, given the efficiency of many classical routines as well as quantum hardware limitations, calls to quantum devices are and will continue to be used as subroutines in larger, hybrid quantum-classical algorithms. This Perspective explores the essential differences between pure and hybrid quantum algorithms, discusses some examples, and frames the future of hybrid quantum-classical algorithms in its natural context of classical heterotic computing.