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.
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
The choreography of 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 computing speedup requires exponential computational power that exceeds that of today’s most powerful super-computers, to confront the world’s most intractable computational challenges 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. Current quantum computational substrates are limited by circuit depth, noise processes, qubit count, and fault tolerant quantum computational substrates unlikely to be available in the near term. Hybrid quantum algorithms/Variational Quantum Algorithms (VQAs), operate by using a classical optimizer to orchestrate the parameters of a quantum circuit, have become a prominent approach to address these limitations. However, several challenges manifest that include the training, speedup. accuracy, and impactful practical application of VQAs. This paper reviews novel new frontiers in current VQAs, provides an analysis of VQAs, their strengths and limitations, and outlines potential applications in the quest towards yielding practical quantum advantage. Secondly, this paper also reviews and provides an assessment of quantum software development frameworks.
The choreography of 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 computing speedup requires exponential computational power that exceeds that of today’s most powerful super computers, to confront the world’s most intractable computational challenges 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. Current quantum computational substrates are limited by circuit depth, noise processes, qubit count, and fault tolerant quantum computational substrates unlikely to be available in the near term. Hybrid quantum algorithms/Variational Quantum Algorithms (VQAs), operate by using a classical optimizer to orchestrate the parameters of a quantum circuit, have become a prominent approach to address these limitations. However, several challenges manifest that include the training, speedup. accuracy, and impactful practical application of VQAs. This paper reviews novel new frontiers in current VQAs, provides an analysis of VQAs, their strengths and limitations, and outlines potential applications in the quest towards yielding practical quantum advantage. Secondly, this paper also reviews and provides an assessment of quantum software development frameworks.
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.
A quantum machine may solve some complex problems that are intractable for even the most powerful classical computers. By exploiting quantum superposition and entanglement phenomena, quantum algorithms can achieve from polynomial to exponential speed up when compared to their best classical counterparts. A quantum computer will be a part of a heterogeneous, multi-core computer in which a classical processor will interact with several accelerators such as FPGAs, GPUs and also a quantum co-processor. Figure 1 shows the different layers of the quantum computer system stack [1]. Building such a quantum system requires contributions from different fields such as physics, electronics, computer science and computer engineering for addressing the following challenges: i) build scalable quantum chips integrating qubits with long coherence times and high-fidelity operations, ii) develop classical control electronics at possibly cryogenic temperatures and iii) create the microarchitecture as well as the software for quantum computation.
Abstract Quantum computing leverages fundamental quantum-mechanical phenomena—most notably superposition, entanglement, and quantum tunneling—to perform computations in ways that differ fundamentally from classical digital processing. Compared to conventional computing architectures, quantum systems promise significant reductions in computational time and potentially lower energy consumption for specific classes of problems. These advantages, combined with advances in nanoscale engineering and the slowing of traditional hardware scaling trends such as Moore’s Law, have led to substantial commercial and national-security-driven investments in quantum technologies over the past decade. A major milestone was reached when Google announced the experimental demonstration of “quantum supremacy,” showing that a quantum processor could execute a specific computational task infeasible for a classical supercomputer within practical time constraints. Although the demonstrated problem had no immediate practical application, this achievement marked a crucial step toward the development of quantum systems capable of delivering real-world computational advantages and provided a more precise technical framing of the concept of quantum advantage. Despite the fact that the mathematical foundations, programming models, and principal quantum algorithms were established in the 1990s, quantum computing remained largely confined to a specialized research community for decades. Today, the increasing accessibility of cloud-based quantum hardware calls for a broader dissemination of implementation-oriented knowledge among researchers and practitioners in computer science, software engineering, and related fields. The quantum programming paradigm differs fundamentally from classical programming models and often relies on physical and algebraic notations that may pose barriers to traditionally trained computer scientists. This series of articles provides a self-contained and concise introduction to quantum computing and its core algorithms, with a strong emphasis on practical implementation. Given that real quantum processors—such as those accessible via the IBM quantum ecosystem—are now available as cloud services, experimental validation can be conducted both on simulators and on actual hardware for small-scale inputs. While other platforms such as Rigetti and IonQ also offer cloud-based quantum computing services, this review focuses exclusively on the IBM quantum computing ecosystem.