Quantum computing represents a paradigm shift requiring reconceptualization of algorithms, architectures, and software. Although much is new, there is much that quantum computing can learn from traditional classical computer engineering. In this Special Issue, we focus on examples of quantum research inspired by classical computer engineering, ranging from quantum machine learning and image processing to the extension of the “multi-core” concept to quantum computing. Hardware/software co-design is difficult when communities have their unique jargon and disjoint problem domains. For parallel programming on classical computers, an approach for communicating application developer needs emerged around identifying key bottlenecks that limited software. It was difficult to guess features of future applications, but experienced developers knew features of a computer system that would limit software performance. For each feature, a small kernel was developed with the idea that if the kernel ran well, a system would be an effective system for application developers. This set of kernels was called the Parallel Research Kernels . They have been useful to guide design of parallel computers. We hypothesize that an analogous tool for quantum computing, called Quantum Research Kernels , may aid hardware/software co-design of quantum computing systems. In this article, we motivate the Quantum Research Kernels and provide working examples.
Quantum computing represents a paradigm shift requiring reconceptualization of algorithms, architectures, and software. Although much is new, there is much that quantum computing can learn from traditional classical computer engineering. In this Special Issue, we focus on examples of quantum research inspired by classical computer engineering, ranging from quantum machine learning and image processing to the extension of the “multi-core” concept to quantum computing. Hardware/software co-design is difficult when communities have their unique jargon and disjoint problem domains. For parallel programming on classical computers, an approach for communicating application developer needs emerged around identifying key bottlenecks that limited software. It was difficult to guess features of future applications, but experienced developers knew features of a computer system that would limit software performance. For each feature, a small kernel was developed with the idea that if the kernel ran well, a system would be an effective system for application developers. This set of kernels was called the Parallel Research Kernels . They have been useful to guide design of parallel computers. We hypothesize that an analogous tool for quantum computing, called Quantum Research Kernels , may aid hardware/software co-design of quantum computing systems. In this article, we motivate the Quantum Research Kernels and provide working examples.
Quantum computing represents a paradigm shift for computation requiring an entirely new computer architecture. However, there is much that can be learned from traditional classical computer engineering. In this paper, we describe the Parallel Research Kernels (PRK), a tool that was very useful for designing classical parallel computing systems. The PRK are simple kernels written to expose bottlenecks that limit classical parallel computing performance. We hypothesize that an analogous tool for quantum computing, Quantum Research Kernels (QRK), may similarly aid the co-design of software and hardware for quantum computing systems, and we give a few examples of representative QRKs.
Quantum computing is rapidly emerging as a transformative force poised to redefine the foundations of software engineering. By leveraging quantum mechanical phenomena such as superposition, entanglement, and interference, quantum computers offer the potential to solve complex problems far beyond the reach of classical systems. This paper explores the multifaceted impact of quantum computing on software engineering, focusing on key areas including quantum algorithms, cryptography, programming paradigms, and application domains. It examines how quantum algorithms like Shor's and Grover's promise significant computational speedups, threatening classical encryption schemes and prompting the rise of post-quantum cryptography. The paper also analyzes the challenges of quantum programming, including steep learning curves, limited hardware access, and the need for new development tools and testing frameworks. These challenges highlight the current limitations of quantum technology, such as hardware instability and scalability issues. As quantum hardware evolves, the integration of hybrid quantum-classical systems is identified as a practical approach for near-term applications in areas such as drug discovery, finance, and optimization. The paper further discusses ethical considerations, such as equitable access to quantum resources and privacy concerns, alongside potential societal impacts to ensure responsible innovation. It underscores the need for future advancements in education, standardization, and hybrid systems to bridge the gap between current limitations and quantum computing’s potential. The paper concludes by emphasizing the importance of educational reform, standardization, and ethical considerations in preparing the software engineering community for a quantum-driven future.
Quantum computing, built on the principles of quantum mechanics, has emerged as a transformative paradigm with the potential to revolutionize computational capabilities, enabling solutions to problems that are currently intractable for classical systems. Quantum computing integrates hardware, architectures, and software for improved applications while utilizing superposition, entanglement, and interference to promote materials science, cryptography, optimization, and artificial intelligence. Sections introduce and describe the model components needed in order to create quantum processors, including qubits, quantum gates and control electronics, as well as a review of new developments in quantum software frameworks and programming tools. Notable examples, such as Shor’s algorithm for cryptographic caveat, Grover’s algorithm for inefficient search processes, and quantum-enhanced optimization, illuminate the position of quantum computing as a means of tackling tough problems that span disciplines, such as pharmaceuticals, predictors of climate, and finance modeling. However, the field continues to face significant challenges, like limited scalability, quantum gate error rates, qubit coherence times, and difficulties integrating with classical systems. Future efforts will focus on developing hybrid quantum-classical systems, scalable architectures, and industry- specific applications to transition quantum computing from exploration to practical deployment. This review provides a comprehensive analysis of quantum computing, covering its potential, trends, and challenges, while exploring hardware-software integration, classicalquantum comparisons, and insights to guide research and industry in unlocking its revolutionary possibility.
The convergence of artificial intelligence (AI), quantum computing, and cloud infrastructure represents a paradigm shift in how intelligent systems are designed, trained, and deployed. As AI models continue to demand exponential computational resources, classical systems are increasingly reaching their performance limits in domains such as high-dimensional optimization, complex pattern recognition, and probabilistic modeling. Quantum computing, with its foundational principles of superposition, entanglement, and quantum parallelism, offers a theoretical edge in solving problems that are intractable for conventional hardware. However, the practical deployment of quantum algorithms—particularly those relevant to machine learning (ML) has been hindered by the limited scalability and accessibility of quantum hardware. To bridge this gap, cloud-based quantum computing platforms have emerged, providing remote, programmable access to quantum devices and hybrid runtimes. In this context, the present study offers a comprehensive review of cloud-delivered quantum AI systems, examining core architectures, hybrid quantum-classical algorithms, platform capabilities, and real-world use cases. The paper synthesizes recent advances and performance insights from systems such as Amazon Braket, IBM Quantum, and Azure Quantum, while critically analyzing algorithmic trends like variational quantum circuits and quantum kernel methods. It further explores challenges including circuit depth limitations, latency in hybrid models, and standardization barriers across toolchains. By consolidating current research and implementation patterns, this review aims to inform the design and future trajectory of scalable, cloud-native quantum AI applications.
A quantum computing system naturally consists of two components, the software system and the hardware system. Quantum applications are programmed using the quantum software and then executed on the quantum hardware. However, the performance of existing quantum computing system is still limited. Solving a practical problem that is beyond the capability of classical computers on a quantum computer has not yet been demonstrated. In this review, we point out that the quantum software and hardware systems should be designed collaboratively to fully exploit the potential of quantum computing. We first review three related works, including one hardware-aware quantum compiler optimization, one application-aware quantum hardware architecture design flow, and one co-design approach for the emerging quantum computational chemistry. Then we discuss some potential future directions following the co-design principle.
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.