We present the design, development, and application of QHDL, a quantum hardware description language specifically designed for tightly-coupled quantum–classical computing systems. Together with the language design principles, we describe the QHDL compiler, debugger, and co-simulation infrastructure. We showcase the benefits of using a quantum–classical integrated approach in four use cases, requiring close quantum–classical device interaction: Bell’s pair circuit, dynamic delay, Quantum Fourier Transform (QFT), and teleportation. To interface with QHDL, we propose to use synchronous techniques that are commonplace in digital hardware design. We illustrate examples of modelling both loosely-coupled and tightly-coupled quantum circuits that use so-called measurement-in-the-middle by utilizing these techniques in QHDL. For clock-cycle accurate implementations, we propose implementing such modules as programmable hardware blocks using Register-Transfer Level (RTL) or gate-level approaches. These approaches provide the highest coupling performance and are feasible to be implemented in state-of-the-art control systems.

There is much debate on whether quantum computing on current NISQ devices, consisting of noisy hundred qubits and requiring a non-negligible usage of classical computing as part of the algorithms, has utility and will ever offer advantages for scientific and industrial applications with respect to traditional computing. In this position paper, we argue that while real-world NISQ quantum applications have yet to surpass their classical counterparts, strategic approaches can be used to facilitate advancements in both industrial and scientific applications. We have identified three key strategies to guide NISQ computing towards practical and useful implementations. Firstly, prioritizing the identification of a "killer app" is a key point. An application demonstrating the distinctive capabilities of NISQ devices can catalyze broader development. We suggest focusing on applications that are inherently quantum, e.g., pointing towards quantum chemistry and material science as promising domains. These fields hold the potential to exhibit benefits, setting benchmarks for other applications to follow. Secondly, integrating AI and deep-learning methods into NISQ computing is a promising approach. Examples such as quantum Physics-Informed Neural Networks and Differentiable Quantum Circuits (DQC) demonstrate the synergy between quantum computing and AI. Lastly, recognizing the interdisciplinary nature of NISQ computing, we advocate for a co-design approach. Achieving synergy between classical and quantum computing necessitates an effort in co-designing quantum applications, algorithms, and programming environments, and the integration of HPC with quantum hardware. The interoperability of these components is crucial for enabling the full potential of NISQ computing. In conclusion, through the usage of these three approaches, we argue that NISQ computing can surpass current limitations and evolve into a valuable tool for scientific and industrial applications. This requires an approach that integrates domain-specific killer apps, harnesses the power of quantum-enhanced AI, and embraces a collaborative co-design methodology.

This paper proposes a descriptive language called QHDL, akin to VHDL, to program gate-based quantum computing systems. Unlike other popular quantum programming languages, QHDL targets low-level quantum computing programming and aims to provide a common framework for programming FPGAs and gate-based quantum computing systems. The paper presents an initial implementation and design principles of the QHDL framework, including a compiler and quantum computer simulator. We discuss the challenges of low-level integration of streaming models and quantum computing for programming FPGAs and gate-based quantum computing systems.

Abstract Many production Grid and e‐Science infrastructures have begun to offer services to end‐users during the past several years with an increasing number of scientific applications that require access to a wide variety of resources and services in multiple Grids. Therefore, the Grid Interoperation Now—Community Group of the Open Grid Forum—organizes and manages interoperation efforts among those production Grid infrastructures to reach the goal of a world‐wide Grid vision on a technical level in the near future. This contribution highlights fundamental approaches of the group and discusses open standards in the context of production e‐Science infrastructures. Copyright © 2009 John Wiley & Sons, Ltd.