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Ryan Landfield

Publications and source records attributed to Ryan Landfield.

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Dataflows and Computational Patterns for Hybrid Quantum-Classical Scientific Computing

Hybrid quantum-classical computing has emerged as the dominant paradigm for near-term quantum applications, yet hybrid workflows are typically described by individual algorithms rather than their underlying execution behavior. We introduce the Quantum Execution Locality Framework (QELF), a qualitative framework for characterizing hybrid quantum-classical workflows according to recurring dataflow structures and quantum execution locality, the extent to which computation remains resident on the Quantum Processing Unit (QPU) before host intervention or classical synchronization. From a representative cross-section of applications, QELF identifies five recurring computational patterns with distinct locality characteristics and discusses their implications for communication overhead, workflow organization, and future hybrid computing architectures. By providing a common vocabulary for reasoning about hybrid workloads, QELF establishes a foundation for future quantitative validation and the co-design of algorithms, runtime systems, and hybrid computing architectures.

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Integrating Quantum Computing Resources into Scientific HPC Ecosystems

Quantum Computing (QC) offers significant potential to enhance scientific discovery in fields such as quantum chemistry, optimization, and artificial intelligence. Yet QC faces challenges due to the noisy intermediate-scale quantum era's inherent external noise issues. This paper discusses the integration of QC as a computational accelerator within classical scientific high-performance computing (HPC) systems. By leveraging a broad spectrum of simulators and hardware technologies, we propose a hardware-agnostic framework for augmenting classical HPC with QC capabilities. Drawing on the HPC expertise of the Oak Ridge National Laboratory (ORNL) and the HPC lifecycle management of the Department of Energy (DOE), our approach focuses on the strategic incorporation of QC capabilities and acceleration into existing scientific HPC workflows. This includes detailed analyses, benchmarks, and code optimization driven by the needs of the DOE and ORNL missions. Our comprehensive framework integrates hardware, software, workflows, and user interfaces to foster a synergistic environment for quantum and classical computing research. This paper outlines plans to unlock new computational possibilities, driving forward scientific inquiry and innovation in a wide array of research domains.

quant-ph

Quantum-centric Supercomputing for Materials Science: A Perspective on Challenges and Future Directions

Computational models are an essential tool for the design, characterization, and discovery of novel materials. Hard computational tasks in materials science stretch the limits of existing high-performance supercomputing centers, consuming much of their simulation, analysis, and data resources. Quantum computing, on the other hand, is an emerging technology with the potential to accelerate many of the computational tasks needed for materials science. In order to do that, the quantum technology must interact with conventional high-performance computing in several ways: approximate results validation, identification of hard problems, and synergies in quantum-centric supercomputing. In this paper, we provide a perspective on how quantum-centric supercomputing can help address critical computational problems in materials science, the challenges to face in order to solve representative use cases, and new suggested directions.

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