Dataflows and Computational Patterns for Hybrid Quantum-Classical Scientific Computing
- URL: http://arxiv.org/abs/2608.19348v1
- Date: Wed, 19 Aug 2026 18:10:01 GMT
- Title: Dataflows and Computational Patterns for Hybrid Quantum-Classical Scientific Computing
- Abstract summary: Hybrid quantum-classical computing has emerged as the dominant paradigm for near-term quantum applications.<n>We introduce the Quantum Execution Locality Framework (QELF), a framework for characterizing hybrid quantum-classical computation.
- Score: 1.7125060577264746
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
Related papers
- Learnable yet not simulable: a quantum resource theory of learning models [57.695822250391906]
It remains unclear which quantum resource governs the classical learnability of quantum circuits.<n>We study the expectation-value functions of families of tunable quantum circuits.<n>These results establish a quantitative resource-theoretic framework for delineating the boundary between classical simulation and learning.
arXiv Detail & Related papers (2026-08-03T14:48:03Z) - Three ways to share a QPU: Scheduling strategies for hybrid Quantum-HPC applications [0.9023764564490087]
We investigate three distinct methodologies for HPC-QC resource scheduling.<n>We show that time-based multiplexing, dynamic resource management, and workflow decomposition are most effective for hybrid jobs.<n>These findings underscore the practical viability of tailored scheduling strategies for hybrid HPC-QC environments.
arXiv Detail & Related papers (2026-04-16T12:52:45Z) - Kubernetes-Orchestrated Hybrid Quantum-Classical Workflows [0.0]
Hybrid quantum-classical combine quantum processing units (QPUs) with classical hardware to address computational tasks that are challenging or infeasible for conventional systems alone.<n>We present a framework for managing hybrid quantum-native pipelines using, Argos, and Kueue.<n>We demonstrate the framework with a proof-of-concept showcasing distributed quantum circuit cutting, HPC execution across heterogeneous nodes and integration of classical and quantum tasks.
arXiv Detail & Related papers (2026-03-25T11:31:25Z) - HQPEF-Py: Metrics, Python Patterns, and Guidance for Evaluating Hybrid Quantum Programs [0.0]
We study how to evaluate hybrid quantum programs as end-to-end rather than as isolated devices or algorithms.<n>We formalize a workflow-aware Quantum Readiness Level (QRL) score; define a normalized speedup under quality constraints for the Utility of Quantumness (UQ)<n>We complement these definitions with concise Python reference implementations that illustrate how to instantiate the metrics and audit procedures.
arXiv Detail & Related papers (2025-11-23T15:47:43Z) - Quantum Hyperdimensional Computing: a foundational paradigm for quantum neuromorphic architectures [0.5785414922972825]
We introduce Quantum Hyperdimensional Computing (QHDC), a fundamentally new paradigm.<n>We demonstrate that the core operations of its classical counterpart, Hyperdimensional Computing (HDC), map with remarkable elegance and direct correspondence onto the native operations of a QC.<n>We present the first-ever implementation of this framework, validated through symbolic analogical reasoning and supervised classification tasks.
arXiv Detail & Related papers (2025-11-16T16:01:56Z) - VQC-MLPNet: An Unconventional Hybrid Quantum-Classical Architecture for Scalable and Robust Quantum Machine Learning [50.95799256262098]
Variational quantum circuits (VQCs) hold promise for quantum machine learning but face challenges in expressivity, trainability, and noise resilience.<n>We propose VQC-MLPNet, a hybrid architecture where a VQC generates the first-layer weights of a classical multilayer perceptron during training, while inference is performed entirely classically.
arXiv Detail & Related papers (2025-06-12T01:38:15Z) - Multi-GPU-Enabled Hybrid Quantum-Classical Workflow in Quantum-HPC Middleware: Applications in Quantum Simulations [1.9922905420195367]
This study introduces an innovative distribution-aware Quantum-Classical-Quantum architecture.
It integrates cutting-edge quantum software framework works with high-performance classical computing resources.
It addresses challenges in quantum simulation for materials and condensed matter physics.
arXiv Detail & Related papers (2024-03-09T07:38:45Z) - A Quantum-Classical Collaborative Training Architecture Based on Quantum
State Fidelity [50.387179833629254]
We introduce a collaborative classical-quantum architecture called co-TenQu.
Co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting.
It outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.
arXiv Detail & Related papers (2024-02-23T14:09:41Z) - QuaSiMo: A Composable Library to Program Hybrid Workflows for Quantum
Simulation [48.341084094844746]
We present a composable design scheme for the development of hybrid quantum/classical algorithms and for applications of quantum simulation.
We implement our design scheme using the hardware-agnostic programming language QCOR into the QuaSiMo library.
arXiv Detail & Related papers (2021-05-17T16:17:57Z) - Tensor Network Quantum Virtual Machine for Simulating Quantum Circuits
at Exascale [57.84751206630535]
We present a modernized version of the Quantum Virtual Machine (TNQVM) which serves as a quantum circuit simulation backend in the e-scale ACCelerator (XACC) framework.
The new version is based on the general purpose, scalable network processing library, ExaTN, and provides multiple quantum circuit simulators.
By combining the portable XACC quantum processors and the scalable ExaTN backend we introduce an end-to-end virtual development environment which can scale from laptops to future exascale platforms.
arXiv Detail & Related papers (2021-04-21T13:26:42Z) - Composable Programming of Hybrid Workflows for Quantum Simulation [48.341084094844746]
We present a composable design scheme for the development of hybrid quantum/classical algorithms and for applications of quantum simulation.
We implement our design scheme using the hardware-agnostic programming language QCOR into the QuaSiMo library.
arXiv Detail & Related papers (2021-01-20T14:20:14Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.