Observability Architecture for Quantum-Centric Supercomputing Workflows

TL;DR

This paper introduces an observability architecture for quantum-centric supercomputing workflows that improves monitoring, reproducibility, and analysis of hybrid classical-quantum algorithms on remote hardware.

Contribution

It presents a novel architecture that decouples telemetry collection from execution, enabling persistent, detailed monitoring and retrospective analysis in QCSC workflows.

Findings

Reveals solver behavior across multiple iterations

Enhances transparency and reproducibility in quantum workflows

Supports infrastructure-aware algorithm design

Abstract

Quantum-centric supercomputing (QCSC) workflows often involve hybrid classical-quantum algorithms that are inherently probabilistic and executed on remote quantum hardware, making them difficult to interpret and limiting the ability to monitor runtime performance and behavior. The high cost of quantum circuit execution and large-scale high-performance computing (HPC) infrastructure further restricts the number of feasible trials, making comprehensive evaluation of execution results essential for iterative development. We propose an observability architecture tailored for QCSC workflows that decouples telemetry collection from workload execution, enabling persistent monitoring across system and algorithmic layers and retaining detailed execution data for reproducible and retrospective analysis, eliminating redundant runs. Applied to a representative workflow involving sample-based quantum diagonalization, our system reveals solver behavior across multiple iterations. This approach enhances transparency and reproducibility in QCSC environments, supporting infrastructure-aware algorithm design and systematic experimentation.