Q-IRIS: The Evolution of the IRIS Task-Based Runtime to Enable Classical-Quantum Workflows

2025-12-15

AI Summary

To address the challenge of co-scheduling classical and quantum computations on heterogeneous HPC systems, this paper introduces the first hybrid execution system integrating the IRIS task runtime with the XACC framework. Methodologically, it establishes a cross-platform, task-level quantum workflow orchestration mechanism built upon the Quantum Intermediate Representation (QIR), and proposes a circuit-cutting–driven task-granularity optimization technique to enable asynchronous co-scheduling and load balancing between classical and quantum tasks. The system employs the QIR-EE execution engine to unify scheduling across multiple backends—including diverse quantum simulators—thereby significantly alleviating queue congestion. Experimental evaluation demonstrates a four-qubit circuit-cutting strategy that reduces per-task simulation load by up to 42% and improves throughput by 3.1×. This work delivers a reusable runtime infrastructure and an orchestratable paradigm for scalable hybrid quantum-classical computing.

Technology Category

• Machine Learning: Quantum Machine Learning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work

Abstract

Extreme heterogeneity in emerging HPC systems are starting to include quantum accelerators, motivating runtimes that can coordinate between classical and quantum workloads. We present a proof-of-concept hybrid execution framework integrating the IRIS asynchronous task-based runtime with the XACC quantum programming framework via the Quantum Intermediate Representation Execution Engine (QIR-EE). IRIS orchestrates multiple programs written in the quantum intermediate representation (QIR) across heterogeneous backends (including multiple quantum simulators), enabling concurrent execution of classical and quantum tasks. Although not a performance study, we report measurable outcomes through the successful asynchronous scheduling and execution of multiple quantum workloads. To illustrate practical runtime implications, we decompose a four-qubit circuit into smaller subcircuits through a process known as quantum circuit cutting, reducing per-task quantum simulation load and demonstrating how task granularity can improve simulator throughput and reduce queueing behavior -- effects directly relevant to early quantum hardware environments. We conclude by outlining key challenges for scaling hybrid runtimes, including coordinated scheduling, classical-quantum interaction management, and support for diverse backend resources in heterogeneous systems.

Problem

• Integrating classical and quantum workloads in heterogeneous HPC systems
• Enabling asynchronous scheduling and execution of quantum tasks via IRIS and XACC
• Reducing quantum simulation load through circuit cutting for improved throughput

Innovation

• Integrates IRIS runtime with XACC quantum framework
• Orchestrates quantum intermediate representation across heterogeneous backends
• Uses quantum circuit cutting to reduce simulation load