Classic and Quantum Task-Based Intelligent Runtime for QIRs Running on Multiple QPUs

TL;DR

This paper presents a unified runtime system that manages hybrid quantum-classical workloads across multiple quantum back-ends, enabling concurrent execution and efficient simulation of quantum circuits.

Contribution

It introduces an integrated task-based runtime combining IRIS and QIR-EE for hybrid quantum-classical computing on heterogeneous platforms.

Findings

Enables concurrent dispatch of QIR programs to multiple quantum back-ends.

Demonstrates quantum circuit cutting to parallelize simulation tasks.

Shows that task granularity improves simulation efficiency while maintaining accuracy.

Abstract

High-performance computing systems are rapidly evolving into heterogeneous platforms that fuse quantum accelerators with traditional classical processing units (CPUs) and graphical processing units (GPUs). This convergence calls for runtimes capable of managing both classical and quantum workloads in a unified manner. We introduce an intelligent, task-based runtime that marries the Intelligent RuntIme System (IRIS) asynchronous scheduler with a quantum programming stack through the Quantum Intermediate Representation Execution Engine (QIR-EE). Our design allows programs written in the quantum intermediate representation (QIR) to be dispatched concurrently to a variety of back-ends, including multiple quantum simulators and nascent quantum processors, enabling genuine hybrid execution on a single node. To illustrate its practicality, we partition a 4-qubit and 20-qubit circuit into three sub-circuits using quantum circuit cutting via the QCut library. Each sub-circuit is simulated independently by the QIR-EE driver within IRIS, after which a classical post-processing step merges the simulation results to recover the outcome of the original full-circuit computation. This case study demonstrates how finer task granularity can enable the parallel execution and lower the simulation burden per quantum task while preserving overall accuracy, highlighting the feasibility of our hybrid approach.