- By:
- Miniskar, Narasinga Rao ; Wong, Elaine Y; Leyton Ortega, Vicente A; Vetter, Jeffrey S; Humble, Travis S
- Page Number:
- 1-6
- Volume:
- 2026
- Book Title:
- Proceedings of the 19th IEEE Dallas Circuits and Systems Conference (DCAS 2026)
- Publication Date:
- May 20, 2026
- Conference Name:
- 19th IEEE Dallas Circuits and Systems Conference (DCAS 2026)
- Conference Location:
- Dallas, Texas, United States of America
- Conference Sponsor:
- IEEE
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.
