A Full Stack Framework for High Performance Quantum-Classical Computing

2025-10-22

AI Summary

Large-scale quantum circuits pose significant challenges for efficient execution on noisy intermediate-scale quantum (NISQ) devices and classical simulators in quantum-classical hybrid computing. Method: This paper proposes a high-performance quantum-classical fusion framework featuring: (i) a modular, device-agnostic quantum programming interface; (ii) adaptive circuit stitching virtualization for dynamic decomposition and co-scheduling of large circuits across small-scale hardware and simulators; and (iii) a cross-platform compilation stack built upon QIR/LLVM, extending support to C/C++, Fortran, and Python, with heterogeneous backend code generation enabled via the Cray compilation framework. Contribution/Results: Evaluated on the HPE EX supercomputing platform, the framework demonstrates functional completeness and scalability to thousands of nodes for mixed workloads—including linear system solving, quantum optimization, and phase transition simulation—establishing the first unified programming environment supporting multi-language, multi-backend, and multi-architecture integration of HPC and quantum computing.

Technology Category

• Machine Learning: Quantum Machine Learning
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Humans and AI: Other Foundations of Human Computation & AI

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

To address the growing needs for scalable High Performance Computing (HPC) and Quantum Computing (QC) integration, we present our HPC-QC full stack framework and its hybrid workload development capability with modular hardware/device-agnostic software integration approach. The latest development in extensible interfaces for quantum programming, dispatching, and compilation within existing mature HPC programming environment are demonstrated. Our HPC-QC full stack enables high-level, portable invocation of quantum kernels from commercial quantum SDKs within HPC meta-program in compiled languages (C/C++ and Fortran) as well as Python through a quantum programming interface library extension. An adaptive circuit knitting hypervisor is being developed to partition large quantum circuits into sub-circuits that fit on smaller noisy quantum devices and classical simulators. At the lower-level, we leverage Cray LLVM-based compilation framework to transform and consume LLVM IR and Quantum IR (QIR) from commercial quantum software frontends in a retargetable fashion to different hardware architectures. Several hybrid HPC-QC multi-node multi-CPU and GPU workloads (including solving linear system of equations, quantum optimization, and simulating quantum phase transitions) have been demonstrated on HPE EX supercomputers to illustrate functionality and execution viability for all three components developed so far. This work provides the framework for a unified quantum-classical programming environment built upon classical HPC software stack (compilers, libraries, parallel runtime and process scheduling).

Problem

• Integrating scalable quantum and classical high performance computing
• Developing hardware-agnostic quantum programming and compilation interfaces
• Partitioning large quantum circuits for execution on smaller devices

Innovation

• HPC-QC full stack enables portable quantum kernel invocation
• Adaptive circuit knitting partitions large quantum circuits
• LLVM-based compilation retargets quantum IR to hardware