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Quantum Machine Learning

A Full Stack Framework for High Performance Quantum-Classical Computing

arXiv
Authors: Xin Zhan, K. Grace Johnson, Aniello Esposito, Barbara Chapman, Marco Fiorentino, Kirk M. Bresniker, Raymond G. Beausoleil, Masoud Mohseni

Year

2025

Paper ID

50866

Status

Preprint

Abstract Read

~2 min

Abstract Words

214

Citations

N/A

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).

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  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
  • It adds a 2025 reference point for readers tracking recent quantum research.
  • 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...

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