Python in the front, party in the Backline: compiling quantum workloads across CPUs, GPUs, and FPGAs
Abstract
Moving from quantum research and development to production-grade, fault-tolerant quantum workload execution remains one of the most significant challenges facing quantum platform builders. While Python frameworks have enabled an easy entry point for quantum algorithm design, the low-latency requirements for real-time quantum error correction (QEC) demand performance that traditional interpreted environments cannot provide. FPGAs and ASICs play a central role at these layers, but their specialized programming models make development rigid and time-consuming. CPUs, GPUs, and other accelerators introduce a different challenge: as infrastructure becomes increasingly heterogeneous, programming across different devices and their associated abstractions becomes more complex. Allowing researchers to write workloads in high-level languages that map to low-latency execution across diverse distributed target platforms will enable the development of key infrastructure for utility-scale quantum systems. For this, we introduce Backline, a heterogeneous compilation and runtime framework built within PennyLane and Catalyst. Backline allows us to design and build quantum-classical workloads for high-performance and low-latency devices, with compilation directly from a Python interface through MLIR. We demonstrate the compilation and execution of several quantum workloads with low-latency data movement across a mix of CPUs, GPUs, and FPGAs, for both local and distributed remote hardware targets, all from a vendor-agnostic Python frontend. With an AMD VPK120 FPGA board as the controller, issuing each round from its hardware-handshake engine, we measured median steady-state round-trip latencies over RoCE v2 of 2.305 μs to an AMD Ryzen Threadripper PRO CPU and 4.5 μs to an AMD Instinct MI210 GPU across 106−1 rounds per path, demonstrating microsecond-scale synchronous co-processing.
Related
- Reducing quantum and classical resources for quantum-centric supercomputing workloads on near-term hardware
- PauLIB: HPC Strategies for Pauli-Arithmetic-Based Quantum Computing Workloads and Simulations
- Computational Complexity of Clifford Template Compilation: Are Quantum Computers Useful for Compiling Quantum Circuits?
- Standard estimators cannot represent fault-tolerant workloads at measured error rates: evaluated, evidence-based uncertainty for quantum resource estimation
- Compiling the 2D Fermi-Hubbard ground-state energy estimation algorithm for active volume quantum architectures
BibTeX
@misc{lee2026python,
title = {{Python in the front, party in the Backline: compiling quantum workloads across CPUs, GPUs, and FPGAs}},
author = {Joseph K. L. Lee and Mehrdad Malekmohammadi and Hong-Sheng Zheng and Shuli Shu and Cheick Doumbia and Kalman Szenes and Mehran Zamani Abnili and Thomas Ainsworth and Matthew Seymour and Thomas Germain and Leonhard Neuhaus and Josh Izaac and Lee J. O'Riordan},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.09270},
eprint = {2609.09270},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.09270},
}
RIS
TY - UNPB
TI - Python in the front, party in the Backline: compiling quantum workloads across CPUs, GPUs, and FPGAs
AU - Joseph K. L. Lee
AU - Mehrdad Malekmohammadi
AU - Hong-Sheng Zheng
AU - Shuli Shu
AU - Cheick Doumbia
AU - Kalman Szenes
AU - Mehran Zamani Abnili
AU - Thomas Ainsworth
AU - Matthew Seymour
AU - Thomas Germain
AU - Leonhard Neuhaus
AU - Josh Izaac
AU - Lee J. O'Riordan
PY - 2026
DA - 2026/09/08
PB - arXiv
DO - 10.48550/arxiv.2609.09270
UR - https://arxiv.org/abs/2609.09270
AB - Moving from quantum research and development to production-grade, fault-tolerant quantum workload execution remains one of the most significant challenges facing quantum platform builders. While Python frameworks have enabled an easy entry point for quantum algorithm design, the low-latency requirements for real-time quantum error correction (QEC) demand performance that traditional interpreted environments cannot provide. FPGAs and ASICs play a central role at these layers, but their specialized programming models make development rigid and time-consuming. CPUs, GPUs, and other accelerators introduce a different challenge: as infrastructure becomes increasingly heterogeneous, programming across different devices and their associated abstractions becomes more complex. Allowing researchers to write workloads in high-level languages that map to low-latency execution across diverse distributed target platforms will enable the development of key infrastructure for utility-scale quantum systems. For this, we introduce Backline, a heterogeneous compilation and runtime framework built within PennyLane and Catalyst. Backline allows us to design and build quantum-classical workloads for high-performance and low-latency devices, with compilation directly from a Python interface through MLIR. We demonstrate the compilation and execution of several quantum workloads with low-latency data movement across a mix of CPUs, GPUs, and FPGAs, for both local and distributed remote hardware targets, all from a vendor-agnostic Python frontend. With an AMD VPK120 FPGA board as the controller, issuing each round from its hardware-handshake engine, we measured median steady-state round-trip latencies over RoCE v2 of 2.305 μs to an AMD Ryzen Threadripper PRO CPU and 4.5 μs to an AMD Instinct MI210 GPU across 10^6−1 rounds per path, demonstrating microsecond-scale synchronous co-processing.
ER -