Python in the front, party in the Backline: compiling quantum workloads across CPUs, GPUs, and FPGAs

• URL: http://arxiv.org/abs/2609.09270v1
• Date: Tue, 08 Sep 2026 18:00:00 GMT
• Title: Python in the front, party in the Backline: compiling quantum workloads across CPUs, GPUs, and FPGAs
• Abstract summary: We introduce Backline, a heterogeneous compilation and runtime framework built within PennyLane and Catalyst.<n>Backline allows us to design and build quantum-classical workloads for high-performance and low-latency devices.<n>We demonstrate the compilation and execution of several quantum workloads with low-latency data movement.
• Score: 1.4035981933195663
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 $\textit{Backline}$, 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 $2.305~μ$s to an AMD Ryzen Threadripper PRO CPU and $4.5~μ$s to an AMD Instinct MI210 GPU across $10^6-1$ rounds per path, demonstrating microsecond-scale synchronous co-processing.

Related papers

• A Platform-aware Compilation Framework for Fault-tolerant Quantum Computation [0.0]
  We present a platform-aware compilation framework that re-compiles a quantum circuit into a hardware-compatible instruction set.<n>We benchmark the framework for Hamiltonian simulation of the 2D Fermi Hubbard model and for eigenenergy estimation of a small molecule.<n>For the latter, we show that end-to-end quantum simulations can be achieved with $sim104$ physical qubits and algorithms ranging from $102$ ms (photonics, superconducting) to $105$ ms (neutral atoms)
  arXiv  Detail & Related papers  (2026-09-08T15:41:54Z)
• Eliminating Vendor Lock-In in Quantum Machine Learning via Framework-Agnostic Neural Networks [0.0]
  Current quantum machine learning (QML) software frameworks suffer from severe fragmentation.<n>We present a framework-agnostic quantum neural network (HALQ) architecture that abstracts away vendor-specific interfaces.
  arXiv  Detail & Related papers  (2026-04-06T04:43:09Z)
• GPU-Accelerated Quantum Simulation: Empirical Backend Selection, Gate Fusion, and Adaptive Precision [0.0]
  This paper presents a GPU-accelerated quantum circuit simulation framework.<n>An empirical backend selection algorithm benchmarks CuPy, PyTorchCUDA, and NumPy CPU backends at runtime.<n>A gate fusion engine reduces circuit depth through automated identification of gate sequences.<n>A fallback mechanism monitors GPU memory consumption and gracefully degrades to memory execution when resources are exhausted.
  arXiv  Detail & Related papers  (2026-04-04T17:46:37Z)
• A Full Stack Framework for High Performance Quantum-Classical Computing [1.9263941068840744]
  We present our HPC-QC full stack framework and its hybrid workload development capability with modular hardware/device-agnostic software integration approach.<n>The latest development in interfaces for quantum programming, dispatching, and compilation within existing mature HPC programming environment are demonstrated.
  arXiv  Detail & Related papers  (2025-10-23T02:07:29Z)
• Pilot-Quantum: A Quantum-HPC Middleware for Resource, Workload and Task Management [1.381966718755792]
  Pilot-Quantum is designed to provide unified application-level management of resources and workloads across hybrid quantum-classical environments.<n>It implements the Pilot Abstraction conceptual model, originally developed for HPC, to manage resources, workloads, and tasks.
  arXiv  Detail & Related papers  (2024-12-24T15:55:46Z)
• Harnessing Deep Learning and HPC Kernels via High-Level Loop and Tensor Abstractions on CPU Architectures [67.47328776279204]
  This work introduces a framework to develop efficient, portable Deep Learning and High Performance Computing kernels. We decompose the kernel development in two steps: 1) Expressing the computational core using Processing Primitives (TPPs) and 2) Expressing the logical loops around TPPs in a high-level, declarative fashion. We demonstrate the efficacy of our approach using standalone kernels and end-to-end workloads that outperform state-of-the-art implementations on diverse CPU platforms.
  arXiv  Detail & Related papers  (2023-04-25T05:04:44Z)
• Quantum simulation with just-in-time compilation [0.0]
  We present a first attempt to perform circuit-based quantum simulation using the just-in-time (JIT) compilation technique. Qibojit is a new module for the Qibo quantum computing framework, which uses a just-in-time compilation approach through Python.
  arXiv  Detail & Related papers  (2022-03-16T18:00:00Z)
• Enabling Retargetable Optimizing Compilers for Quantum Accelerators via a Multi-Level Intermediate Representation [78.8942067357231]
  We present a multi-level quantum-classical intermediate representation (IR) that enables an optimizing, retargetable, ahead-of-time compiler. We support the entire gate-based OpenQASM 3 language and provide custom extensions for common quantum programming patterns and improved syntax. Our work results in compile times that are 1000x faster than standard Pythonic approaches, and 5-10x faster than comparative standalone quantum language compilers.
  arXiv  Detail & Related papers  (2021-09-01T17:29:47Z)
• Fast quantum circuit simulation using hardware accelerated general purpose libraries [69.43216268165402]
  CuPy is a general purpose library (linear algebra) developed specifically for GPU-based quantum circuits. For supremacy circuits the speedup is around 2x, and for quantum multipliers almost 22x compared to state-of-the-art C++-based simulators.
  arXiv  Detail & Related papers  (2021-06-26T10:41:43Z)
• Extending Python for Quantum-Classical Computing via Quantum Just-in-Time Compilation [78.8942067357231]
  Python is a popular programming language known for its flexibility, usability, readability, and focus on developer productivity. We present a language extension to Python that enables heterogeneous quantum-classical computing via a robust C++ infrastructure for quantum just-in-time compilation.
  arXiv  Detail & Related papers  (2021-05-10T21:11:21Z)
• A MLIR Dialect for Quantum Assembly Languages [78.8942067357231]
  We demonstrate the utility of the Multi-Level Intermediate Representation (MLIR) for quantum computing. We extend MLIR with a new quantum dialect that enables the expression and compilation of common quantum assembly languages. We leverage a qcor-enabled implementation of the QIR quantum runtime API to enable a retargetable (quantum hardware agnostic) compiler workflow.
  arXiv  Detail & Related papers  (2021-01-27T13:00:39Z)
• Extending C++ for Heterogeneous Quantum-Classical Computing [56.782064931823015]
  qcor is a language extension to C++ and compiler implementation that enables heterogeneous quantum-classical programming, compilation, and execution in a single-source context. Our work provides a first-of-its-kind C++ compiler enabling high-level quantum kernel (function) expression in a quantum-language manner.
  arXiv  Detail & Related papers  (2020-10-08T12:49:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.