As quantum software stacks scale up to support future fault-tolerant quantum hardware and algorithms, quantum compilation is becoming an increasingly important component of the stack. How do we ensure that our software stacks support dynamic algorithms, including patterns such as mid-circuit measurement feedforward and repeat-until-success, with hundreds of logical qubits and billions of quantum operations? To do so, we must re-think how we represent quantum programs beyond straight-line circuits, to representations that include classical structure and dynamism, and make this the default representation to consider when performing quantum compilation. In this work, we present important patterns and algorithms from fault-tolerant quantum applications which admit a structured representation that we argue is crucial to preserve, and set a challenge to the community to compile such representations without unrolling them into straight-line quantum circuits. We also explore the status quo of structured program support in quantum software, and ask ourselves the rhetorical question: how much more efficient can we make quantum compilation tooling when we take into account the additional information from classical structure?
Damian Rovara, Daniel Haag, Mark Koch et al.· 0 citations
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 $2.305~\mu$s to an AMD Ryzen Threadripper PRO CPU and $4.5~\mu$s to an AMD Instinct MI210 GPU across $10^6-1$ rounds per path, demonstrating microsecond-scale synchronous co-processing.
Joseph K. L. Lee, M. Malekmohammadi, Hong-Sheng Zheng et al.· 0 citations
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