Per-Phase Fidelity Attribution for Quantum Compilers using HBR Decomposition

2026-05-08

AI Summary

Existing quantum compiler benchmarks provide only aggregate fidelity metrics, making it difficult to pinpoint the sources of fidelity loss across different stages of the compilation pipeline. This work proposes the HBR decomposition framework, which partitions compilation into three distinct phases: high-level structure decomposition (H), basis gate translation (B), and routing (R), thereby enabling, for the first time, stage-wise fidelity attribution. Leveraging this model, the study evaluates eight quantum algorithms across varying optimization levels on Qiskit, PennyLane, and TKET, using both noise-aware simulations and real-device experiments on IBM Heron and IonQ Forte hardware topologies. The results accurately predict the relative performance ranking of the SDKs, reveal that the dominant performance bottleneck shifts with algorithm class and optimization level, and uncover phase-specific limitations invisible to conventional holistic benchmarks.

Abstract

Quantum compilers sit between an algorithm's theoretical promise and what executes on physical hardware. Existing benchmarks report aggregate post-transpilation metrics but cannot attribute where fidelity is lost within the compilation pipeline. We present HBR decomposition, a per-phase fidelity attribution model that quantifies relative fidelity loss across High-level structural decomposition (H), Basis translation (B), and Routing (R). We evaluate three production SDKs (Qiskit, PennyLane, TKET) across eight algorithms on two backend topologies: IBM Heron (heavy-hex) and IonQ Forte (all-to-all). The dominant compiler bottleneck is strongly circuit-class dependent: Routing accounts for up to 60% of relative fidelity loss in search-class circuits, while synthesis dominates Hamiltonian simulation workloads. Early synthesis choices amplify or compress downstream routing overhead depending on circuit connectivity. SDK rankings at diagnostic optimization level (opt=0) reverse at production levels (opt=2) for deep circuits, showing that stagewise diagnostics and production results answer different questions. HBR correctly predicts SDK rank ordering across noisy simulations (8 circuits x 3 SDKs x 2 tiers) and real IBM Fez hardware executions, revealing stage-specific bottlenecks that are not observable through aggregate compiler benchmarks.

Problem

• quantum compilers
• fidelity attribution
• compilation pipeline
• benchmarking
• quantum circuit optimization

Innovation

• HBR decomposition
• fidelity attribution
• quantum compilation
• compiler benchmarking
• stage-wise analysis