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.
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.
Quantum programming languages and their compilers form the critical software layer between abstract quantum algorithm specifications and physical hardware execution -- yet compiler quality varies enormously across the quantum software ecosystem, with suboptimal compilation directly increasing circuit depth, gate count, and consequently error rates on NISQ hardware. This paper proposes the Quantum Compiler Design and Evaluation (QCDE) framework, a systematic methodology for quantum compiler optimisation across four compilation stages: gate synthesis (decomposing abstract gates to hardware-native gate sets), routing (mapping virtual qubits to physical qubits under connectivity constraints), scheduling (ordering gates to minimise decoherence), and cross-layer optimisation (combining all stages with error mitigation awareness). QCDE evaluates six quantum compilers (Qiskit transpiler L1-L3, TKET, BQSKit, Cirq, Quilc, and a novel QCDE-Opt compiler) on 48 benchmark circuits from the QOAS, HQCAD, and QNNDE algorithm ecosystems. QCDE-Opt achieves 18.4% lower CX gate count vs. Qiskit L3 (best existing compiler) through a novel error-aware peephole optimisation pass. BQSKit provides the best synthesis quality at 28.4% CX reduction but 12.4x compilation overhead. TKET achieves the best balance of quality and speed (8.4% CX reduction, 1.8x overhead). QCDE provides a compiler selection guide and the QCDE-Opt compiler as an open-source contribution.
We present an analysis of quantum circuit fidelity across the full compilation stack, from high-level gate optimization through pulse-level control. We connect a C++ circuit optimizer to a per-gate Lindblad master-equation fidelity model whose decoherence channels are cross-validated against qiskit-dynamics and whose absolute predictions are benchmarked against execution on real hardware. Across a campaign of 4,452 experiment runs over 371 benchmark circuits, gate cancellation provides the dominant improvement (d=1.66, 72% of circuits improved), while circuit size and pulse duration are the strongest negative predictors of process fidelity (input gates r=−0.78; pulse duration r=−0.73, R2=0.53). A formal ablation study shows that pass ordering has no significant effect on two-qubit gate reduction (Kruskal--Wallis p=0.302). Comparing against Qiskit transpilation levels, we show that two-qubit gate count, not total gate count, is the hardware-relevant metric: our optimizer attains superior two-qubit reduction on structured circuits (87.8% on QFT, 100% on QAOA) whereas Qiskit's larger total-gate reduction is dominated by single-qubit (u3) consolidation. Finally, executing eight circuits on the IQM Resonance Garnet processor (8/8 jobs completed, job identifiers released) reveals that the model is a consistent upper bound: it preserves the relative difficulty ordering of circuits but overestimates absolute fidelity by a mean of 0.49, quantifying the error budget (crosstalk, leakage, readout) outside a T1/T2/depolarizing model. We release the framework, data, and scripts as open source.
Quantum compilation reconciles a program's idealized interaction topology with hardware locality constraints, yet evaluations at scale lack calibrated references for realization overhead. We present QROB, a scalable reverse-construction methodology that generates compilation instances backward from directly realizable configurations, retaining the inverse paths as feasible, compiler-independent references. QROB provides a common evaluation substrate for NISQ SWAP routing and fault-tolerant lattice-surgery scheduling, while extending its reference-preserving principle to capacity-constrained quantum memory-access scheduling.
Across systems ranging from 9 to 156 qubits, evaluations highlight QROB's utility as both a diagnostic benchmark and a data source. First, for compiler characterization, QROB reveals substantial realization gaps in existing tools, with NISQ compilers incurring up to 24.1x the reference SWAP cost and fault-tolerant compilers requiring up to 7.0x the reference makespan. Second, as a supervision source for data-driven compilation, a router trained on QROB references outperforms Qiskit SABRE on 84.8% of real-world application circuits. Finally, on real hardware, QROB reference realizations achieve a median mirror-circuit survival rate 1.65x that of full Qiskit O3 compilations across three 156-qubit IBM Heron-r2 processors, demonstrating that closing algorithmic compilation gaps translates directly into physical fidelity gains.
AbstractCurrent quantum compilers inject lossy SWAP gates to satisfy heavy-hex connectivity, incurring15-30% fidelity penalties per routed operation. We present a hardware-compliant routing paradigmbased on the Z6 topological bus that achieves zero SWAP overhead by constraining entanglementto nearest-neighbor CX chains. Combined with parity compression into a single terminal anchorqubit and thermodynamic dark-memory mapping, the framework was executed across 57,344shots on IBM Heron r2 with 95.2% average GHZ fidelity and 0 compiler-inserted SWAPs.
This technical note investigates whether Layer II structural diagnostics can predict transpilation costs in IBM Heavy-Hex quantum architectures before compilation is performed. The study evaluates Heavy-Hex coupling maps generated through Qiskit for distances d = 3, 5, 7, and 9. Structural observables are computed directly from the architecture graph and compared against normalized transpilation metrics obtained from Quantum Fourier Transform (QFT), GHZ-state preparation, and Hardware-Efficient Ansatz (HEA) circuits. A leave-one-out validation procedure is used to assess predictive capability on unseen architecture instances. Results indicate that mediation, normalized structural cost, and feasibility observables contain measurable predictive information regarding transpilation overhead. The strongest performance is observed for HEA and GHZ workloads, where several prediction targets achieve low error levels. The benchmark provides preliminary evidence that circuit-independent structural observables may support predictive assessment of compilation costs during early-stage quantum architecture evaluation.
Quantum processors are being integrated into HPC ecosystems as co-processors, where compilation of quantum circuits into hardware-executable form determines both output fidelity and runtime. Current compilers use a fixed pass sequence and ignore the fact that optimal pass selection varies with circuit, hardware, and noise conditions. We present TuniQ, a reinforcement learning-based system that selects compilation passes at each pipeline stage, adapting to circuit, backend, and current noise profile. TuniQ introduces several novel design components like a dual-encoder for stage-aware representation, shaped rewards for cross-stage credit assignment, and dynamic action masking for valid compilation. Evaluated across diverse quantum workloads on multiple IBM Quantum Cloud processors, TuniQ improves fidelity and reduces compilation time over the state-of-the-art IBM Qiskit transpiler, generalizes across backends without retraining, and scales strongly to utility-scale circuits with growing advantage.