This work introduces ΩQ-SILQ, a structure-sensitive quantum compilation framework that extends high-level quantum programming concepts with Hamiltonian-aware stability analysis. Modern quantum compilers and languages such as Silq improve quantum software development through abstraction, resource optimization, and automatic uncomputation. However, traditional optimization primarily focuses on computational metrics such as gate count, circuit depth, and qubit usage. ΩQ-SILQ proposes an additional structure-sensitive layer based on a commutator-norm metric: Omega_Q(U,H) = ||[H,U]|| / ||U|| to evaluate the compatibility between quantum operations and the underlying Hamiltonian dynamics of the target system. The framework introduces: • Hamiltonian-aware structural scoring of quantum circuits • Structure-sensitive compiler decision functions • Integration concept with high-level quantum language pipelines • Fidelity-based validation using logically equivalent circuit decompositions • Extended GHZ benchmark experiments • A proposed Qiskit transpiler comparison protocol Initial simulations demonstrate that lower ΩQ circuit selections can preserve higher fidelity under structurally coupled noise models. A multi-sample GHZ benchmark further evaluates the relationship between structural-score differences and fidelity behavior. This work does not propose replacing existing quantum compilers or quantum error correction methods. Instead, ΩQ-SILQ is presented as a complementary pre-error structural optimization layer designed to explore how future quantum compilers may incorporate physical system dynamics alongside computational efficiency. Version v1.1 includes extended hub-selection benchmarking, and the accompanying v1.2 protocol defines future validation against Qiskit transpiler optimization levels and standard quantum algorithm families.
This work introduces ΩQ-SILQ, a structure-sensitive quantum compilation framework that extends high-level quantum programming concepts with Hamiltonian-aware stability analysis. Modern quantum compilers and languages such as Silq improve quantum software development through abstraction, resource optimization, and automatic uncomputation. However, traditional optimization primarily focuses on computational metrics such as gate count, circuit depth, and qubit usage. ΩQ-SILQ proposes an additional structure-sensitive layer based on a commutator-norm metric: Omega_Q(U,H) = ||[H,U]|| / ||U|| to evaluate the compatibility between quantum operations and the underlying Hamiltonian dynamics of the target system. The framework introduces: • Hamiltonian-aware structural scoring of quantum circuits • Structure-sensitive compiler decision functions • Integration concept with high-level quantum language pipelines • Fidelity-based validation using logically equivalent circuit decompositions • Extended GHZ benchmark experiments • A proposed Qiskit transpiler comparison protocol Initial simulations demonstrate that lower ΩQ circuit selections can preserve higher fidelity under structurally coupled noise models. A multi-sample GHZ benchmark further evaluates the relationship between structural-score differences and fidelity behavior. This work does not propose replacing existing quantum compilers or quantum error correction methods. Instead, ΩQ-SILQ is presented as a complementary pre-error structural optimization layer designed to explore how future quantum compilers may incorporate physical system dynamics alongside computational efficiency. Version v1.1 includes extended hub-selection benchmarking, and the accompanying v1.2 protocol defines future validation against Qiskit transpiler optimization levels and standard quantum algorithm families.
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.
The rapid advancement of Noisy Intermediate-Scale Quantum (NISQ) devices has driven the need for efficient design automation tools that can translate complex engineering models into executable quantum circuits. This study explores the development of automated quantum circuit design frameworks focused on gate optimization and noise-aware compilation, enabling practical quantum computation for engineering applications such as optimization, simulation, and control systems. Traditional circuit design methods often fail to account for hardware constraints, decoherence, and gate infidelity, which significantly affect computational accuracy on NISQ devices. To address these challenges, this research introduces a hybrid automation approach combining quantum transpilation, variational circuit pruning, and error-aware synthesis algorithms to enhance circuit reliability and scalability. The framework integrates quantum-classical co-design principles to optimize both logical and physical circuit layers, leveraging hardware-specific calibration data to adapt compilation strategies dynamically. Through simulations and benchmark analysis, the proposed system demonstrates significant improvements in quantum resource utilization, error mitigation, and gate depth reduction compared to conventional compilation methods. This work underscores the growing importance of design automation in quantum engineering, where algorithmic efficiency must be harmonized with physical constraints to realize functional and scalable quantum workflows. By establishing a methodology for gate-level optimization and noise-adaptive design, the study contributes to bridging the gap between theoretical quantum algorithms and real-world engineering implementations within the NISQ computing era.
Structure-Sensitive Quantum Runtime (SSQR): An Adaptive Runtime Framework for Structure-Aware Error Mitigation in NISQ Quantum Computing proposes a structure-sensitive runtime architecture for adaptive execution in Noisy Intermediate-Scale Quantum (NISQ) computing. Rather than applying a fixed error mitigation strategy to every quantum circuit, the proposed framework introduces a pre-execution decision layer that analyzes circuit structure and estimates execution reliability before selecting an appropriate mitigation strategy. The paper defines the core concepts of Structure Profile, Structure Fingerprint, Structure Complexity Index (SCI), Runtime Risk, and Confidence Score, and integrates them into a layered runtime architecture consisting of Structure Analysis, Hardware Abstraction, Runtime Intelligence, Adaptive Optimization, and Knowledge layers. This work is intentionally presented as a position and methodology paper. It introduces the mathematical framework, architectural design, theoretical complexity analysis, related-work comparison, and a pre-registered experimental protocol, but does not claim experimental validation or performance improvements. Those questions are reserved for subsequent papers in the SSQR research series. The proposed framework is intended to provide a falsifiable foundation for future research on adaptive quantum runtime systems, structure-aware error mitigation, and experience-driven execution strategies across NISQ quantum computing platforms.
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.
Efficient compilation of quantum algorithms is vital in the era of Noisy Intermediate-Scale Quantum (NISQ) devices. While multiple open-source quantum compilation and circuit optimization frameworks are available, e.g. IBM Qiskit, CQC Tket, Google Cirq, Rigetti Quilc, PyZX, their relative performance is not always clear to a quantum programmer. The growth of complexity and diversity of quantum circuit compilation algorithms creates a demand for a dedicated tool for cross-benchmarking and profiling of inner workflow of the quantum compilation stack. We present an open-source software package, Arline Benchmarks, that is designed to perform automated benchmarking of quantum compilers with the focus on NISQ applications. The name "Arline" was given in honour of Arline Greenbaum Feynman, the first wife of Richard Feynman, the pioneer of quantum computing. We compared several quantum compilation frameworks based on a set of important metrics such as post-optimization gate counts, circuit depth, hardware-dependent circuit cost function, compiler run time etc. with a detailed analysis of metrics for each compilation stage. We performed a variety of compiler tests for random circuits and structured quantum algorithms (VQE, Trotter decomposition, Grover search, Option Pricing via Amplitude Estimation) for several popular quantum hardware architectures. Leveraging cross-platform functionality of Arline, we propose a concept of composite compilation pipeline that combines compiler-specific circuit optimization subroutines in a single compilation stack and finds an optimized sequence of compilation passes. By providing detailed insights into the compilation flow of quantum compilers, Arline Benchmarks offers a valuable toolkit for quantum computing researchers and software developers to gain additional insights into compilers' characteristics.
Scalable quantum computation is expected to rely on fault-tolerant quantum computation (FTQC), in which quantum error correction (QEC) suppresses physical errors sufficiently to support reliable logical operations. This requires quantum compilation to move beyond general-purpose circuit optimization toward encoding-aware and protocol-structured compilation across the full stack of fault-tolerant quantum computers. Beyond circuit synthesis and hardware mapping, an FTQC compiler must lower algorithm-level operations into the logical gate set supported by the chosen code, coordinate encoded data and ancilla resources, realize logical operations together with repeated syndrome extraction under hardware constraints, and provide the resulting measurement stream to real-time decoding.
This survey presents a full-stack view of compiler design for QEC-protected quantum computation. We organize existing work into three interacting layers: logical-level QEC compilation, physical-level QEC realization, and decoder runtime integration. At the logical level, we review surface-code lattice-surgery compilers, beyond-surface-code code-surgery frameworks including emerging qLDPC approaches, and compilation support for non-Clifford operations such as magic-state distillation and code switching. At the physical level, we survey hardware-aware QEC realization on superconducting, trapped-ion, and neutral-atom platforms. We further examine decoder models, real-time decoding systems, and frame-management mechanisms that close the feedback loop during fault-tolerant execution. Finally, we identify open challenges in cross-layer optimization, qLDPC compilation, compiler-decoder co-design, runtime adaptivity, and the development of integrated and benchmarkable FTQC compilation stacks. An actively maintained paper list is available at: github.com/chenghongz/QEC-compiler-design.