The design space of current quantum computers is expansive, with no obvious winning solution, leaving practitioners with a crucial question: “What is the optimal system configuration to run an algorithm?” This paper explores hardware design trade-offs across NISQ systems to better guide algorithm and hardware development. Algorithmic workloads and fidelity models drive the evaluation to appropriately capture architectural features such as gate expressivity, fidelity, and crosstalk. As a result of our analysis, we extend the criteria for gate design and selection from only maximizing average fidelity to a more comprehensive approach that additionally considers expressivity with respect to algorithm structures. A custom synthesis-driven compilation workflow that produces minimal circuit representations for a given system configuration drives our methodology and allows us to analyze any gate set effectively. In this work, we focus on native entangling gates (CNOT, ECR, CZ, ZZ, XX, Sycamore, √iSWAP), proposed gates (B Gate,4√CNOT, -8√CNOT), as well as parameterized gates (FSim, XY). By providing a method to evaluate the suitability of algorithms for hardware platforms, this work emphasizes the importance of hardware-software codesign for quantum computing.
The design space of current quantum computers is expansive, with no obvious winning solution, leaving practitioners with a crucial question: “What is the optimal system configuration to run an algorithm?” This paper explores hardware design trade-offs across NISQ systems to better guide algorithm and hardware development. Algorithmic workloads and fidelity models drive the evaluation to appropriately capture architectural features such as gate expressivity, fidelity, and crosstalk. As a result of our analysis, we extend the criteria for gate design and selection from only maximizing average fidelity to a more comprehensive approach that additionally considers expressivity with respect to algorithm structures. A custom synthesis-driven compilation workflow that produces minimal circuit representations for a given system configuration drives our methodology and allows us to analyze any gate set effectively. In this work, we focus on native entangling gates (CNOT, ECR, CZ, ZZ, XX, Sycamore, √iSWAP), proposed gates (B Gate,4√CNOT, -8√CNOT), as well as parameterized gates (FSim, XY). By providing a method to evaluate the suitability of algorithms for hardware platforms, this work emphasizes the importance of hardware-software codesign for quantum computing.
This review aims to provide researchers, experimentalists, and quantum engineers with an updated and technically grounded perspective on the implementation of quantum gates in superconducting NISQ devices. The goal is to establish a comprehensive bridge between theoretical quantum circuit synthesis and its practical, experimental implementation, focusing on superconducting quantum technologies. We systematically examine the diverse landscape of superconducting qubits and processor architectures, ranging from conventional transmons to alternative and unconventional designs, evaluating their unique design trade-offs, connectivity constraints, device physics, and materials impact. Moving from theory to actual implementation, we detail how single- and two-qubit operations are synthesized via microwave-driven and flux-activated pulses. We here present our pulse engineering, wave shaping, calibration workflows, demonstrating how to bridge the gap between abstract gate models and real-world hardware limitations in the direction of a hardware-software co-design framework. To validate these operations, we choose benchmarking methodologies with the purpose to get the more efficient metric for the specific evaluation aspect, including truth tables and quantum process tomography. Therefore, we both discuss the theoretical concepts behind the gate synthesis and show experimental implementation of most fundamental gates by proposing a practical workflow.
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.
Abstract Design space exploration (DSE) plays an important role in optimising quantum circuit execution by systematically evaluating different configurations of compilation strategies and hardware settings. In this paper, we conduct a comprehensive investigation into the impact of various layout methods, qubit routing techniques, and optimisation levels, as well as device-specific properties such as different variants and strengths of noise and imperfections, the topological structure of qubits, connectivity densities, and back-end sizes. By spanning through these dimensions, we aim to understand the interplay between compilation choices and hardware characteristics. A key question driving our exploration is whether the optimal selection of device parameters, mapping techniques, comprising of initial layout strategies and routing heuristics can mitigate device induced errors beyond standard error mitigation approaches. Our results show that carefully selecting software strategies (e.g., mapping and routing algorithms) and tailoring hardware characteristics (such as minimising noise and leveraging topology and connectivity density) significantly improve the fidelity of circuit execution outcomes, and thus the expected correctness or success probability of the computational result. We provide estimates based on key metrics such as circuit depth, gate count and expected fidelity. Our results highlight the importance of hardware–software co-design, particularly as quantum systems scale to larger dimensions, and along the way towards fully error corrected quantum systems: Our study is based on computationally noisy simulations, but considers various implementations of quantum error correction (QEC) using the same approach as for other algorithms. The observed sensitivity of circuit fidelity to noise and connectivity suggests that co-design principles will be equally critical when integrating QEC in future systems. Our exploration provides practical guidelines for co-optimising physical mapping, qubit routing, and hardware configurations in realistic quantum computing scenarios.
Quantum computing, leveraging the principles of superposition and entanglement, holds the potential to revolutionize computational problem-solving across a wide array of fields, from cryptography to complex system simulations. However, unlocking the full power of quantum computing requires the development of advanced quantum compilation techniques that can efficiently map high-level algorithms onto physical quantum hardware. This paper delves into state-of-the-art optimization strategies within quantum compilers, with a particular focus on methods such as Relaxed Peephole Optimization (RPO), gate reordering techniques for the Quantum Approximate Optimization Algorithm (QAOA), and reinforcement learning (RL)-driven frameworks. It also explores innovative approaches like distributed quantum compilation, parity quantum optimization, and the Paulihedral framework, all designed to minimize gate counts, reduce circuit depth, and optimize overall resource utilization. A key contribution of this work is the introduction of error mitigation techniques that enhance the fidelity and scalability of quantum circuits, particularly on noisy intermediate-scale quantum (NISQ) devices. Through comprehensive empirical evaluations across a variety of quantum algorithms, the paper demonstrates significant improvements in critical performance metrics such as circuit depth, gate count, and execution efficiency, providing a promising direction for optimizing quantum circuit design tailored to diverse quantum hardware platforms.
Design space exploration (DSE) plays an important role in optimising quantum circuit execution by systematically evaluating different configurations of compilation strategies and hardware settings. In this work, we study the impact of layout methods, qubit routing techniques, compiler optimization levels, and hardware-specific properties, including noise characteristics, topological structures, connectivity densities, and device sizes. By traversing these dimensions, we aim to understand how compilation choices interact with hardware features. A central question in our study is whether carefully selected device parameters and mapping strategies, including initial layouts and routing heuristics, can mitigate hardware-induced errors beyond standard error mitigation methods. Our results show that choosing the right software strategies (e.g., layout and routing) and tailoring hardware properties (e.g., reducing noise or leveraging connectivity) significantly enhances the fidelity of quantum circuit executions. We provide performance estimates using metrics such as circuit depth, gate count, and expected fidelity. These findings highlight the value of hardware-software co-design, especially as quantum systems scale and move toward error-corrected computing. Our simulations, though noisy, include quantum error correction (QEC) scenarios, revealing similar sensitivities to layout and connectivity. This suggests that co-design principles will be vital for integrating QEC in future devices. Overall, we offer practical guidance for co-optimizing mapping, routing, and hardware configuration in real-world quantum computing.
Parameterized quantum circuits (PQCs) serve as a flexible framework for implementing various quantum algorithms, particularly in quantum machine learning and optimization tasks. However, their training is challenging due to both theoretical constraints and the inherent limitations in current quantum hardware. A judicious choice of PQC architecture is crucial for accurately representing the solution space, and for ensuring convergence in training. Several works introduced measures of expressibility and entangling capability for PQCs, and compared these metrics across different circuit structures, providing insights into how they impact performance and efficiency. However, these investigations often assumed idealized all-to-all qubit connectivity, which is not applicable to most hardware architectures. In practice, additional transpilation steps are performed prior to execution on hardware. The potential impact of better alignment of the PQC structure with the underlying native topology has in fact motivated introduction of hardware-efficient ansatze. In our work, we extend the methodology of measuring PQC efficiency to include more realistic quantum hardware models. We examine several popular hardware topologies, including hexagonal, square, circular, linear, and all-to-all. We evaluate the expressibility and entangling capability of circuits with different connectivity types and various arrangements of rotation gates. We assess the training performance of these PQC types in the context of a variational compiling procedure and identify the preferred gate patterns. This approach provides a more realistic evaluation of PQC performance, thus offering practical insights into their applications on existing quantum devices.