Special-purpose and general-purpose quantum computers offer two distinct paths toward practical quantum computing applications. While both accelerate solutions to classical problems, little research has explored how integrating different quantum systems can enhance quantum computing. This work presents a novel approach that uses a coherent optical quantum computer to optimize qubit layout mapping for superconducting quantum processors. It formulates qubit mapping as a Quadratic Unconstrained Binary Optimization (QUBO) problem, enabling efficient placement of quantum circuits onto IBM’s heavy-hex topology. Using the QBoson 550w optical Ising machine to solve and post-process this model, we tested 54 quantum circuits and achieved an 88.07% reduction in SWAP gate count. This result outperforms Qiskit’s LookaheadSwap and SabreSwap routing methods by at least 21.35% when those methods are executed without prior layout optimization. Our approach surpasses TrivialLayout, DenseLayout, and SabreLayout in Qiskit, delivering at least 7.06% better optimization. Additionally, we demonstrated that the quantum approach offers notable advantages in solution success rate and efficiency, as its solution time is below the millisecond level, and it can maintain an optimization rate comparable to that of classical quadratic solvers (e.g., Gurobi and CPLEX) for the QUBO model. To our knowledge, our work is the first deep integration of optical and superconducting quantum systems, introducing a new paradigm where quantum computing optimizes itself—unlocking new possibilities for self-improving quantum architectures.

Electronic Design Automation (EDA) plays a crucial role in classical chip design and significantly influences the development of quantum chip design. However, traditional EDA tools cannot be directly applied to quantum chip design due to vast differences compared to the classical realm. Several EDA products tailored for quantum chip design currently exist, yet they only cover partial stages of the quantum chip design process instead of offering a fully comprehensive solution. Additionally, they often encounter issues such as limited automation, steep learning curves, challenges in integrating with actual fabrication processes, and difficulties in expanding functionality. To address these issues, we developed a full-stack EDA tool specifically for quantum chip design, called EDA-Q. The design workflow incorporates functionalities present in existing quantum EDA tools while supplementing critical design stages such as device mapping and fabrication process mapping, which users expect. EDA-Q utilizes a unique architecture to achieve exceptional scalability and flexibility. The integrated design mode guarantees algorithm compatibility with different chip components, while employing a specialized interactive processing mode to offer users a straightforward and adaptable command interface. Application examples demonstrate that EDA-Q significantly reduces chip design cycles, enhances automation levels, and decreases the time required for manual intervention. Multiple rounds of testing on the designed chip have validated the effectiveness of EDA-Q in practical applications.