Design Automation Challenges in Quantum Computing

Prabhat Mishra

Alwin Zulehner, Robert Wille

Lukas Burgholzer, Robert Wille

Rasit Onur Topaloglu

Ilia Polian, Austin G. Fowler

Building a quantum computer that is sufficiently large for solving classically intractable computational problem instances is a grand challenge of today's science. The main fundamental obstacle to construction of scalable quantum computers is the vulnerability of their extremely fragile components to noise and decoherence due to environment interaction. This paper focuses on design of technology-independent quantum circuit architectures that are scalable and reliable as well as well-suited for physical implementation. The considered architectures will be protected against errors by state-of-the-art topological quantum error-correcting (TQEC) codes which combine a number of properties enabling the best scalability among all discussed alternatives. The theory of TQEC is very mature, the suitable hardware has made significant progress in recent years, and therefore the software challenges constitute the research gaps. We explain the abstraction levels which lay the foundation for systematic, top-down design automation methods for TQEC computers, discuss the appropriate optimization objectives and formulate a number of largely open design automation problems in this fields.

Feng Wu, Jingzhe Guo, Tian Xia +12 more

Quantum computing is transitioning from laboratory research to industrial deployment, yet significant challenges persist: system scalability and performance, fabrication yields, and the advancement of algorithms and applications. We emphasize that in building quantum computers -- spanning quantum chips, system integration, instruction sets, algorithms, and middleware such as quantum error correction schemes -- design is everywhere. In this paper, we advocate for a holistic design perspective in quantum computing, a perspective we argue is pivotal to unlocking innovative co-design opportunities and addressing the aforementioned key challenges. To equip readers with sufficient background for exploring co-optimization opportunities, we detail how interconnected computational methods and tools collaborate to enable end-to-end quantum computer design. This coverage encompasses critical stages -- such as chip layout design automation, high-fidelity system-level simulation, Hamiltonian derivation for quantum system modeling, control pulse simulation, decoherence analysis, and physical verification and testing -- followed by quantum instruction set design. We then proceed to quantum system and software development, including quantum circuit synthesis, quantum error correction and fault tolerance, and logic verification and testing. Through these discussions, we illustrate with concrete examples -- including co-optimizing quantum instruction sets with algorithmic considerations, customizing error correction circuits to hardware-specific constraints, and streamlining quantum chip design through tailored code design, among others. We hope that the detailed end-to-end design workflow as well as these examples will foster dialogue between the hardware and software communities, ultimately facilitating the translation of meaningful research findings into future quantum hardware implementations.