Design, simulation, analysis and verification methodologies are crucial for developing electronic circuits and systems at large. Whereas long-standing EDA software is used in the semiconductor technology, there is no counterpart for quantum computing systems yet. Although the quantum computing community started utilizing and adapting some of the already existing EDA tools, for instance, to design quantum processors and control electronics for driving the qubits, or even to solve some quantum computing design tasks, they do not fully use the expertise gained over the last decades in the field of design automation. Current intermediate-scale quantum computers have been designed in an ‘adhoc’ manner with heterogeneous methods and tools. As we are entering the large-scale era, it is timely and key to further adopt EDA methodologies and software for quantum computing. In this paper, we provide an overview on how full-stack quantum computing systems are being implemented nowadays and discuss which the main challenges are for transitioning from this current scenario to a comprehensive framework encompassing full automated system-wide architecting, design, simulation, verification, and test.
Design, simulation, analysis and verification methodologies are crucial for developing electronic circuits and systems at large. Whereas long-standing EDA software is used in the semiconductor technology, there is no counterpart for quantum computing systems yet. Although the quantum computing community started utilizing and adapting some of the already existing EDA tools, for instance, to design quantum processors and control electronics for driving the qubits, or even to solve some quantum computing design tasks, they do not fully use the expertise gained over the last decades in the field of design automation. Current intermediate-scale quantum computers have been designed in an ‘adhoc’ manner with heterogeneous methods and tools. As we are entering the large-scale era, it is timely and key to further adopt EDA methodologies and software for quantum computing. In this paper, we provide an overview on how full-stack quantum computing systems are being implemented nowadays and discuss which the main challenges are for transitioning from this current scenario to a comprehensive framework encompassing full automated system-wide architecting, design, simulation, verification, and test.
In the span of four decades, quantum computation has evolved from an intellectual curiosity to a potentially realizable technology. Today, small-scale demonstrations have become possible for quantum algorithmic primitives on hundreds of physical qubits. Nevertheless, there are significant outstanding challenges in quantum hardware, fabrication, software architecture, and algorithms on the path towards a full-stack scalable quantum computing technology. Here, we provide a comprehensive review of these scaling challenges. We show how to facilitate scaling by adopting existing semiconductor technology to build much higher-quality qubits, employing systems engineering approaches, and performing distributed heterogeneous quantum-classical computing. We provide a detailed resource and sensitivity analysis for quantum applications on surface-code error-corrected quantum computers given current, target, and desired hardware specifications based on superconducting qubits, accounting for a realistic distribution of errors. We provide comprehensive resource estimates for several utility-scale applications including quantum chemistry calculations, catalyst design, NMR spectroscopy, and Fermi-Hubbard simulation. We show that orders of magnitude enhancement in performance could be obtained by a combination of hardware improvements and tight quantum-HPC integration. Furthermore, we introduce high-performance architectures for quantum-probabilistic computing with custom-designed accelerators to tackle today's industry-scale classical optimization, machine learning, and quantum simulation tasks in a cost-effective manner.
The advances in quantum hardware with functional quantum processors integrating tens of noisy qubits, together with the availability of near-term quantum algorithms have allowed the development of the so-called full-stacks that bridge quantum applications with quantum devices. In this talk, we will provide an overview of the different layers of the quantum computing full-stack, with emphasis on the software ones that include the compilation of quantum algorithms. We will then discuss the need for optimal physical-aware and algorithm-driven compilation techniques in the midst of the NISQ (Noisy Intermediate-scale quantum) era. We will also focus on key principles for architecting quantum computers such as codesign, optimization and benchmarking and illustrate how the use of structured design space exploration methodologies could help towards the development of a cross-layer codesign framework for full-stack quantum computing systems. Finally, we will talk about the scalability of quantum computing systems, which is one of the main challenges the quantum computing community is currently facing.
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.
Quantum computers promise to efficiently solve important problems classical computers never will. However, in order to capitalize on these prospects, a fully automated quantum software stack needs to be developed. This involves a multitude of complex tasks from the classical simulation of quantum circuits, over their compilation to specific devices, to the verification of the circuits to be executed as well as the obtained results. All of these tasks are highly non-trivial and necessitate efficient data structures to tackle the inherent complexity. Starting from rather straight-forward arrays over decision diagrams (inspired by the design automation community) to tensor networks and the ZX-calculus, various complementary approaches have been proposed. This work provides a look "under the hood" of today's tools and showcases how these means are utilized in them, e.g., for simulation, compilation, and verification of quantum circuits.
Quantum computing is emerging at a meteoric pace from a pure academic field to a fully industrial framework. Rapid advances are happening both in the physical realisations of quantum chips, and in their potential software applications. In contrast, we are not seeing that rapid growth in the design and verification methodologies for scaled-up quantum machines. In this work we describe the field of verification of quantum computers. We discuss the underlying concepts of this field, its theoretical and practical challenges, and state-of-the-art approaches to addressing those challenges. The goal of this paper is to help facilitate early efforts to adapt and create verification methodologies for quantum computers and systems. Without such early efforts, a debilitating gap may form between the state-of-the-art of low level physical technologies for quantum computers, and our ability to build medium, large, and very large scale integrated quantum circuits (M/L/VLSIQ).
Abstract Currently, there is an ongoing “race” to build the first practically useful quantum computer that provides substantial speed-ups for certain problems compared to conventional computers. In addition to the development of such devices, this also requires the development of automated tools and methods that provide assistance in the simulation and design of corresponding applications. Otherwise, a situation might be reached where we have powerful quantum computers but hardly any proper means to actually use them. This work provides an overview of corresponding solutions for the task of quantum circuit simulation, the task of quantum circuit design, as well as corresponding mapping tasks. The covered solutions utilise expertise on efficient data structures and algorithms gained in the design of conventional circuits and systems over the last decades. While the respective descriptions are kept brief and mainly convey the general ideas, references to further readings are provided for a more detailed treatment.