Quantum computation is currently moving from an academic idea to a practical reality. The recent past has seen tremendous progress in the physical implementation of corresponding quantum computers - also involving big players such as IBM, Google, Intel, Rigetti, Microsoft, and Alibaba. These devices promise substantial speedups over conventional computers for applications like quantum chemistry, optimization, machine learning, cryptography, quantum simulation, and systems of linear equations. The Computer-Aided Design and Verification (jointly referred as CAD) community needs to be ready for this revolutionizing new technology. While research on automatic design methods for quantum computers is currently underway, there is still far too little coordination between the CAD community and the quantum computation community. Consequently, many CAD approaches proposed in the past have either addressed the wrong problems or failed to reach the end users. In this summary paper, we provide a glimpse into both sides. To this end, we review and discuss selected accomplishments from the CAD domain as well as open challenges within the quantum domain. These examples showcase the recent state-of-the-art but also outline the remaining work left to be done in both communities.
Quantum computation is currently moving from an academic idea to a practical reality. The recent past has seen tremendous progress in the physical implementation of corresponding quantum computers - also involving big players such as IBM, Google, Intel, Rigetti, Microsoft, and Alibaba. These devices promise substantial speedups over conventional computers for applications like quantum chemistry, optimization, machine learning, cryptography, quantum simulation, and systems of linear equations. The Computer-Aided Design and Verification (jointly referred as CAD) community needs to be ready for this revolutionizing new technology. While research on automatic design methods for quantum computers is currently underway, there is still far too little coordination between the CAD community and the quantum computation community. Consequently, many CAD approaches proposed in the past have either addressed the wrong problems or failed to reach the end users. In this summary paper, we provide a glimpse into both sides. To this end, we review and discuss selected accomplishments from the CAD domain as well as open challenges within the quantum domain. These examples showcase the recent state-of-the-art but also outline the remaining work left to be done in both communities.
Quantum computing as emerging technology raises high expectations to accelerate complicated calculations, superseding capabilities of presently available supercomputers and to provide a solution to the ever-increasing challenge of processing big amount of data. Besides theoretical promises, quantum computers have successfully been realized in experimental settings during the past few years, and can now also be accessed commercially. Given this availability of quantum computers, their application potential can be scrutinized. Promised to lead to disruptive changes in addressing and solving problems from the domains of simulation, optimization and machine learning, quantum computers have been researched in context of many application scenarios in industrial and academic setting in the recent years. However, it turns out that currently available quantum computers are still to small in size and limited in quality to fulfill the high hopes set in them at the moment. This review reflects on the current state of the technology coming from the perspective of the application, and indicates the developments required on hardware, software and algorithmic side to eventually make quantum computing beneficial for applications.
During the past decade, quantum computing has grown from a field known mostly for generating scientific papers to one that is poised to reshape computing as we know it [1]. Major industrial research efforts in quantum computing are currently underway at many companies, including IBM [2], Microsoft [3], Google [4], [5], Alibaba [6], and Intel [7], to name a few. The European Union [8], Australia [9], China [10], Japan [11], Canada [12], Russia [13], and the United States [14] are each funding large national research initiatives focused on the quantum information sciences. And, recently, tens of start-up companies have emerged with goals ranging from the development of software for use on quantum computers [15] to the implementation of full-fledged quantum computers (e.g., Rigetti [16], ION-Q [17], Psi-Quantum [18], and so on). However, despite this rapid growth, because quantum computing as a field brings together many different disciplines, there is currently a shortage of engineers who understand both the engineering aspects (e.g., microwave design) and the quantum aspects required to build a quantum computer [19]. The aim of this article is to introduce microwave engineers to quantum computing and demonstrate how the microwave community's expertise could contribute to that field.
Quantum computing is rapidly evolving as a paradigm that promises to solve complex computational problems far beyond the capabilities of classical systems. This article explores the current challenges facing quantum computation, particularly in algorithm development, error correction, and hardware scalability. The paper also outlines the prospects of next-generation quantum algorithms, especially those applicable to cryptography, optimization, and machine learning. Recent advances in quantum error mitigation and hybrid quantum-classical models are reviewed, emphasizing the transition from theoretical constructs to practical deployment. This study aims to bridge the gap between quantum algorithm research and its implementation, offering insights into the interdisciplinary efforts needed to make quantum advantage a reality.
Practical challenges in simulating quantum systems on classical computers have been widely recognized in the quantum physics and quantum chemistry communities over the past century. Although many approximation methods have been introduced, the complexity of quantum mechanics remains hard to appease. The advent of quantum computation brings new pathways to navigate this challenging and complex landscape. By manipulating quantum states of matter and taking advantage of their unique features such as superposition and entanglement, quantum computers promise to efficiently deliver accurate results for many important problems in quantum chemistry, such as the electronic structure of molecules. In the past two decades, significant advances have been made in developing algorithms and physical hardware for quantum computing, heralding a revolution in simulation of quantum systems. This Review provides an overview of the algorithms and results that are relevant for quantum chemistry. The intended audience is both quantum chemists who seek to learn more about quantum computing and quantum computing researchers who would like to explore applications in quantum chemistry.
Quantum computing is currently moving from an academic idea to a practical reality. Quantum computing in the cloud is already available and allows users from all over the world to develop and execute real quantum algorithms. However, companies which are heavily investing in this new technology such as Google, IBM, Rigetti, Intel, IonQ, and Xanadu follow diverse technological approaches. This led to a situation where we have substantially different quantum computing devices available thus far. They mostly differ in the number and kind of qubits and the connectivity between them. Because of that, various methods for realizing the intended quantum functionality on a given quantum computing device are available. This paper provides an introduction and overview into this domain and describes corresponding methods, also referred to as compilers, mappers, synthesizers, transpilers, or routers.
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.